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Magaña-López G, Calzone L, Zinovyev A, Paulevé L. scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics. PLoS Comput Biol 2024; 20:e1011620. [PMID: 38976751 PMCID: PMC11257695 DOI: 10.1371/journal.pcbi.1011620] [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] [Received: 10/22/2023] [Revised: 07/18/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.
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Affiliation(s)
| | - Laurence Calzone
- Institut Curie, Université PSL, Paris, France
- INSERM, U900, Paris, France
- Mines ParisTech, Université PSL, Paris, France
| | | | - Loïc Paulevé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France
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2
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Toshimoto K. Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models. Drug Metab Pharmacokinet 2024; 56:101011. [PMID: 38833901 DOI: 10.1016/j.dmpk.2024.101011] [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: 11/06/2023] [Revised: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 06/06/2024]
Abstract
Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.
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Affiliation(s)
- Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Ibaraki, Japan.
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3
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Latini S, Venafra V, Massacci G, Bica V, Graziosi S, Pugliese GM, Iannuccelli M, Frioni F, Minnella G, Marra JD, Chiusolo P, Pepe G, Helmer Citterich M, Mougiakakos D, Böttcher M, Fischer T, Perfetto L, Sacco F. Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction. eLife 2024; 12:RP90532. [PMID: 38564252 PMCID: PMC10987088 DOI: 10.7554/elife.90532] [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] [Indexed: 04/04/2024] Open
Abstract
Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
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Affiliation(s)
- Sara Latini
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Veronica Venafra
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Valeria Bica
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Simone Graziosi
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | | | - Filippo Frioni
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gessica Minnella
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico A. Gemelli IRCCSRomeItaly
| | - John Donald Marra
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Patrizia Chiusolo
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Dimitros Mougiakakos
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Martin Böttcher
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Thomas Fischer
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Institute of Molecular and Clinical Immunology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Livia Perfetto
- Department of Biology, University of Rome Tor VergataRomeItaly
- Department of Biology, Fondazione Human TechnopoleMilanItaly
| | - Francesca Sacco
- Department of Biology, University of Rome Tor VergataRomeItaly
- Telethon Institute of Genetics and Medicine (TIGEM)PozzuoliItaly
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4
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Thakar J. Pillars of biology: Boolean modeling of gene-regulatory networks. J Theor Biol 2024; 578:111682. [PMID: 38008156 DOI: 10.1016/j.jtbi.2023.111682] [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: 09/18/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 11/28/2023]
Abstract
Boolean modeling is a mathematical modeling framework used for defining and studying gene-regulatory networks (GRNs). It serves as a means to develop mechanistic models, offering insights into the trajectories and dynamic properties of GRNs. In this review, I delve into seminal papers published in the Journal of Theoretical Biology that have spearheaded this field. Additionally, I explore the application of these modeling methods in the current era of data-intensive science.
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Affiliation(s)
- Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, USA; Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, USA.
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5
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Chicco D, Spolaor S, Nobile MS. Ten quick tips for fuzzy logic modeling of biomedical systems. PLoS Comput Biol 2023; 19:e1011700. [PMID: 38127800 PMCID: PMC10734980 DOI: 10.1371/journal.pcbi.1011700] [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: 12/23/2023] Open
Abstract
Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced researchers, who might overlook some important aspects or might make common mistakes. Malpractices and pitfalls, in turn, can lead to wrong or overoptimistic, inflated results, with negative consequences to the biomedical research community trying to comprehend a particular phenomenon, or even to patients suffering from the investigated disease. To avoid common mistakes, we present here a list of quick tips for fuzzy logic modeling any biomedical scenario: some guidelines which should be taken into account by any fuzzy logic practitioner, including experts. We believe our best practices can have a strong impact in the scientific community, allowing researchers who follow them to obtain better, more reliable results and outcomes in biomedical contexts.
