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Esteban-Medina M, de la Oliva Roque VM, Herráiz-Gil S, Peña-Chilet M, Dopazo J, Loucera C. drexml: A command line tool and Python package for drug repurposing. Comput Struct Biotechnol J 2024; 23:1129-1143. [PMID: 38510973 PMCID: PMC10950807 DOI: 10.1016/j.csbj.2024.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.
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Affiliation(s)
- Marina Esteban-Medina
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Víctor Manuel de la Oliva Roque
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Sara Herráiz-Gil
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U714, Madrid, Spain
- Departamento de Bioingeniería, Universidad Carlos III de Madrid (UC3M), Madrid, Spain
- Regenerative Medicine and Tissue Engineering Group, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital (IIS-FJD), Madrid, Spain
- Epithelial Biomedicine Division, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
| | - María Peña-Chilet
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Platform of Big Data, AI and Biostatistics, Health Research Institute La Fe (IISLAFE), Valencia, Spain
| | - Joaquín Dopazo
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Seville, Spain
| | - Carlos Loucera
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
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Bai D, Ellington CN, Mo S, Song L, Xing EP. AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects. Bioinformatics 2024; 40:i453-i461. [PMID: 38940174 PMCID: PMC11211811 DOI: 10.1093/bioinformatics/btae244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge. RESULTS We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene-gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/BaiDing1234/AttentionPert.
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Affiliation(s)
- Ding Bai
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Caleb N Ellington
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
| | - Shentong Mo
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Le Song
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Eric P Xing
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
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3
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Huang W, Liu H. Predicting single-cell cellular responses to perturbations using cycle consistency learning. Bioinformatics 2024; 40:i462-i470. [PMID: 38940153 DOI: 10.1093/bioinformatics/btae248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Phenotype-based drug screening emerges as a powerful approach for identifying compounds that actively interact with cells. Transcriptional and proteomic profiling of cell lines and individual cells provide insights into the cellular state alterations that occur at the molecular level in response to external perturbations, such as drugs or genetic manipulations. In this paper, we propose cycleCDR, a novel deep learning framework to predict cellular response to external perturbations. We leverage the autoencoder to map the unperturbed cellular states to a latent space, in which we postulate the effects of drug perturbations on cellular states follow a linear additive model. Next, we introduce the cycle consistency constraints to ensure that unperturbed cellular state subjected to drug perturbation in the latent space would produces the perturbed cellular state through the decoder. Conversely, removal of perturbations from the perturbed cellular states can restore the unperturbed cellular state. The cycle consistency constraints and linear modeling in the latent space enable to learn transferable representations of external perturbations, so that our model can generalize well to unseen drugs during training stage. We validate our model on four different types of datasets, including bulk transcriptional responses, bulk proteomic responses, and single-cell transcriptional responses to drug/gene perturbations. The experimental results demonstrate that our model consistently outperforms existing state-of-the-art methods, indicating our method is highly versatile and applicable to a wide range of scenarios. AVAILABILITY AND IMPLEMENTATION The source code is available at: https://github.com/hliulab/cycleCDR.
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Affiliation(s)
- Wei Huang
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, China
| | - Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, China
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4
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Papp O, Jordán V, Hetey S, Balázs R, Kaszás V, Bartha Á, Ordasi NN, Kamp S, Farkas B, Mettetal J, Dry JR, Young D, Sidders B, Bulusu KC, Veres DV. Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors. NPJ Syst Biol Appl 2024; 10:68. [PMID: 38906870 PMCID: PMC11192759 DOI: 10.1038/s41540-024-00394-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 06/14/2024] [Indexed: 06/23/2024] Open
Abstract
Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.
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Affiliation(s)
- Orsolya Papp
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | | | | | - Róbert Balázs
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | - Valér Kaszás
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | - Árpád Bartha
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | - Nóra N Ordasi
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | | | - Bálint Farkas
- Turbine Simulated Cell Technologies, Budapest, Hungary
| | - Jay Mettetal
- Oncology Bioscience, Research and Early Development, Oncology R&D, AstraZeneca, Waltham, MA, USA
| | - Jonathan R Dry
- Early Data Science, Oncology Data Science, Oncology R&D, AstraZeneca, Waltham, MA, USA
| | - Duncan Young
- Search and Evaluation, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ben Sidders
- Early Data Science, Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Krishna C Bulusu
- Early Data Science, Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK
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Cavalcante BRR, Freitas RD, Siquara da Rocha LO, Santos RSB, Souza BSDF, Ramos PIP, Rocha GV, Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front Pharmacol 2024; 15:1400029. [PMID: 38919258 PMCID: PMC11196849 DOI: 10.3389/fphar.2024.1400029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
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Affiliation(s)
- Bruno Raphael Ribeiro Cavalcante
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Raíza Dias Freitas
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Social and Pediatric Dentistry of the School of Dentistry, Federal University of Bahia, Salvador, Brazil
| | - Leonardo de Oliveira Siquara da Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Bruno Solano de Freitas Souza
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil
| | - Gisele Vieira Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Clarissa Araújo Gurgel Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
- Department of Propaedeutics, School of Dentistry of the Federal University of Bahia, Salvador, Brazil
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Herajy M, Liu F, Heiner M. Design patterns for the construction of computational biological models. Brief Bioinform 2024; 25:bbae318. [PMID: 38961813 PMCID: PMC11222664 DOI: 10.1093/bib/bbae318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/16/2024] [Accepted: 06/17/2024] [Indexed: 07/05/2024] Open
Abstract
Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.
