1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Konur S, Gheorghe M, Krasnogor N. Verifiable biology. J R Soc Interface 2023; 20:20230019. [PMID: 37160165 PMCID: PMC10169095 DOI: 10.1098/rsif.2023.0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.
Collapse
Affiliation(s)
- Savas Konur
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Marian Gheorghe
- Department of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK
| | - Natalio Krasnogor
- School of Computing Science, Newcastle University, Science Square, Newcastle upon Tyne NE4 5TG, UK
| |
Collapse
|
3
|
Lucas M, Morris A, Townsend-Teague A, Tichit L, Habermann B, Barrat A. Inferring cell cycle phases from a partially temporal network of protein interactions. CELL REPORTS METHODS 2023; 3:100397. [PMID: 36936083 PMCID: PMC10014271 DOI: 10.1016/j.crmeth.2023.100397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/13/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023]
Abstract
The temporal organization of biological systems is key for understanding them, but current methods for identifying this organization are often ad hoc and require prior knowledge. We present Phasik, a method that automatically identifies this multiscale organization by combining time series data (protein or gene expression) and interaction data (protein-protein interaction network). Phasik builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate the method's effectiveness by recovering well-known phases and sub-phases of the cell cycle of budding yeast and phase arrests of mutants. We also show its general applicability using temporal gene expression data from circadian rhythms in wild-type and mutant mouse models. We systematically test Phasik's robustness and investigate the effect of having only partial temporal information. As time-resolved, multiomics datasets become more common, this method will allow the study of temporal regulation in lesser-known biological contexts, such as development, metabolism, and disease.
Collapse
Affiliation(s)
- Maxime Lucas
- Aix Marseille University, CNRS, I2M UMR 7373, Turing Center for Living Systems, Marseille, France
- Aix Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille, France
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | | | | | - Laurent Tichit
- Aix Marseille University, CNRS, I2M UMR 7373, Turing Center for Living Systems, Marseille, France
| | - Bianca Habermann
- Aix Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems, Marseille, France
| | - Alain Barrat
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| |
Collapse
|
4
|
Thomas C, Cosme M, Gaucherel C, Pommereau F. Model-checking ecological state-transition graphs. PLoS Comput Biol 2022; 18:e1009657. [PMID: 35666771 PMCID: PMC9203009 DOI: 10.1371/journal.pcbi.1009657] [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: 11/18/2021] [Revised: 06/16/2022] [Accepted: 05/08/2022] [Indexed: 11/18/2022] Open
Abstract
Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications. This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.
Collapse
Affiliation(s)
- Colin Thomas
- IBISC, Univ. Évry, Univ. Paris-Saclay, 91020 Évry-Courcouronne, France
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Maximilien Cosme
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Cédric Gaucherel
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Franck Pommereau
- IBISC, Univ. Évry, Univ. Paris-Saclay, 91020 Évry-Courcouronne, France
| |
Collapse
|
5
|
Montagud A, Béal J, Tobalina L, Traynard P, Subramanian V, Szalai B, Alföldi R, Puskás L, Valencia A, Barillot E, Saez-Rodriguez J, Calzone L. Patient-specific Boolean models of signalling networks guide personalised treatments. eLife 2022; 11:e72626. [PMID: 35164900 PMCID: PMC9018074 DOI: 10.7554/elife.72626] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/27/2022] [Indexed: 11/22/2022] Open
Abstract
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
Collapse
Affiliation(s)
- Arnau Montagud
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3BarcelonaSpain
| | - Jonas Béal
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
| | - Pauline Traynard
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Vigneshwari Subramanian
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
| | - Bence Szalai
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
- Semmelweis University, Faculty of Medicine, Department of PhysiologyBudapestHungary
| | | | | | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3BarcelonaSpain
- ICREA, Pg. Lluís Companys 23BarcelonaSpain
| | - Emmanuel Barillot
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen UniversityAachenGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg UniversityHeidelbergGermany
| | - Laurence Calzone
- Institut Curie, PSL Research UniversityParisFrance
- INSERM, U900ParisFrance
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyParisFrance
| |
Collapse
|
6
|
Noël V, Ruscone M, Stoll G, Viara E, Zinovyev A, Barillot E, Calzone L. WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically. Front Mol Biosci 2021; 8:754444. [PMID: 34888352 PMCID: PMC8651056 DOI: 10.3389/fmolb.2021.754444] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/20/2021] [Indexed: 12/13/2022] Open
Abstract
WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS.