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Affiliation(s)
- Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy
| | - Simone Spolaor
- Microsystems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Marco S. Nobile
- Department of Environmental Sciences Informatics and Statistics, Ca’ Foscari University of Venice, Venice, Italy
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Patidar K, Versypt ANF. Logic-Based Modeling of Inflammatory Macrophage Crosstalk with Glomerular Endothelial Cells in Diabetic Kidney Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535594. [PMID: 37066138 PMCID: PMC10104015 DOI: 10.1101/2023.04.04.535594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Diabetic kidney disease is a complication in 1 out of 3 patients with diabetes. Aberrant glucose metabolism in diabetes leads to an immune response causing inflammation and to structural and functional damage in the glomerular cells of the kidney. Complex cellular signaling lies at the core of metabolic and functional derangement. Unfortunately, the mechanism underlying the role of inflammation in glomerular endothelial cell dysfunction during diabetic kidney disease is not fully understood. Computational models in systems biology allow the integration of experimental evidence and cellular signaling networks to understand mechanisms involved in disease progression. We built a logic-based ordinary differential equations model to study macrophage-dependent inflammation in glomerular endothelial cells during diabetic kidney disease progression. We studied the crosstalk between macrophages and glomerular endothelial cells in the kidney using a protein signaling network stimulated with glucose and lipopolysaccharide. The network and model were built using the open-source software package Netflux. This modeling approach overcomes the complexity of studying network models and the need for extensive mechanistic details. The model simulations were fitted and validated against available biochemical data from in vitro experiments. The model identified mechanisms responsible for dysregulated signaling in macrophages and glomerular endothelial cells during diabetic kidney disease. In addition, we investigated the influence of signaling interactions and species that on glomerular endothelial cell morphology through selective knockdown and downregulation. We found that partial knockdown of VEGF receptor 1, PLC-γ, adherens junction proteins, and calcium partially recovered the endothelial cell fenestration size. Our model findings contribute to understanding signaling and molecular perturbations that affect the glomerular endothelial cells in the early stage of diabetic kidney disease.
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Loman TE, Ma Y, Ilin V, Gowda S, Korsbo N, Yewale N, Rackauckas C, Isaacson SA. Catalyst: Fast and flexible modeling of reaction networks. PLoS Comput Biol 2023; 19:e1011530. [PMID: 37851697 PMCID: PMC10584191 DOI: 10.1371/journal.pcbi.1011530] [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] [Received: 06/03/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst's broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.
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Affiliation(s)
- Torkel E. Loman
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
- Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Yingbo Ma
- JuliaHub, Cambridge, Massachusetts, United States of America
| | - Vasily Ilin
- Department of Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Shashi Gowda
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Niklas Korsbo
- Pumas-AI, Baltimore, Maryland, United States of America
| | - Nikhil Yewale
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Chris Rackauckas
- Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- JuliaHub, Cambridge, Massachusetts, United States of America
- Pumas-AI, Baltimore, Maryland, United States of America
| | - Samuel A. Isaacson
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
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Glazer BJ, Lifferth JT, Lopez CF. Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search. Front Cell Dev Biol 2023; 11:1198359. [PMID: 37691824 PMCID: PMC10485623 DOI: 10.3389/fcell.2023.1198359] [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: 04/01/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Many important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. We demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multi-model analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation.
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Affiliation(s)
- Bryan J. Glazer
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, United States
| | - Jonathan T. Lifferth
- Department of Human Genetics, Vanderbilt University, Nashville, TN, United States
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
- Altos Labs, Redwood City, CA, United States
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Veth TS, Francavilla C, Heck AJR, Altelaar M. Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling. Mol Cell Proteomics 2023; 22:100594. [PMID: 37328066 PMCID: PMC10368922 DOI: 10.1016/j.mcpro.2023.100594] [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: 02/01/2023] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 06/18/2023] Open
Abstract
Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.
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Affiliation(s)
- Tim S Veth
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Chiara Francavilla
- Division of Molecular and Cellular Function, School of Biological Science, and Manchester Breast Centre, Manchester Cancer Research Centre, Faculty of Biology Medicine and Health (FBMH), The University of Manchester, Manchester, UK
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands.
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10
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Carretero Chavez W, Krantz M, Klipp E, Kufareva I. kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models. BMC Bioinformatics 2023; 24:246. [PMID: 37308855 PMCID: PMC10258968 DOI: 10.1186/s12859-023-05329-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon ("reaction-contingency") formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called "combinatorial explosion" of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems. RESULTS We present the kboolnet toolkit ( https://github.com/Kufalab-UCSD/kboolnet , complete documentation at https://github.com/Kufalab-UCSD/kboolnet/wiki ), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined "modules". CONCLUSION The kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future.
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Affiliation(s)
- Willow Carretero Chavez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 USA
- Present Address: Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139 USA
| | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
- Present Address: School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 USA
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Hemedan AA, Schneider R, Ostaszewski M. Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses. FRONTIERS IN BIOINFORMATICS 2023; 3:1189723. [PMID: 37325771 PMCID: PMC10267406 DOI: 10.3389/fbinf.2023.1189723] [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: 03/19/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
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Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
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Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
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13
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Lesage R, Ferrao Blanco MN, Narcisi R, Welting T, van Osch GJVM, Geris L. An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis. BMC Biol 2022; 20:253. [DOI: 10.1186/s12915-022-01451-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes, while in silico modeling can help unravel that complexity. In this study, we aim to develop a virtual articular chondrocyte to guide experiments in order to rationalize the identification of potential drug targets via screening of combination therapies through computational modeling and simulations.