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Affiliation(s)
- Mostafa Herajy
- Department of Mathematics and Computer Science, Faculty of Science, Port Said University, 42521 Port Said, Egypt
| | - Fei Liu
- School of Software Engineering, South China University of Technology, 510006 Guangzhou, China
| | - Monika Heiner
- Computer Science Institute, Brandenburg University of Technology, 03013 Cottbus, Germany
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Kim Y, Han Y, Hopper C, Lee J, Joo JI, Gong JR, Lee CK, Jang SH, Kang J, Kim T, Cho KH. A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations. CELL REPORTS METHODS 2024; 4:100773. [PMID: 38744288 PMCID: PMC11133856 DOI: 10.1016/j.crmeth.2024.100773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024]
Abstract
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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Affiliation(s)
- Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Younghyun Han
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Corbin Hopper
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jae Il Joo
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong-Ryeol Gong
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chun-Kyung Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong-Hoon Jang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Junsoo Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Taeyoung Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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8
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Lotfollahi M, Yuhan Hao, Theis FJ, Satija R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 2024; 187:2343-2358. [PMID: 38729109 PMCID: PMC11184658 DOI: 10.1016/j.cell.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 05/12/2024]
Abstract
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK; Department of Mathematics, Technical University of Munich, Garching, Germany.
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA.
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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Arakane K, Imoto H, Ormersbach F, Okada M. Extending BioMASS to construct mathematical models from external knowledge. BIOINFORMATICS ADVANCES 2024; 4:vbae042. [PMID: 38606187 PMCID: PMC11007111 DOI: 10.1093/bioadv/vbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/13/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Motivation Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models. Results We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models. Availability and implementation The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.
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Affiliation(s)
- Kiwamu Arakane
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Hiroaki Imoto
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | | | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka 565-0871, Japan
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11
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Shen J, Wang S, Dong Y, Sun H, Wang X, Tang Z. A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data. BMC Bioinformatics 2024; 25:119. [PMID: 38509499 PMCID: PMC10953151 DOI: 10.1186/s12859-024-05741-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. RESULTS We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. CONCLUSIONS The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.
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Affiliation(s)
- Junjie Shen
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Shuo Wang
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79085, Freiburg, Germany
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, No. 199 Renai Road, Suzhou, 215123, Jiangsu, People's Republic of China.
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12
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Oliveira RHDM, Annex BH, Popel AS. Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway. Front Physiol 2024; 15:1351753. [PMID: 38455844 PMCID: PMC10917925 DOI: 10.3389/fphys.2024.1351753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024] Open
Abstract
Introduction: Several signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. Methods: We calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. Results: Our results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. Discussion: This project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.
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Affiliation(s)
| | - Brian H. Annex
- Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
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13
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Konrath F, Willenbrock M, Busse D, Scheidereit C, Wolf J. A computational model of the DNA damage-induced IKK/ NF-κB pathway reveals a critical dependence on irradiation dose and PARP-1. iScience 2023; 26:107917. [PMID: 37817938 PMCID: PMC10561052 DOI: 10.1016/j.isci.2023.107917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/01/2023] [Accepted: 09/12/2023] [Indexed: 10/12/2023] Open
Abstract
The activation of IKK/NF-κB by genotoxic stress is a crucial process in the DNA damage response. Due to the anti-apoptotic impact of NF-κB, it can affect cell-fate decisions upon DNA damage and therefore interfere with tumor therapy-induced cell death. Here, we developed a dynamical model describing IKK/NF-κB signaling that faithfully reproduces quantitative time course data and enables a detailed analysis of pathway regulation. The approach elucidates a pathway topology with two hubs, where the first integrates signals from two DNA damage sensors and the second forms a coherent feedforward loop. The analyses reveal a critical role of the sensor protein PARP-1 in the pathway regulation. Introducing a method for calculating the impact of changes in individual components on pathway activity in a time-resolved manner, we show how irradiation dose influences pathway activation. Our results give a mechanistic understanding relevant for the interpretation of experimental and clinical studies.
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Affiliation(s)
- Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michael Willenbrock
- Laboratory for Signal Transduction in Tumor Cells, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Dorothea Busse
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Claus Scheidereit
- Laboratory for Signal Transduction in Tumor Cells, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Mathematics and Computer Science, Free University Berlin, Germany
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14
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Payá-Milans M, Peña-Chilet M, Loucera C, Esteban-Medina M, Dopazo J. Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models. Int J Mol Sci 2023; 24:14732. [PMID: 37834179 PMCID: PMC10572617 DOI: 10.3390/ijms241914732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.
