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Musilova J, Vafek Z, Puniya BL, Zimmer R, Helikar T, Sedlar K. Augusta: From RNA-Seq to gene regulatory networks and Boolean models. Comput Struct Biotechnol J 2024; 23:783-790. [PMID: 38312198 PMCID: PMC10837063 DOI: 10.1016/j.csbj.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.
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Affiliation(s)
- Jana Musilova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 61600, Czech Republic
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Zdenek Vafek
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
- Institute of Forensic Engineering, Brno University of Technology, Brno 61200, Czech Republic
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Ralf Zimmer
- Department of Informatics, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Tomas Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Karel Sedlar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 61600, Czech Republic
- Department of Informatics, Ludwig-Maximilians-Universität München, Munich 80539, Germany
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Samad SS, Schwartz JM, Francavilla C. Functional selectivity of Receptor Tyrosine Kinases regulates distinct cellular outputs. Front Cell Dev Biol 2024; 11:1348056. [PMID: 38259512 PMCID: PMC10800419 DOI: 10.3389/fcell.2023.1348056] [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: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Functional selectivity refers to the activation of differential signalling and cellular outputs downstream of the same membrane-bound receptor when activated by two or more different ligands. Functional selectivity has been described and extensively studied for G-protein Coupled Receptors (GPCRs), leading to specific therapeutic options for dysregulated GPCRs functions. However, studies regarding the functional selectivity of Receptor Tyrosine Kinases (RTKs) remain sparse. Here, we will summarize recent data about RTK functional selectivity focusing on how the nature and the amount of RTK ligands and the crosstalk of RTKs with other membrane proteins regulate the specificity of RTK signalling. In addition, we will discuss how structural changes in RTKs upon ligand binding affects selective signalling pathways. Much remains to be known about the integration of different signals affecting RTK signalling specificity to orchestrate long-term cellular outcomes. Recent advancements in omics, specifically quantitative phosphoproteomics, and in systems biology methods to study, model and integrate different types of large-scale omics data have increased our ability to compare several signals affecting RTK functional selectivity in a global, system-wide fashion. We will discuss how such methods facilitate the exploration of important signalling hubs and enable data-driven predictions aiming at improving the efficacy of therapeutics for diseases like cancer, where redundant RTK signalling pathways often compromise treatment efficacy.
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Affiliation(s)
- Sakim S. Samad
- Division of Molecular and Cellular Functions, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Division of Evolution, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Jean-Marc Schwartz
- Division of Evolution, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Chiara Francavilla
- Division of Molecular and Cellular Functions, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Section of Protein Science and Biotherapeutics, Department of Bioengineering and Biomedicine, Danish Technical University, Lyngby, Denmark
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Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
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Gjerga E, Naarmann-de Vries IS, Dieterich C. Characterizing alternative splicing effects on protein interaction networks with LINDA. Bioinformatics 2023; 39:i458-i464. [PMID: 37387163 DOI: 10.1093/bioinformatics/btad224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Alternative RNA splicing plays a crucial role in defining protein function. However, despite its relevance, there is a lack of tools that characterize effects of splicing on protein interaction networks in a mechanistic manner (i.e. presence or absence of protein-protein interactions due to RNA splicing). To fill this gap, we present Linear Integer programming for Network reconstruction using transcriptomics and Differential splicing data Analysis (LINDA) as a method that integrates resources of protein-protein and domain-domain interactions, transcription factor targets, and differential splicing/transcript analysis to infer splicing-dependent effects on cellular pathways and regulatory networks. RESULTS We have applied LINDA to a panel of 54 shRNA depletion experiments in HepG2 and K562 cells from the ENCORE initiative. Through computational benchmarking, we could show that the integration of splicing effects with LINDA can identify pathway mechanisms contributing to known bioprocesses better than other state of the art methods, which do not account for splicing. Additionally, we have experimentally validated some of the predicted splicing effects that the depletion of HNRNPK in K562 cells has on signalling.