Collapse
Affiliation(s)
- Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Marco Ruscone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Gautier Stoll
- Equipe 11 labellisée Par la Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, INSERM U1138, Universite de Paris, Sorbonne Universite, Paris, France
| | | | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| |
Collapse
|
7
|
SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models. Processes (Basel) 2021. [DOI: 10.3390/pr9101830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.
Collapse
|
8
|
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.
Collapse
|
9
|
Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics 2021; 36:4473-4482. [PMID: 32403123 PMCID: PMC7575051 DOI: 10.1093/bioinformatics/btaa484] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/17/2020] [Accepted: 05/06/2020] [Indexed: 12/16/2022] Open
Abstract
Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Sara Sadat Aghamiri
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Vidisha Singh
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Aurélien Naldi
- Département de Biologie, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ècole Normale Supérieure, CNRS, INSERM, Université PSL, Paris 75005, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Anna Niarakis
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| |
Collapse
|
10
|
Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response. Cancers (Basel) 2020; 12:cancers12123600. [PMID: 33276543 PMCID: PMC7761568 DOI: 10.3390/cancers12123600] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The future of cancer immunotherapy relies on a combination of individually targeted therapies. However, a lot of experiments are needed to define the most effective combinations of drugs. A computational and modelling approach could help reduce the number of experiments and suggest optimal treatments to test. This article presents a logical model of T cell activation influenced by immune checkpoints, and explores the effect of these checkpoints, suggests mechanisms that would explain why some treatments might be better suited than others. The model includes not only programmed cell death protein 1 (PD1) and cytotoxic T-lymphocyte-associated protein 4 (CTL4) downstream pathways but also those of other immune checkpoints such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), lymphocyte activation gene 3 (LAG3), T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), cluster of differentiation 226 (CD226), inducible T-cell costimulator (ICOS), and tumour necrosis factor receptors (TNFRs). Abstract After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.
Collapse
|
11
|
Hernandez C, Thomas-Chollier M, Naldi A, Thieffry D. Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors. Front Physiol 2020; 11:558606. [PMID: 33101049 PMCID: PMC7554341 DOI: 10.3389/fphys.2020.558606] [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] [Received: 05/03/2020] [Accepted: 08/19/2020] [Indexed: 12/31/2022] Open
Abstract
At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.
Collapse
Affiliation(s)
- Céline Hernandez
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Morgane Thomas-Chollier
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France.,Institut Universitaire de France, Paris, France
| | - Aurélien Naldi
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Denis Thieffry
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| |
Collapse
|
12
|
Paulevé L, Kolčák J, Chatain T, Haar S. Reconciling qualitative, abstract, and scalable modeling of biological networks. Nat Commun 2020; 11:4256. [PMID: 32848126 PMCID: PMC7450094 DOI: 10.1038/s41467-020-18112-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/30/2020] [Indexed: 11/24/2022] Open
Abstract
Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.
Collapse
Affiliation(s)
- Loïc Paulevé
- Université Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, 351 cours de la Libération, Talence, 33400, France.
- LRI UMR8623, Université Paris-Sud, CNRS, Université Paris-Saclay, Bat 650 Ada Lovelace, Rue Raimond Castaing, Gif-sur-Yvette, 91190, France.