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Affiliation(s)
- Miriam Payá-Milans
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Carlos Loucera
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Marina Esteban-Medina
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Joaquín Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
- FPS/ELIXIR-ES, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocío, 41013 Sevilla, Spain
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15
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Myers PJ, Lee SH, Lazzara MJ. An integrated mechanistic and data-driven computational model predicts cell responses to high- and low-affinity EGFR ligands. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.25.543329. [PMID: 37425852 PMCID: PMC10327094 DOI: 10.1101/2023.06.25.543329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The biophysical properties of ligand binding heavily influence the ability of receptors to specify cell fates. Understanding the rules by which ligand binding kinetics impact cell phenotype is challenging, however, because of the coupled information transfers that occur from receptors to downstream signaling effectors and from effectors to phenotypes. Here, we address that issue by developing an integrated mechanistic and data-driven computational modeling platform to predict cell responses to different ligands for the epidermal growth factor receptor (EGFR). Experimental data for model training and validation were generated using MCF7 human breast cancer cells treated with the high- and low-affinity ligands epidermal growth factor (EGF) and epiregulin (EREG), respectively. The integrated model captures the unintuitive, concentration-dependent abilities of EGF and EREG to drive signals and phenotypes differently, even at similar levels of receptor occupancy. For example, the model correctly predicts the dominance of EREG over EGF in driving a cell differentiation phenotype through AKT signaling at intermediate and saturating ligand concentrations and the ability of EGF and EREG to drive a broadly concentration-sensitive migration phenotype through cooperative ERK and AKT signaling. Parameter sensitivity analysis identifies EGFR endocytosis, which is differentially regulated by EGF and EREG, as one of the most important determinants of the alternative phenotypes driven by different ligands. The integrated model provides a new platform to predict how phenotypes are controlled by the earliest biophysical rate processes in signal transduction and may eventually be leveraged to understand receptor signaling system performance depends on cell context. One-sentence summary Integrated kinetic and data-driven EGFR signaling model identifies the specific signaling mechanisms that dictate cell responses to EGFR activation by different ligands.
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16
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Lotfollahi M, Klimovskaia Susmelj A, De Donno C, Hetzel L, Ji Y, Ibarra IL, Srivatsan SR, Naghipourfar M, Daza RM, Martin B, Shendure J, McFaline-Figueroa JL, Boyeau P, Wolf FA, Yakubova N, Günnemann S, Trapnell C, Lopez-Paz D, Theis FJ. Predicting cellular responses to complex perturbations in high-throughput screens. Mol Syst Biol 2023:e11517. [PMID: 37154091 DOI: 10.15252/msb.202211517] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/23/2023] [Accepted: 03/31/2023] [Indexed: 05/10/2023] Open
Abstract
Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.
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Affiliation(s)
- Mohammad Lotfollahi
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Carlo De Donno
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Leon Hetzel
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Yuge Ji
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Ignacio L Ibarra
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
| | - Sanjay R Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Riza M Daza
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Beth Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | | | - Pierre Boyeau
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - F Alexander Wolf
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
| | | | - Stephan Günnemann
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - David Lopez-Paz
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Fabian J Theis
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
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17
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Erdem C, Birtwistle MR. MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets. FRONTIERS IN SYSTEMS BIOLOGY 2023; 3:1099413. [PMID: 38269333 PMCID: PMC10807051 DOI: 10.3389/fsysb.2023.1099413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States
| | - Marc R. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States
- Department of Bioengineering, Clemson University, Clemson, SC, United States
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18
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Fröhlich F, Gerosa L, Muhlich J, Sorger PK. Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance. Mol Syst Biol 2023; 19:e10988. [PMID: 36700386 PMCID: PMC9912026 DOI: 10.15252/msb.202210988] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 11/29/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023] Open
Abstract
BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAFV600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAFV600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAFV600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.
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Affiliation(s)
- Fabian Fröhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA,Present address:
Genentech, Inc.South San FranciscoCAUSA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
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19
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Lakrisenko P, Stapor P, Grein S, Paszkowski Ł, Pathirana D, Fröhlich F, Lines GT, Weindl D, Hasenauer J. Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks. PLoS Comput Biol 2023; 19:e1010783. [PMID: 36595539 PMCID: PMC9838866 DOI: 10.1371/journal.pcbi.1010783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/13/2023] [Accepted: 12/01/2022] [Indexed: 01/04/2023] Open
Abstract
Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.
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Affiliation(s)
- Polina Lakrisenko
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Paul Stapor
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Stephan Grein
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
| | | | - Dilan Pathirana
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Daniel Weindl
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
| | - Jan Hasenauer
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
- * E-mail:
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20
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Shin SY, Nguyen LK. SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks. Methods Mol Biol 2023; 2634:357-381. [PMID: 37074588 DOI: 10.1007/978-1-0716-3008-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.
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Affiliation(s)
- Sung-Young Shin
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia.
- Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia.
- Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
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21
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Quinn KN, Abbott MC, Transtrum MK, Machta BB, Sethna JP. Information geometry for multiparameter models: new perspectives on the origin of simplicity. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 86:10.1088/1361-6633/aca6f8. [PMID: 36576176 PMCID: PMC10018491 DOI: 10.1088/1361-6633/aca6f8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 11/29/2022] [Indexed: 05/20/2023]
Abstract
Complex models in physics, biology, economics, and engineering are oftensloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce themodel manifoldof predictions, whose coordinates are the model parameters. Itshyperribbonstructure explains why only a few parameter combinations matter for the behavior. We review recent rigorous results that connect the hierarchy of hyperribbon widths to approximation theory, and to the smoothness of model predictions under changes of the control variables. We discuss recent geodesic methods to find simpler models on nearby boundaries of the model manifold-emergent theories with fewer parameters that explain the behavior equally well. We discuss a Bayesian prior which optimizes the mutual information between model parameters and experimental data, naturally favoring points on the emergent boundary theories and thus simpler models. We introduce a 'projected maximum likelihood' prior that efficiently approximates this optimal prior, and contrast both to the poor behavior of the traditional Jeffreys prior. We discuss the way the renormalization group coarse-graining in statistical mechanics introduces a flow of the model manifold, and connect stiff and sloppy directions along the model manifold with relevant and irrelevant eigendirections of the renormalization group. Finally, we discuss recently developed 'intensive' embedding methods, allowing one to visualize the predictions of arbitrary probabilistic models as low-dimensional projections of an isometric embedding, and illustrate our method by generating the model manifold of the Ising model.