Results
We developed a signal transduction network model using knowledge-based and data-driven (machine learning) modeling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions potentially affecting the hypertrophic switch. A selection of promising combinations was further tested in a murine cell line and primary human chondrocytes, which notably highlighted a previously unreported synergistic effect between the protein kinase A and the fibroblast growth factor receptor 1.
Conclusions
Here, we provide a virtual articular chondrocyte in the form of a signal transduction interactive knowledge base and of an executable computational model. Our in silico-in vitro strategy opens new routes for developing osteoarthritis targeting therapies by refining the early stages of drug target discovery.
Graphical Abstract
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14
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Maheshwari P, Assmann SM, Albert R. Inference of a Boolean Network From Causal Logic Implications. Front Genet 2022; 13:836856. [PMID: 35783282 PMCID: PMC9246059 DOI: 10.3389/fgene.2022.836856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.
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Affiliation(s)
- Parul Maheshwari
- Department of Physics, Penn State University, University Park, PA, United States
- *Correspondence: Parul Maheshwari, ; Reka Albert,
| | - Sarah M. Assmann
- Biology Department, Penn State University, University Park, PA, United States
| | - Reka Albert
- Department of Physics, Penn State University, University Park, PA, United States
- Biology Department, Penn State University, University Park, PA, United States
- *Correspondence: Parul Maheshwari, ; Reka Albert,
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Vignet P, Coquet J, Auber S, Boudet M, Siegel A, Théret N. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Comput Biol 2022; 18:e1010175. [PMID: 35696426 PMCID: PMC9232147 DOI: 10.1371/journal.pcbi.1010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/24/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states. The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene). Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.
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Affiliation(s)
- Pierre Vignet
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jean Coquet
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Sébastien Auber
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Matéo Boudet
- IGEPP, Agrocampus Ouest, INRAE, Université de Rennes 1, Le Rheu, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- * E-mail: (AS); (NT)
| | - Nathalie Théret
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- * E-mail: (AS); (NT)
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Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments. Sci Rep 2022; 12:3856. [PMID: 35264634 PMCID: PMC8907219 DOI: 10.1038/s41598-022-07776-2] [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: 10/06/2021] [Accepted: 02/18/2022] [Indexed: 11/21/2022] Open
Abstract
In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.
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Putnins M, Campagne O, Mager DE, Androulakis IP. From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building. J Pharmacokinet Pharmacodyn 2022; 49:101-115. [PMID: 34988912 PMCID: PMC9876619 DOI: 10.1007/s10928-021-09797-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/27/2021] [Indexed: 01/27/2023]
Abstract
Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.
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Affiliation(s)
- M. Putnins
- Biomedical Engineering Department, Rutgers University, Piscataway, USA
| | - O. Campagne
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, USA
| | - D. E. Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, USA
| | - I. P. Androulakis
- Biomedical Engineering Department, Rutgers University, Piscataway, USA,Chemical & Biochemical Engineering Department, Rutgers University, Piscataway, USA
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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Hemedan AA, Niarakis A, Schneider R, Ostaszewski M. Boolean modelling as a logic-based dynamic approach in systems medicine. Comput Struct Biotechnol J 2022; 20:3161-3172. [PMID: 35782730 PMCID: PMC9234349 DOI: 10.1016/j.csbj.2022.06.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
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Affiliation(s)
- Ahmed Abdelmonem Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde – Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author at: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.
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Karanam A, Rappel WJ. Boolean modelling in plant biology. QUANTITATIVE PLANT BIOLOGY 2022; 3:e29. [PMID: 37077966 PMCID: PMC10095905 DOI: 10.1017/qpb.2022.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/24/2022] [Accepted: 11/16/2022] [Indexed: 05/03/2023]
Abstract
Signalling and genetic networks underlie most biological processes and are often complex, containing many highly connected components. Modelling these networks can provide insight into mechanisms but is challenging given that rate parameters are often not well defined. Boolean modelling, in which components can only take on a binary value with connections encoded by logic equations, is able to circumvent some of these challenges, and has emerged as a viable tool to probe these complex networks. In this review, we will give an overview of Boolean modelling, with a specific emphasis on its use in plant biology. We review how Boolean modelling can be used to describe biological networks and then discuss examples of its applications in plant genetics and plant signalling.