| | - Juri Kolčák
- Inria and LSV, CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay, 4 avenue des Sciences, Gif-sur-Yvette, 91190, France
| | - Thomas Chatain
- Inria and LSV, CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay, 4 avenue des Sciences, Gif-sur-Yvette, 91190, France
| | - Stefan Haar
- Inria and LSV, CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay, 4 avenue des Sciences, Gif-sur-Yvette, 91190, France
| |
Collapse
|
13
|
Guttula PK, Monteiro PT, Gupta MK. A Boolean Logical model for Reprogramming of Testes-derived male Germline Stem Cells into Germline pluripotent stem cells. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 192:105473. [PMID: 32305736 DOI: 10.1016/j.cmpb.2020.105473] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available. However, the mechanism of reprogramming of GS cells into GPS cells remains elusive. This study was designed to develop a Boolean logical model of gene regulatory network (GRN) that might be involved in the reprogramming of GS cells into GPS cells. METHODS The gene expression profile of GS and GPS cells (GSE ID: GSE11274 and GSE74151) were analyzed using R Bioconductor to identify differentially expressed genes (DEGs) and were functionally annotated with DAVID server. Potential pluripotent genes among the DEGs were then predicted using a combination of machine learning [Support Vector Machine (SVM)] and BLAST search. Protein isoforms were identified by pattern matching with UniProt database with in-house scripts written in C++. Both linear and non-linear interaction maps were generated using the STRING server. CellNet is used to study the relationship of GRNs between the GS and GPS cells. Finally, the GRNs involving all the genes from integrated methods and literature was constructed and qualitative modelling for reprogramming of GS to GPS cells were done by considering the discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tool. RESULTS Through the use of machine learning and logical modeling, the present study identified 3585 DEGs and 221 novel pluripotent genes including Tet1, Cdh1, Tfap2c, Etv4, Etv5, Prdm14, and Prdm10 in GPS cells. Pathway analysis revealed that important signaling pathways such as core pluripotency network, PI3K-Akt, WNT, GDNF and BMP4 signalling pathways were important for the reprogramming of GS cells to GPS cells. On the other hand, CellNet analysis of GRNs of GS and GPS cells revealed that GS cells were similar to gonads whereas GPS cells were similar to ESCs in gene expression profile. A logical regulatory model was developed, which showed that TGFβ negatively regulated the reprogramming of the GS to GPS cells, as confirmed by perturbations studies. CONCLUSION The study identified novel pluripotent genes involved in the reprogramming of GS cells into GPS cells. A multivalued logical model of cellular reprogramming is proposed, which suggests that reprogramming of GS cells to GPS cells involves signalling pathways namely LIF, GDNF, BMP4, and TGFβ along with some novel pluripotency genes.
Collapse
Affiliation(s)
- Praveen Kumar Guttula
- Gene Manipulation Laboratory, Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela,769008, India
| | - Pedro T Monteiro
- Department of Computer Science and Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal; INESC-ID, SW Algorithms and Tools for Constraint Solving Group, R. Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Mukesh Kumar Gupta
- Gene Manipulation Laboratory, Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela,769008, India.
| |
Collapse
|
14
|
Koltai M, Noel V, Zinovyev A, Calzone L, Barillot E. Exact solving and sensitivity analysis of stochastic continuous time Boolean models. BMC Bioinformatics 2020; 21:241. [PMID: 32527218 PMCID: PMC7291460 DOI: 10.1186/s12859-020-03548-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 05/18/2020] [Indexed: 12/02/2022] Open
Abstract
Background Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models. Results We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation’s kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights. Conclusion Up to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model’s timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.
Collapse
Affiliation(s)
- Mihály Koltai
- Institut Curie, PSL Research University, Paris, F-75005, France. .,INSERM, U900, Paris, F-75005, France. .,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France.