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Affiliation(s)
- Katherine N Quinn
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, United States of America
| | - Michael C Abbott
- Department of Physics, Yale University, New Haven, CT, United States of America
| | - Mark K Transtrum
- Department of Physics and Astronomy, Brigham Young University, Provo, UT, United States of America
| | - Benjamin B Machta
- Department of Physics and Systems Biology Institute, Yale University, New Haven, CT, United States of America
| | - James P Sethna
- Department of Physics, Cornell University, Ithaca, NY, United States of America
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22
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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23
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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24
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Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? Bull Math Biol 2022; 84:130. [PMID: 36175705 PMCID: PMC9522842 DOI: 10.1007/s11538-022-01075-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022]
Abstract
Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.
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25
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Flobak Å, Skånland SS, Hovig E, Taskén K, Russnes HG. Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models. Trends Pharmacol Sci 2022; 43:973-985. [PMID: 36163057 DOI: 10.1016/j.tips.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 10/31/2022]
Abstract
Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification.
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Affiliation(s)
- Åsmund Flobak
- The Cancer Clinic, St. Olav University Hospital, Trondheim, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sigrid S Skånland
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eivind Hovig
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, Norway
| | - Kjetil Taskén
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Hege G Russnes
- Department of Pathology, Oslo University Hospital, Oslo, Norway; Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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26
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Frank SA. Optimization of Transcription Factor Genetic Circuits. BIOLOGY 2022; 11:biology11091294. [PMID: 36138773 PMCID: PMC9495410 DOI: 10.3390/biology11091294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 04/13/2023]
Abstract
Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day-night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.
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Affiliation(s)
- Steven A Frank
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA
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27
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Fröhlich F, Sorger PK. Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models. PLoS Comput Biol 2022; 18:e1010322. [PMID: 35830470 PMCID: PMC9312381 DOI: 10.1371/journal.pcbi.1010322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/25/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using experimental data. Optimization-based calibration of ODE models on is often challenging, even for low-dimensional problems. Multiple hypotheses have been advanced to explain why biochemical model calibration is challenging, including non-identifiability of model parameters, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking. Nonetheless, reliable model calibration is essential for uncertainty analysis, model comparison, and biological interpretation.
We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving a variety of Hessian approximation schemes. We evaluated fides on a recently developed corpus of biologically realistic benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same mathematical instructions (algorithms). Analysis of possible sources of poor optimizer performance identified limitations in the widely used Gauss-Newton, BFGS and SR1 Hessian approximation schemes. We addressed these drawbacks with a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. When applied to the corpus of test models, we found that fides was on average more reliable and efficient than existing methods using a variety of criteria. We expect fides to be broadly useful for ODE constrained optimization problems in biochemical models and to be a foundation for future methods development.
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Affiliation(s)
- Fabian Fröhlich
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (FF); (PKS)
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (FF); (PKS)
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28
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Erdem C, Mutsuddy A, Bensman EM, Dodd WB, Saint-Antoine MM, Bouhaddou M, Blake RC, Gross SM, Heiser LM, Feltus FA, Birtwistle MR. A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling. Nat Commun 2022; 13:3555. [PMID: 35729113 PMCID: PMC9213456 DOI: 10.1038/s41467-022-31138-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 06/07/2022] [Indexed: 02/01/2023] Open
Abstract
Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Arnab Mutsuddy
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Ethan M Bensman
- Computer Science, School of Computing, Clemson University, Clemson, SC, USA
| | - William B Dodd
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Michael M Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
| | - Robert C Blake
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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29
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Nilsson A, Peters JM, Meimetis N, Bryson B, Lauffenburger DA. Artificial neural networks enable genome-scale simulations of intracellular signaling. Nat Commun 2022; 13:3069. [PMID: 35654811 PMCID: PMC9163072 DOI: 10.1038/s41467-022-30684-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 05/11/2022] [Indexed: 12/14/2022] Open
Abstract
Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.
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Affiliation(s)
- Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Joshua M Peters
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bryan Bryson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA.