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Affiliation(s)
- Aravind Karanam
- Department of Physics, University of California, San Diego, La Jolla, California92093, USA
| | - Wouter-Jan Rappel
- Department of Physics, University of California, San Diego, La Jolla, California92093, USA
- Author for correspondence: W.-J. Rappel, E-mail:
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Karanam A, He D, Hsu PK, Schulze S, Dubeaux G, Karmakar R, Schroeder JI, Rappel WJ. Boolink: a graphical interface for open access Boolean network simulations and use in guard cell CO2 signaling. PLANT PHYSIOLOGY 2021; 187:2311-2322. [PMID: 34618035 PMCID: PMC8644243 DOI: 10.1093/plphys/kiab344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 06/30/2021] [Indexed: 05/02/2023]
Abstract
Signaling networks are at the heart of almost all biological processes. Most of these networks contain large number of components, and often either the connections between these components are not known or the rate equations that govern the dynamics of soluble signaling components are not quantified. This uncertainty in network topology and parameters can make it challenging to formulate detailed mathematical models. Boolean networks, in which all components are either on or off, have emerged as viable alternatives to detailed mathematical models that contain rate constants and other parameters. Therefore, open-source platforms of Boolean models for community use are desirable. Here, we present Boolink, a freely available graphical user interface that allows users to easily construct and analyze existing Boolean networks. Boolink can be applied to any Boolean network. We demonstrate its application using a previously published network for abscisic acid (ABA)-driven stomatal closure in Arabidopsis spp. (Arabidopsis thaliana). We also show how Boolink can be used to generate testable predictions by extending the network to include CO2 regulation of stomatal movements. Predictions of the model were experimentally tested, and the model was iteratively modified based on experiments showing that ABA effectively closes Arabidopsis stomata at near-zero CO2 concentrations (1.5-ppm CO2). Thus, Boolink enables public generation and the use of existing Boolean models, including the prior developed ABA signaling model with added CO2 signaling components.
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Affiliation(s)
- Aravind Karanam
- Physics Department, University of California, San Diego, La Jolla, California 92093, USA
| | - David He
- Physics Department, University of California, San Diego, La Jolla, California 92093, USA
| | - Po-Kai Hsu
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093-0116, USA
| | - Sebastian Schulze
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093-0116, USA
| | - Guillaume Dubeaux
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093-0116, USA
| | - Richa Karmakar
- Physics Department, University of California, San Diego, La Jolla, California 92093, USA
| | - Julian I Schroeder
- Division of Biological Sciences, Section of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093-0116, USA
| | - Wouter-Jan Rappel
- Physics Department, University of California, San Diego, La Jolla, California 92093, USA
- Author for communication:
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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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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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Bernardo-Faura M, Rinas M, Wirbel J, Pertsovskaya I, Pliaka V, Messinis DE, Vila G, Sakellaropoulos T, Faigle W, Stridh P, Behrens JR, Olsson T, Martin R, Paul F, Alexopoulos LG, Villoslada P, Saez-Rodriguez J. Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis. Genome Med 2021; 13:117. [PMID: 34271980 PMCID: PMC8284018 DOI: 10.1186/s13073-021-00925-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/14/2021] [Indexed: 11/21/2022] Open
Abstract
Background Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. Methods Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. Results Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Conclusions Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00925-8.
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Affiliation(s)
- Marti Bernardo-Faura
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Melanie Rinas
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany
| | - Jakob Wirbel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany
| | - Inna Pertsovskaya
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | - Vicky Pliaka
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | | | - Gemma Vila
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | | | | | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Janina R Behrens
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Friedemann Paul
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Leonidas G Alexopoulos
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece. .,ProtATonce Ltd., Athens, Greece.
| | - Pablo Villoslada
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain.
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. .,Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany. .,Institute for Computational Biomedicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany.