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Affiliation(s)
- Enio Gjerga
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg 69120, Germany
- Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Heidelberg 69120, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, Heidelberg 69120, Germany
| | - Isabel S Naarmann-de Vries
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg 69120, Germany
- Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Heidelberg 69120, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, Heidelberg 69120, Germany
| | - Christoph Dieterich
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg 69120, Germany
- Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Heidelberg 69120, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg/Mannheim, Heidelberg 69120, Germany
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Li X, Dowling EK, Yan G, Dereli Z, Bozorgui B, Imanirad P, Elnaggar JH, Luna A, Menter DG, Pilié PG, Yap TA, Kopetz S, Sander C, Korkut A. Precision combination therapies based on recurrent oncogenic co-alterations. Cancer Discov 2022; 12:1542-1559. [PMID: 35412613 PMCID: PMC9524464 DOI: 10.1158/2159-8290.cd-21-0832] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/28/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.
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Affiliation(s)
- Xubin Li
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Gonghong Yan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zeynep Dereli
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Behnaz Bozorgui
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Parisa Imanirad
- Department of Systems Biology, and The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jacob H. Elnaggar
- Department of Microbiology, Immunology, and Parasitology, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - David G. Menter
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Patrick G. Pilié
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Timothy A. Yap
- Department of Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Corresponding Author: Anil Korkut, Bioinformatics & Comp Biology, Phone: 718-300-0666, , 1515 Holcombe Blvd., Houston, Texas 77030-4009
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Palshikar MG, Hilchey SP, Zand MS, Thakar J. WikiNetworks: translating manually created biological pathways for topological analysis. Bioinformatics 2021; 38:869-871. [PMID: 34636843 PMCID: PMC8756176 DOI: 10.1093/bioinformatics/btab699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/28/2021] [Accepted: 10/11/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY WikiPathways is a database of 2979 biological pathways across 31 species created using the drawing software PathVisio. Many of these pathways are not directly usable for network-based topological analyses due to differences in curation styles and drawings. We developed the WikiNetworks package to standardize and construct directed networks by combining geometric information and manual annotations from WikiPathways. WikiNetworks performs significantly better than existing tools. This enables the use of high-quality WikiPathways resource for network-based topological analysis of high-throughput data. AVAILABILITY AND IMPLEMENTATION WikiNetworks is written in Python3 and is available on github.com/Thakar-Lab/wikinetworks and on PyPI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mukta G Palshikar
- Biophysics, Structural, and Computational Biology Program, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Shannon P Hilchey
- Division of Nephrology, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Martin S Zand
- Division of Nephrology, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA,Clinical and Translational Science Institute, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
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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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Selvaggio G, Chaouiya C, Janody F. In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer. Int J Mol Sci 2021; 22:ijms22094897. [PMID: 34063110 PMCID: PMC8125147 DOI: 10.3390/ijms22094897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/13/2022] Open
Abstract
The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.
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Affiliation(s)
- Gianluca Selvaggio
- Fondazione the Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy;
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- CNRS, Centrale Marseille, I2M, Aix Marseille University, 13397 Marseille, France
- Correspondence: (C.C.); (F.J.)
| | - Florence Janody
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
- IPATIMUP—Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
- Correspondence: (C.C.); (F.J.)
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Gjerga E, Dugourd A, Tobalina L, Sousa A, Saez-Rodriguez J. PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data. J Proteome Res 2021; 20:2138-2144. [PMID: 33682416 DOI: 10.1021/acs.jproteome.0c00958] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.
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Affiliation(s)
- Enio Gjerga
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Aurelien Dugourd
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Abel Sousa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, United Kingdom.,Institute for Research and Innovation in Health (i3s), Rua Alfredo Allen 208, 4200-135 Porto, Portugal
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
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Abstract
MOTIVATION Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. RESULTS We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. AVAILABILITY AND IMPLEMENTATION Find the full codebase at https://github.com/gitter-lab/ssps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Merrell
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Anthony Gitter
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53726, USA
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Niarakis A, Helikar T. A practical guide to mechanistic systems modeling in biology using a logic-based approach. Brief Bioinform 2020; 22:5925256. [PMID: 33064138 DOI: 10.1093/bib/bbaa236] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/10/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process-the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.
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Affiliation(s)
- Anna Niarakis
- GenHotel, Univ Evry, University of Paris-Saclay, Genopole, 91025 Evry, France and Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
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