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30
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Imoto H, Yamashiro S, Okada M. A text-based computational framework for patient -specific modeling for classification of cancers. iScience 2022; 25:103944. [PMID: 35535207 PMCID: PMC9076893 DOI: 10.1016/j.isci.2022.103944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 01/03/2022] [Accepted: 02/14/2022] [Indexed: 02/07/2023] Open
Abstract
Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response. A text file describing biochemical systems is converted into an executable model Patient-specific models incorporate individual gene expression profiles In silico signaling dynamics can be utilized as prognostic biomarkers Personalized kinetic models are capable of predicting potential drug targets
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31
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Courcelles E, Boissel JP, Massol J, Klingmann I, Kahoul R, Hommel M, Pham E, Kulesza A. Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models? FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:810315. [PMID: 35281671 PMCID: PMC8907708 DOI: 10.3389/fmedt.2022.810315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/17/2022] [Indexed: 01/11/2023] Open
Abstract
Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
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Affiliation(s)
| | | | - Jacques Massol
- Phisquare Institute, Transplantation Foundation, Paris, France
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32
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Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR. A protocol for dynamic model calibration. Brief Bioinform 2022; 23:bbab387. [PMID: 34619769 PMCID: PMC8769694 DOI: 10.1093/bib/bbab387] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 08/29/2021] [Indexed: 12/23/2022] Open
Abstract
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Affiliation(s)
- Alejandro F Villaverde
- Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53115, Germany
| | - Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Harvard Medical School, Cambridge, MA 02115, USA
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Vigo 36208, Galicia, Spain
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Stapor P, Schmiester L, Wierling C, Merkt S, Pathirana D, Lange BMH, Weindl D, Hasenauer J. Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Commun 2022; 13:34. [PMID: 35013141 PMCID: PMC8748893 DOI: 10.1038/s41467-021-27374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.
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Affiliation(s)
- Paul Stapor
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | - Leonard Schmiester
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | | | - Simon Merkt
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | - Dilan Pathirana
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | | | - Daniel Weindl
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany.
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany.
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany.
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34
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Zhang Y, Wang H, Oliveira RHM, Zhao C, Popel AS. Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mech Dis 2021; 14:e1550. [PMID: 34970866 PMCID: PMC9243197 DOI: 10.1002/wsbm.1550] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/10/2023]
Abstract
Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under:Cardiovascular Diseases > Computational Models Cancer > Computational Models
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Schmucker R, Farina G, Faeder J, Fröhlich F, Saglam AS, Sandholm T. Combination treatment optimization using a pan-cancer pathway model. PLoS Comput Biol 2021; 17:e1009689. [PMID: 34962919 PMCID: PMC8747684 DOI: 10.1371/journal.pcbi.1009689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 01/10/2022] [Accepted: 11/29/2021] [Indexed: 12/11/2022] Open
Abstract
The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available. Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.
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Affiliation(s)
- Robin Schmucker
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Gabriele Farina
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - James Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ali Sinan Saglam
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tuomas Sandholm
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Strategy Robot, Inc., Pittsburgh, Pennsylvania, United States of America
- Optimized Markets, Inc., Pittsburgh, Pennsylvania, United States of America
- Strategic Machine, Inc., Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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36
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Schmiester L, Weindl D, Hasenauer J. Efficient gradient-based parameter estimation for dynamic models using qualitative data. BIOINFORMATICS (OXFORD, ENGLAND) 2021. [PMID: 34260697 DOI: 10.1101/2021.02.06.430039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
MOTIVATION Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. RESULTS Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. AVAILABILITY AND IMPLEMENTATION The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53113, Germany
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37
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Myers PJ, Lee SH, Lazzara MJ. MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:100349. [PMID: 35935921 PMCID: PMC9348571 DOI: 10.1016/j.coisb.2021.05.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.
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Affiliation(s)
- Paul J. Myers
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Sung Hyun Lee
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Matthew J. Lazzara
- Department of Chemical Engineering, Charlottesville, VA 22904
- Department of Biomedical Engineering University of Virginia, Charlottesville, VA 22904
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38
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Niu J, Nguyen VA, Ghasemi M, Chen T, Mager DE. Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics. AAPS JOURNAL 2021; 23:110. [PMID: 34622346 DOI: 10.1208/s12248-021-00640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/19/2021] [Indexed: 11/30/2022]
Abstract
Ordinary differential equation (ODE)-based models of signal transduction pathways often contain parameters that are unidentifiable or unmeasurable by experimental data, and calibrating such models to data remains challenging. Here, two efficient parameter estimation methods, cluster Gauss-Newton (CGN) and CellNOpt (CNO), were applied to fit a signaling network model of U266 multiple myeloma cells to the activity dynamics of key proteins in response to vorinostat and/or bortezomib. A logic-based network model was constructed and transformed to 17 ODEs with 79 parameters estimated within broad ranges of biologically plausible values. The top 10% best-fit parameters by both methods had high uncertainties with CV > 50% for the majority of parameters. The root mean square and prediction errors were comparable without statistically significant differences between the two methods. Despite uncertain parameter estimation, protein dynamics after the sequential combination of bortezomib and vorinostat was predicted with reasonable accuracy and precision. Global sensitivity analyses of partial rank correlation coefficients and Sobol sensitivity demonstrated that apoptosis induction was most sensitive to parameters governing the activity of the proteasome-JNK-caspase-8 axis. Simulations revealed that the greatest magnitude of pharmacodynamic drug interactions between bortezomib and vorinostat occurred at caspase-9, AKT, and Bcl-2. Two sequential combinations were explored in silico, and the outcome matched qualitatively with an empirical evaluation of the pharmacodynamic interaction based on cell viability. Overall, the CGN and CNO algorithms performed similarly for this ODE-based network model calibration, and the calibrated model provided meaningful insights into cellular signaling mechanisms in response to pharmacological perturbations.