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25
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Trinh HC, Kwon YK. A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data. Bioinformatics 2021; 37:i383-i391. [PMID: 34252959 PMCID: PMC8275338 DOI: 10.1093/bioinformatics/btab295] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/24/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression. RESULTS In this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time. AVAILABILITY AND IMPLEMENTATION Source code and data are freely available at https://github.com/csclab/CGA-BNI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hung-Cuong Trinh
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 758307, Vietnam
| | - Yung-Keun Kwon
- Department of IT Convergence, University of Ulsan, Ulsan 680-749, Korea
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26
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Cesareni G, Sacco F, Perfetto L. Assembling Disease Networks From Causal Interaction Resources. Front Genet 2021; 12:694468. [PMID: 34178043 PMCID: PMC8226215 DOI: 10.3389/fgene.2021.694468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 12/27/2022] Open
Abstract
The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
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Affiliation(s)
- Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology, Fondazione Human Technopole, Milan, Italy
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27
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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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28
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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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29
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Wijaya LS, Trairatphisan P, Gabor A, Niemeijer M, Keet J, Alcalà Morera A, Snijders KE, Wink S, Yang H, Schildknecht S, Stevens JL, Bouwman P, Kamp H, Hengstler J, Beltman J, Leist M, Le Dévédec S, Saez-Rodriguez J, van de Water B. Integration of temporal single cell cellular stress response activity with logic-ODE modeling reveals activation of ATF4-CHOP axis as a critical predictor of drug-induced liver injury. Biochem Pharmacol 2021; 190:114591. [PMID: 33957093 DOI: 10.1016/j.bcp.2021.114591] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022]
Abstract
Drug-induced liver injury (DILI) is the most prevalent adversity encountered in drug development and clinical settings leading to urgent needs to understand the underlying mechanisms. In this study, we have systematically investigated the dynamics of the activation of cellular stress response pathways and cell death outcomes upon exposure of a panel of liver toxicants using live cell imaging of fluorescent reporter cell lines. We established a comprehensive temporal dynamic response profile of a large set of BAC-GFP HepG2 cell lines representing the following components of stress signaling: i) unfolded protein response (UPR) [ATF4, XBP1, BIP and CHOP]; ii) oxidative stress [NRF2, SRXN1, HMOX1]; iii) DNA damage [P53, P21, BTG2, MDM2]; and iv) NF-κB pathway [A20, ICAM1]. We quantified the single cell GFP expression as a surrogate for endogenous protein expression using live cell imaging over > 60 h upon exposure to 14 DILI compounds at multiple concentrations. Using logic-based ordinary differential equation (Logic-ODE), we modelled the dynamic profiles of the different stress responses and extracted specific descriptors potentially predicting the progressive outcomes. We identified the activation of ATF4-CHOP axis of the UPR as the key pathway showing the highest correlation with cell death upon DILI compound perturbation. Knocking down main components of the UPR provided partial protection from compound-induced cytotoxicity, indicating a complex interplay among UPR components as well as other stress pathways. Our results suggest that a systematic analysis of the temporal dynamics of ATF4-CHOP axis activation can support the identification of DILI risk for new candidate drugs.
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Affiliation(s)
- Lukas Surya Wijaya
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Panuwat Trairatphisan
- Heidelberg University, Faculty of Medicine, Institute of Computational Biomedicine, 69120 Heidelberg, Germany
| | - Attila Gabor
- Heidelberg University, Faculty of Medicine, Institute of Computational Biomedicine, 69120 Heidelberg, Germany
| | - Marije Niemeijer
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Jason Keet
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Ariadna Alcalà Morera
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Kirsten E Snijders
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Steven Wink
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Huan Yang
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Stefan Schildknecht
- In vitro Toxicology and Biomedicine, Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - James L Stevens
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Peter Bouwman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Hennicke Kamp
- BASF SE, Experimental Toxicology and Ecology, Ludwigshafen am Rhein, Germany
| | - Jan Hengstler
- Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Joost Beltman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Marcel Leist
- In vitro Toxicology and Biomedicine, Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Sylvia Le Dévédec
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Institute of Computational Biomedicine, 69120 Heidelberg, Germany; RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074 Aachen, Germany
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands.
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30
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Pugliese GM, Latini S, Massacci G, Perfetto L, Sacco F. Combining Mass Spectrometry-Based Phosphoproteomics with a Network-Based Approach to Reveal FLT3-Dependent Mechanisms of Chemoresistance. Proteomes 2021; 9:19. [PMID: 33925552 PMCID: PMC8167576 DOI: 10.3390/proteomes9020019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 12/19/2022] Open
Abstract
FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.
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Affiliation(s)
- Giusj Monia Pugliese
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (G.M.P.); (S.L.); (G.M.)
| | - Sara Latini
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (G.M.P.); (S.L.); (G.M.)
| | - Giorgia Massacci
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (G.M.P.); (S.L.); (G.M.)
| | - Livia Perfetto
- Fondazione Human Technopole, Department of Biology, Via Cristina Belgioioso 171, 20157 Milan, Italy;
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Via delle Ricerca Scientifica 1, 00133 Rome, Italy; (G.M.P.); (S.L.); (G.M.)
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31
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Aghamiri SS, Delaplace F. TaBooN Boolean Network Synthesis Based on Tabu Search. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; PP:2499-2511. [PMID: 33661736 DOI: 10.1109/tcbb.2021.3063817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present Taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the TaBooN work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. TaBooN is an automated method for Boolean Network inference from.
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32
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Gjerga E, Trairatphisan P, Gabor A, Koch H, Chevalier C, Ceccarelli F, Dugourd A, Mitsos A, Saez-Rodriguez J. Converting networks to predictive logic models from perturbation signalling data with CellNOpt. Bioinformatics 2021; 36:4523-4524. [PMID: 32516357 PMCID: PMC7575044 DOI: 10.1093/bioinformatics/btaa561] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/26/2020] [Accepted: 06/03/2020] [Indexed: 11/27/2022] Open
Abstract
Summary The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. CellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones to keep up with the computational demand of increasingly large datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implementation for semi-quantitative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify missing links, (v) systematic post-hoc analyses and (vi) an R-Shiny tool to run CellNOpt interactively. Availability and implementation R-package(s): https://github.com/saezlab/cellnopt. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Enio Gjerga
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
| | - Panuwat Trairatphisan
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany
| | - Attila Gabor
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany
| | - Hermann Koch
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Celine Chevalier
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,University Paris-Saclay, Espace Technologique Bat. Discovery,91190 Saint-Aubin, France
| | - Franceco Ceccarelli
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,Computer Laboratory, University of Cambridge, Cambridge CB2 1TN, UK
| | - Aurelien Dugourd
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
| | - Alexander Mitsos
- Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
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33
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Baumgartner L, Wuertz-Kozak K, Le Maitre CL, Wignall F, Richardson SM, Hoyland J, Ruiz Wills C, González Ballester MA, Neidlin M, Alexopoulos LG, Noailly J. Multiscale Regulation of the Intervertebral Disc: Achievements in Experimental, In Silico, and Regenerative Research. Int J Mol Sci 2021; 22:E703. [PMID: 33445782 PMCID: PMC7828304 DOI: 10.3390/ijms22020703] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/17/2022] Open
Abstract
Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations' processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies.
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Affiliation(s)
- Laura Baumgartner
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; (L.B.); (C.R.W.); (M.A.G.B.)
| | - Karin Wuertz-Kozak
- Department of Biomedical Engineering, Rochester Institute of Technology (RIT), Rochester, NY 14623, USA;
- Schön Clinic Munich Harlaching, Spine Center, Academic Teaching Hospital and Spine Research Institute of the Paracelsus Medical University Salzburg (Austria), 81547 Munich, Germany
| | - Christine L. Le Maitre
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Francis Wignall
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9PT, UK; (F.W.); (S.M.R.); (J.H.)
| | - Stephen M. Richardson
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9PT, UK; (F.W.); (S.M.R.); (J.H.)
| | - Judith Hoyland
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9PT, UK; (F.W.); (S.M.R.); (J.H.)
| | - Carlos Ruiz Wills
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; (L.B.); (C.R.W.); (M.A.G.B.)
| | - Miguel A. González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; (L.B.); (C.R.W.); (M.A.G.B.)
- Catalan Institution for Research and Advanced Studies (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain
| | - Michael Neidlin
- Department of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece; (M.N.); (L.G.A.)
| | - Leonidas G. Alexopoulos
- Department of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece; (M.N.); (L.G.A.)
| | - Jérôme Noailly
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; (L.B.); (C.R.W.); (M.A.G.B.)
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34
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Musilova J, Sedlar K. Tools for time-course simulation in systems biology: a brief overview. Brief Bioinform 2021; 22:6076933. [PMID: 33423059 DOI: 10.1093/bib/bbaa392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022] Open
Abstract
Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.
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Affiliation(s)
- Jana Musilova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Karel Sedlar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
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35
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Dai H, Jin QQ, Li L, Chen LN. Reconstructing gene regulatory networks in single-cell transcriptomic data analysis. Zool Res 2020; 41:599-604. [PMID: 33124218 PMCID: PMC7671911 DOI: 10.24272/j.issn.2095-8137.2020.215] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/20/2020] [Indexed: 11/07/2022] Open
Abstract
Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level. Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples. Here, we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.
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Affiliation(s)
- Hao Dai
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China
| | - Qi-Qi Jin
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Lin Li
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luo-Nan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
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36
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Howell RSM, Klemm C, Thorpe PH, Csikász-Nagy A. Unifying the mechanism of mitotic exit control in a spatiotemporal logical model. PLoS Biol 2020; 18:e3000917. [PMID: 33180788 PMCID: PMC7685450 DOI: 10.1371/journal.pbio.3000917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 11/24/2020] [Accepted: 10/09/2020] [Indexed: 11/18/2022] Open
Abstract
The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell-cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.