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Affiliation(s)
- Jin Niu
- Department of Pharmaceutical Sciences, University At Buffalo, State University of New York, 431 Pharmacy Building, Buffalo, NY, 14214, USA
| | - Van Anh Nguyen
- Department of Pharmaceutical Sciences, University At Buffalo, State University of New York, 431 Pharmacy Building, Buffalo, NY, 14214, USA
| | - Mohammad Ghasemi
- Department of Pharmaceutical Sciences, University At Buffalo, State University of New York, 431 Pharmacy Building, Buffalo, NY, 14214, USA
| | - Ting Chen
- Department of Pharmaceutical Sciences, University At Buffalo, State University of New York, 431 Pharmacy Building, Buffalo, NY, 14214, USA
| | - Donald E Mager
- Department of Pharmaceutical Sciences, University At Buffalo, State University of New York, 431 Pharmacy Building, Buffalo, NY, 14214, USA.
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Tsopra R, Fernandez X, Luchinat C, Alberghina L, Lehrach H, Vanoni M, Dreher F, Sezerman OU, Cuggia M, de Tayrac M, Miklasevics E, Itu LM, Geanta M, Ogilvie L, Godey F, Boldisor CN, Campillo-Gimenez B, Cioroboiu C, Ciusdel CF, Coman S, Hijano Cubelos O, Itu A, Lange B, Le Gallo M, Lespagnol A, Mauri G, Soykam HO, Rance B, Turano P, Tenori L, Vignoli A, Wierling C, Benhabiles N, Burgun A. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. BMC Med Inform Decis Mak 2021; 21:274. [PMID: 34600518 PMCID: PMC8487519 DOI: 10.1186/s12911-021-01634-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 09/22/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.
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Affiliation(s)
- Rosy Tsopra
- Centre de Recherche Des Cordeliers, Inserm, Université de Paris, Sorbonne Université, 75006, Paris, France. .,Inria, HeKA, Inria Paris, France. .,Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France. .,Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France.
| | | | - Claudio Luchinat
- Centro Risonanze Magnetiche - CERM/CIRMMP and Department of Chemistry, University of Florence, 50019, Sesto Fiorentino (Florence), Italy
| | - Lilia Alberghina
- Department of Biotechnology and Biosciences, University of Milano Bicocca and ISBE-Italy/SYSBIO - Candidate National Node of Italy for ISBE, Research Infrastructure for Systems Biology Europe, Milan, Italy
| | - Hans Lehrach
- Max Planck Institute for Molecular Genetics, Berlin, Germany.,Alacris Theranostics GmbH, Berlin, Germany
| | - Marco Vanoni
- Department of Biotechnology and Biosciences, University of Milano Bicocca and ISBE-Italy/SYSBIO - Candidate National Node of Italy for ISBE, Research Infrastructure for Systems Biology Europe, Milan, Italy
| | | | - O Ugur Sezerman
- School of Medicine Biostatistics and Medical Informatics Dept., Acibadem University, Istanbul, Turkey
| | - Marc Cuggia
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Marie de Tayrac
- Univ Rennes, Department of Molecular Genetics and Genomics, CHU Rennes, IGDR-UMR6290, CNRS, 35000, Rennes, France
| | | | | | - Marius Geanta
- Centre for Innovation in Medicine, Bucharest, Romania
| | - Lesley Ogilvie
- Max Planck Institute for Molecular Genetics, Berlin, Germany.,Alacris Theranostics GmbH, Berlin, Germany
| | - Florence Godey
- INSERM U1242 « Chemistry, Oncogenesis Stress Signaling », Université de Rennes, 35042, CEDEX, Rennes, France.,Centre de Lutte Contre Le Cancer Eugène Marquis, CRB Santé (BRIF Number: BB-0033-00056), 35042, CEDEX, Rennes, France
| | | | | | | | | | - Simona Coman
- Transilvania University of Brasov, Brasov, Romania
| | | | - Alina Itu
- Transilvania University of Brasov, Brasov, Romania
| | - Bodo Lange
- Alacris Theranostics GmbH, Berlin, Germany
| | - Matthieu Le Gallo
- INSERM U1242 « Chemistry, Oncogenesis Stress Signaling », Université de Rennes, 35042, CEDEX, Rennes, France.,Centre de Lutte Contre Le Cancer Eugène Marquis, CRB Santé (BRIF Number: BB-0033-00056), 35042, CEDEX, Rennes, France
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Genomics, CHU Rennes, 35000, Rennes, France
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano Bicocca and ISBE-Italy/SYSBIO - Candidate National Node of Italy for ISBE, Research Infrastructure for Systems Biology Europe, Milan, Italy
| | | | - Bastien Rance
- Centre de Recherche Des Cordeliers, Inserm, Université de Paris, Sorbonne Université, 75006, Paris, France.,Inria, HeKA, Inria Paris, France.,Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France
| | - Paola Turano
- Centro Risonanze Magnetiche - CERM/CIRMMP and Department of Chemistry, University of Florence, 50019, Sesto Fiorentino (Florence), Italy
| | - Leonardo Tenori
- Centro Risonanze Magnetiche - CERM/CIRMMP and Department of Chemistry, University of Florence, 50019, Sesto Fiorentino (Florence), Italy
| | - Alessia Vignoli
- Centro Risonanze Magnetiche - CERM/CIRMMP and Department of Chemistry, University of Florence, 50019, Sesto Fiorentino (Florence), Italy
| | | | - Nora Benhabiles
- Direction de La Recherche Fondamentale (DRF), CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Anita Burgun
- Centre de Recherche Des Cordeliers, Inserm, Université de Paris, Sorbonne Université, 75006, Paris, France.,Inria, HeKA, Inria Paris, France.,Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France.,PaRis Artificial Intelligence Research InstitutE (Prairie), Paris, France
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Kiwumulo HF, Muwonge H, Ibingira C, Kirabira JB, Ssekitoleko RT. A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:8149-8173. [PMID: 34814293 DOI: 10.3934/mbe.2021404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.