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Affiliation(s)
- Rowan S M Howell
- The Francis Crick Institute, London, United Kingdom.,Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom
| | - Cinzia Klemm
- School of Biological and Chemical Sciences, Queen Mary University, London, United Kingdom
| | - Peter H Thorpe
- School of Biological and Chemical Sciences, Queen Mary University, London, United Kingdom
| | - Attila Csikász-Nagy
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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37
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Mantini G, Pham TV, Piersma SR, Jimenez CR. Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies. Proteomics 2020; 21:e1900312. [PMID: 32875713 DOI: 10.1002/pmic.201900312] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 07/09/2020] [Indexed: 12/24/2022]
Abstract
Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi-omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi-omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi-omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi-omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi-omics data integration and other algorithms for multi-omics data integration promising for future application to phosphoproteomics data.
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Affiliation(s)
- Giulia Mantini
- Department of Medical Oncology, OncoProteomics Laboratory, CCA 1-60, Amsterdam UMC VUmc-location, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Thang V Pham
- Department of Medical Oncology, OncoProteomics Laboratory, CCA 1-60, Amsterdam UMC VUmc-location, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Sander R Piersma
- Department of Medical Oncology, OncoProteomics Laboratory, CCA 1-60, Amsterdam UMC VUmc-location, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Connie R Jimenez
- Department of Medical Oncology, OncoProteomics Laboratory, CCA 1-60, Amsterdam UMC VUmc-location, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
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38
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Loos C, Lauffenburger DA, Alter G. Dissecting the antibody-OME: past, present, and future. Curr Opin Immunol 2020; 65:89-96. [PMID: 32755751 DOI: 10.1016/j.coi.2020.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/24/2020] [Indexed: 02/08/2023]
Abstract
Humoral immunity is key to protection for nearly all licensed vaccines. Yet, the design of vaccines has been more difficult for some of our most deadly killers (e.g. HIV, influenza, Dengue virus, etc.), likely due to our incomplete understanding of the precise immunological mechanisms associated with protection. Humoral immunity is governed both by B-cells and their bi-functional secreted antibodies, all of which have a unique capacity to evolve during an immune response. Current OMIC technologies capture individual features of the humoral immune response, providing a glimpse into humoral components (Fab/Fc/B-cell-omic), but fail to provide a wholistic view of the humoral response as a collective functional arm. Here, we dissect current OMIC strategies reviewing experimental and computational approaches, that if integrated could provide a true systems-level view of the humoral immune response.
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Affiliation(s)
- Carolin Loos
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Galit Alter
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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39
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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40
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Savage SR, Zhang B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteomics 2020; 17:27. [PMID: 32676006 PMCID: PMC7353784 DOI: 10.1186/s12014-020-09290-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 07/04/2020] [Indexed: 12/19/2022] Open
Abstract
Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.
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Affiliation(s)
- Sara R. Savage
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
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41
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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42
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Khazaaleh M, Samarasinghe S. Using activity time windows and logical representation to reduce the complexity of biological network models: G1/S checkpoint pathway with DNA damage. Biosystems 2020; 191-192:104128. [DOI: 10.1016/j.biosystems.2020.104128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/25/2020] [Accepted: 02/25/2020] [Indexed: 01/14/2023]
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43
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Montagud A, Traynard P, Martignetti L, Bonnet E, Barillot E, Zinovyev A, Calzone L. Conceptual and computational framework for logical modelling of biological networks deregulated in diseases. Brief Bioinform 2020; 20:1238-1249. [PMID: 29237040 DOI: 10.1093/bib/bbx163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/24/2017] [Indexed: 01/02/2023] Open
Abstract
Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.
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44
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Gedeon T. Multi-parameter exploration of dynamics of regulatory networks. Biosystems 2020; 190:104113. [PMID: 32057819 PMCID: PMC7082111 DOI: 10.1016/j.biosystems.2020.104113] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/24/2020] [Accepted: 02/02/2020] [Indexed: 01/10/2023]
Abstract
Over the last twenty years advances in systems biology have changed our views on microbial communities and promise to revolutionize treatment of human diseases. In almost all scientific breakthroughs since time of Newton, mathematical modeling has played a prominent role. Regulatory networks emerged as preferred descriptors of how abundances of molecular species depend on each other. However, the central question on how cellular phenotypes emerge from dynamics of these network remains elusive. The principal reason is that differential equation models in the field of biology (while so successful in areas of physics and physical chemistry), do not arise from first principles, and these models suffer from lack of proper parameterization. In response to these challenges, discrete time models based on Boolean networks have been developed. In this review, we discuss an emerging modeling paradigm that combines ideas from differential equations and Boolean models, and has been developed independently within dynamical systems and computer science communities. The result is an approach that can associate a range of potential dynamical behaviors to a network, arrange the descriptors of the dynamics in a searchable database, and allows for multi-parameter exploration of the dynamics akin to bifurcation theory. Since this approach is computationally accessible for moderately sized networks, it allows, perhaps for the first time, to rationally compare different network topologies based on their dynamics.