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Affiliation(s)
| | - Haruna Muwonge
- Department of Medical Physiology, Makerere University, Kampala, Uganda
| | - Charles Ibingira
- Department of Human Anatomy, Makerere University, Kampala, Uganda
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Schmiester L, Weindl D, Hasenauer J. Efficient gradient-based parameter estimation for dynamic models using qualitative data. Bioinformatics 2021; 37:4493-4500. [PMID: 34260697 PMCID: PMC8652033 DOI: 10.1093/bioinformatics/btab512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 07/02/2021] [Accepted: 07/08/2021] [Indexed: 11/22/2022] Open
Abstract
Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. Results Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. Availability and implementation The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany.,Center for Mathematics, Technische Universität München, Garching, 85748, Germany
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, 85764, Germany.,Center for Mathematics, Technische Universität München, Garching, 85748, Germany.,Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, 53113, Germany
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Gómez Tejeda Zañudo J, Mao P, Alcon C, Kowalski K, Johnson GN, Xu G, Baselga J, Scaltriti M, Letai A, Montero J, Albert R, Wagle N. Cell Line-Specific Network Models of ER + Breast Cancer Identify Potential PI3Kα Inhibitor Resistance Mechanisms and Drug Combinations. Cancer Res 2021; 81:4603-4617. [PMID: 34257082 PMCID: PMC8744502 DOI: 10.1158/0008-5472.can-21-1208] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/18/2021] [Accepted: 07/09/2021] [Indexed: 11/16/2022]
Abstract
Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. In this work, we used a network-based mathematical model to identify sensitivity regulators and drug combinations for the PI3Kα inhibitor alpelisib in estrogen receptor positive (ER+) PIK3CA-mutant breast cancer. The model-predicted efficacious combination of alpelisib and BH3 mimetics, for example, MCL1 inhibitors, was experimentally validated in ER+ breast cancer cell lines. Consistent with the model, FOXO3 downregulation reduced sensitivity to alpelisib, revealing a novel potential resistance mechanism. Cell line-specific sensitivity to combinations of alpelisib and BH3 mimetics depended on which BCL2 family members were highly expressed. On the basis of these results, newly developed cell line-specific network models were able to recapitulate the observed differential response to alpelisib and BH3 mimetics. This approach illustrates how network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance. SIGNIFICANCE: Network-based mathematical models of oncogenic signaling and experimental validation of its predictions can identify resistance mechanisms for targeted therapies, as this study demonstrates for PI3Kα-specific inhibitors in breast cancer.
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Affiliation(s)
- Jorge Gómez Tejeda Zañudo
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, Massachusetts. .,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Pingping Mao
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Clara Alcon
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Kailey Kowalski
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Gabriela N Johnson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Guotai Xu
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jose Baselga
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maurizio Scaltriti
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anthony Letai
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Joan Montero
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts. .,Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, Pennsylvania. .,Department of Biology, The Pennsylvania State University, Pennsylvania
| | - Nikhil Wagle
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, Massachusetts. .,Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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43
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Ji Y, Lotfollahi M, Wolf FA, Theis FJ. Machine learning for perturbational single-cell omics. Cell Syst 2021; 12:522-537. [PMID: 34139164 DOI: 10.1016/j.cels.2021.05.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/04/2021] [Accepted: 05/19/2021] [Indexed: 12/18/2022]
Abstract
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Affiliation(s)
- Yuge Ji
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - F Alexander Wolf
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Cellarity, Cambridge, MA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany; Cellarity, Cambridge, MA, USA.
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44
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Meyer P, Saez-Rodriguez J. Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges. Cell Syst 2021; 12:636-653. [PMID: 34139170 DOI: 10.1016/j.cels.2021.05.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/29/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023]
Abstract
Computational and mathematical models are key to obtain a system-level understanding of biological processes, but their limitations have to be clearly defined to allow their proper application and interpretation. Crowdsourced benchmarks in the form of challenges provide an unbiased assessment of methods, and for the past decade, the Dialogue for Reverse Engineering Assessment and Methods (DREAM) organized more than 15 systems biology challenges. From transcription factor binding to dynamical network models, from signaling networks to gene regulation, from whole-cell models to cell-lineage reconstruction, and from single-cell positioning in a tissue to drug combinations and cell survival, the breadth is broad. To celebrate the 5-year anniversary of Cell Systems, we review the genesis of these systems biology challenges and discuss how interlocking the forward- and reverse-modeling paradigms allows to push the rim of systems biology. This approach will persist for systems levels approaches in biology and medicine.