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Affiliation(s)
- Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, MT 59715, United States of America.
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45
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Schwab JD, Kühlwein SD, Ikonomi N, Kühl M, Kestler HA. Concepts in Boolean network modeling: What do they all mean? Comput Struct Biotechnol J 2020; 18:571-582. [PMID: 32257043 PMCID: PMC7096748 DOI: 10.1016/j.csbj.2020.03.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/27/2020] [Accepted: 03/01/2020] [Indexed: 12/02/2022] Open
Abstract
Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models.
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Affiliation(s)
- Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Silke D Kühlwein
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Michael Kühl
- Institute of Biochemistry and Molecular Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
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46
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Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, Saez‐Rodriguez J. Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Mol Syst Biol 2020; 16:e8664. [PMID: 32073727 PMCID: PMC7029724 DOI: 10.15252/msb.20188664] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 12/22/2022] Open
Abstract
Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.
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Affiliation(s)
- Federica Eduati
- European Molecular Biology Laboratory (EMBL)Genome Biology UnitHeidelbergGermany
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
- Joint Research Centre for Computational Biomedicine (JRC‐COMBINE)Faculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | | | - Jessica Wappler
- Department SurgeryMolecular Tumor BiologyRWTH University HospitalAachenGermany
| | - Thorsten Cramer
- Department SurgeryMolecular Tumor BiologyRWTH University HospitalAachenGermany
- ESCAM – European Surgery Center Aachen MaastrichtAachenGermany
- ESCAM – European Surgery Center Aachen MaastrichtMaastrichtThe Netherlands
| | - Christoph A Merten
- European Molecular Biology Laboratory (EMBL)Genome Biology UnitHeidelbergGermany
| | | | - Julio Saez‐Rodriguez
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
- Joint Research Centre for Computational Biomedicine (JRC‐COMBINE)Faculty of MedicineRWTH Aachen UniversityAachenGermany
- Institute for Computational BiomedicineFaculty of MedicineBIOQUANT‐CenterHeidelberg UniversityHeidelbergGermany
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47
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Franzese N, Groce A, Murali TM, Ritz A. Hypergraph-based connectivity measures for signaling pathway topologies. PLoS Comput Biol 2019; 15:e1007384. [PMID: 31652258 PMCID: PMC6834280 DOI: 10.1371/journal.pcbi.1007384] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 11/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022] Open
Abstract
Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules “connected” in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway’s downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis. Signaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules “connected” in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are “connected” in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is “downstream” of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.
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Affiliation(s)
- Nicholas Franzese
- Biology Department, Reed College, Portland, Oregon, United States of America
- Computer Science Department, Reed College, Portland, Oregon, United States of America
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Adam Groce
- Computer Science Department, Reed College, Portland, Oregon, United States of America
| | - T. M. Murali
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
- ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Anna Ritz
- Biology Department, Reed College, Portland, Oregon, United States of America
- * E-mail:
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48
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Sun X, Hu B. Mathematical modeling and computational prediction of cancer drug resistance. Brief Bioinform 2019; 19:1382-1399. [PMID: 28981626 PMCID: PMC6402530 DOI: 10.1093/bib/bbx065] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Indexed: 12/23/2022] Open
Abstract
Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.
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Affiliation(s)
- Xiaoqiang Sun
- Zhong-shan School of Medicine, Sun Yat-Sen University
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University
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49
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Blucher AS, McWeeney SK, Stein L, Wu G. Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz. F1000Res 2019; 8:908. [PMID: 31372215 PMCID: PMC6644836 DOI: 10.12688/f1000research.19592.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2019] [Indexed: 01/08/2023] Open
Abstract
The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.
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Affiliation(s)
- Aurora S Blucher
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Shannon K McWeeney
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA.,Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA
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50
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Pacini C, Koziol MJ. Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0073. [PMID: 29685977 PMCID: PMC5915717 DOI: 10.1098/rstb.2017.0073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2017] [Indexed: 02/07/2023] Open
Abstract
It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes. This article is part of a discussion meeting issue ‘Frontiers in epigenetic chemical biology’.
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Affiliation(s)
- Clare Pacini
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Magdalena J Koziol
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK .,Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
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