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Affiliation(s)
- Pablo Meyer
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University Hospital and Heidelberg University, Faculty of Medicine, Bioquant, Heidelberg 69120, Germany
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45
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Tognetti M, Gabor A, Yang M, Cappelletti V, Windhager J, Rueda OM, Charmpi K, Esmaeilishirazifard E, Bruna A, de Souza N, Caldas C, Beyer A, Picotti P, Saez-Rodriguez J, Bodenmiller B. Deciphering the signaling network of breast cancer improves drug sensitivity prediction. Cell Syst 2021; 12:401-418.e12. [PMID: 33932331 DOI: 10.1016/j.cels.2021.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 12/16/2020] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
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Affiliation(s)
- Marco Tognetti
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland; Molecular Life Science PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zurich, Switzerland
| | - Attila Gabor
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Mi Yang
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany; Faculty of Biosciences, Heidelberg University, 69117 Heidelberg, Germany
| | | | - Jonas Windhager
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Systems Biology PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8093 Zürich, Switzerland
| | - Oscar M Rueda
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Konstantina Charmpi
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Elham Esmaeilishirazifard
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Bioscience, R&D Oncology, Astra Zeneca, Cancer Research UK Cambridge Institute, Cambridge CB2 0RE, UK
| | - Alejandra Bruna
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Natalie de Souza
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Carlos Caldas
- Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cambridge Breast Unit, NIHR Cambridge Biomedical Research Centre and Cambridge Experimental Cancer Medicine Centre at Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Andreas Beyer
- Cologne Excellence Cluster Cellular Stress Response in Aging-Associated Diseases (CECAD), Medical Faculty and Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany; Center for Molecular Medicine (CMMC), University of Cologne, 50923 Cologne, Germany; Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, 50923 Cologne, Germany
| | - Paola Picotti
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, 69117 Heidelberg, Germany; Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland.
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46
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Fröhlich F, Weindl D, Schälte Y, Pathirana D, Paszkowski Ł, Lines GT, Stapor P, Hasenauer J. AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics 2021; 37:3676-3677. [PMID: 33821950 PMCID: PMC8545331 DOI: 10.1093/bioinformatics/btab227] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/18/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Summary Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Availabilityand implementation AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
| | | | | | - Paul Stapor
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany.,Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
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47
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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48
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Ebata K, Yamashiro S, Iida K, Okada M. Building patient-specific models for receptor tyrosine kinase signaling networks. FEBS J 2021; 289:90-101. [PMID: 33755310 DOI: 10.1111/febs.15831] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/26/2021] [Accepted: 03/19/2021] [Indexed: 12/16/2022]
Abstract
Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.
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Affiliation(s)
- Kyoichi Ebata
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Sawa Yamashiro
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Keita Iida
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Japan.,Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Japan.,Institute for Chemical Research, Kyoto University, Japan
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49
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Aldewachi H, Al-Zidan RN, Conner MT, Salman MM. High-Throughput Screening Platforms in the Discovery of Novel Drugs for Neurodegenerative Diseases. Bioengineering (Basel) 2021; 8:30. [PMID: 33672148 PMCID: PMC7926814 DOI: 10.3390/bioengineering8020030] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases (NDDs) are incurable and debilitating conditions that result in progressive degeneration and/or death of nerve cells in the central nervous system (CNS). Identification of viable therapeutic targets and new treatments for CNS disorders and in particular, for NDDs is a major challenge in the field of drug discovery. These difficulties can be attributed to the diversity of cells involved, extreme complexity of the neural circuits, the limited capacity for tissue regeneration, and our incomplete understanding of the underlying pathological processes. Drug discovery is a complex and multidisciplinary process. The screening attrition rate in current drug discovery protocols mean that only one viable drug may arise from millions of screened compounds resulting in the need to improve discovery technologies and protocols to address the multiple causes of attrition. This has identified the need to screen larger libraries where the use of efficient high-throughput screening (HTS) becomes key in the discovery process. HTS can investigate hundreds of thousands of compounds per day. However, if fewer compounds could be screened without compromising the probability of success, the cost and time would be largely reduced. To that end, recent advances in computer-aided design, in silico libraries, and molecular docking software combined with the upscaling of cell-based platforms have evolved to improve screening efficiency with higher predictability and clinical applicability. We review, here, the increasing role of HTS in contemporary drug discovery processes, in particular for NDDs, and evaluate the criteria underlying its successful application. We also discuss the requirement of HTS for novel NDD therapies and examine the major current challenges in validating new drug targets and developing new treatments for NDDs.
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Affiliation(s)
- Hasan Aldewachi
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield S1 1WB, UK;
- College of Pharmacy, Nineveh University, Mosul 41002, Iraq
| | - Radhwan N. Al-Zidan
- College of Pharmacy, University of Mosul, Mosul 41002, Iraq;
- School of Applied Sciences, Edinburgh Napier University, Edinburgh EH11 4BN, UK
| | - Matthew T. Conner
- School of Sciences, Research Institute in Healthcare Science, University of Wolverhampton, Wolverhampton WV1 1LY, UK;
| | - Mootaz M. Salman
- College of Pharmacy, University of Mosul, Mosul 41002, Iraq;
- Oxford Parkinson’s Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
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50
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A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization. iScience 2021; 24:102112. [PMID: 33659877 PMCID: PMC7895754 DOI: 10.1016/j.isci.2021.102112] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/01/2020] [Accepted: 01/21/2021] [Indexed: 01/09/2023] Open
Abstract
Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.
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