| | - Vincent Noel
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, F-75005, France.,INSERM, U900, Paris, F-75005, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, F-75006, France
| |
Collapse
|
15
|
Réda C, Wilczyński B. Automated inference of gene regulatory networks using explicit regulatory modules. J Theor Biol 2020; 486:110091. [PMID: 31790679 DOI: 10.1016/j.jtbi.2019.110091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 08/09/2019] [Accepted: 11/21/2019] [Indexed: 11/25/2022]
Abstract
Gene regulatory networks are a popular tool for modelling important biological phenomena, such as cell differentiation or oncogenesis. Efficient identification of the causal connections between genes, their products and regulating transcription factors, is key to understanding how defects in their function may trigger diseases. Modelling approaches should keep up with the ever more detailed descriptions of the biological phenomena at play, as provided by new experimental findings and technical improvements. In recent years, we have seen great improvements in mapping of specific binding sites of many transcription factors to distinct regulatory regions. Recent gene regulatory network models use binding measurements; but usually only to define gene-to-gene interactions, ignoring regulatory module structure. Moreover, current huge amount of transcriptomic data, and exploration of all possible cis-regulatory arrangements which can lead to the same transcriptomic response, makes manual model building both tedious and time-consuming. In our paper, we propose a method to specify possible regulatory connections in a given Boolean network, based on transcription factor binding evidence. This is implemented by an algorithm which expands a regular Boolean network model into a "cis-regulatory" Boolean network model. This expanded model explicitly defines regulatory regions as additional nodes in the network, and adds new, valuable biological insights to the system dynamics. The expanded model can automatically be compared with expression data. And, for each node, a regulatory function, consistent with the experimental data, can be found. The resulting models are usually more constrained (by biologically-motivated metadata), and can then be inspected in in silico simulations. The fully automated method for model identification has been implemented in Python, and the expansion algorithm in R. The method resorts to the Z3 Satisfiability Modulo Theories (SMT) solver, and is similar to the RE:IN application (Yordanov et al., 2016). It is available on https://github.com/regulomics/expansion-network.
Collapse
Affiliation(s)
- Clémence Réda
- École Normale Supérieure Paris-Saclay, 61 avenue du Président Wilson, 94230 Cachan, France.
| | - Bartek Wilczyński
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, ulica Stefana Banacha 2, Warsaw 02-097, Poland
| |
Collapse
|
16
|
Ha S, Dimitrova E, Hoops S, Altarawy D, Ansariola M, Deb D, Glazebrook J, Hillmer R, Shahin H, Katagiri F, McDowell J, Megraw M, Setubal J, Tyler BM, Laubenbacher R. PlantSimLab - a modeling and simulation web tool for plant biologists. BMC Bioinformatics 2019; 20:508. [PMID: 31638901 PMCID: PMC6805577 DOI: 10.1186/s12859-019-3094-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 09/10/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. RESULTS This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. CONCLUSIONS Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.
Collapse
Affiliation(s)
- S Ha
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, VA, USA
| | - E Dimitrova
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | - S Hoops
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA
| | | | | | - D Deb
- Department of Natural Sciences, Mercy College, Dobbs Ferry, NY, USA
| | - J Glazebrook
- College of Biological Sciences, University of Minnesota, St. Paul, MN, USA
| | - R Hillmer
- Mendel Biological Solutions, San Franciso, CA, USA
| | - H Shahin
- Virginia Tech, Blacksburg, VA, USA
| | - F Katagiri
- College of Biological Sciences, University of Minnesota, St. Paul, MN, USA
| | - J McDowell
- Department of Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA, USA
| | - M Megraw
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - J Setubal
- Biochemistry Department, University of Sao Paolo, Sao Paolo, Brazil.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - B M Tyler
- Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR, USA
| | - R Laubenbacher
- Center for Quantitative Medicine, School of Medicine, University of Connecticut, Hartford, USA.
| |
Collapse
|
17
|
Gan X, Albert R. General method to find the attractors of discrete dynamic models of biological systems. Phys Rev E 2018; 97:042308. [PMID: 29758614 DOI: 10.1103/physreve.97.042308] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Indexed: 12/31/2022]
Abstract
Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.
Collapse
Affiliation(s)
- Xiao Gan
- Department of Physics, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| |
Collapse
|
18
|
Fages F, Martinez T, Rosenblueth DA, Soliman S. Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1138-1151. [PMID: 29994637 DOI: 10.1109/tcbb.2018.2805686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Biochemical reaction networks are one of the most widely used formalisms in systems biology to describe the molecular mechanisms of high-level cell processes. However, modellers also reason with influence diagrams to represent the positive and negative influences between molecular species and may find an influence network useful in the process of building a reaction network. In this paper, we introduce a formalism of influence networks with forces, and equip it with a hierarchy of Boolean, Petri net, stochastic and differential semantics, similarly to reaction networks with rates. We show that the expressive power of influence networks is the same as that of reaction networks under the differential semantics, but weaker under the discrete semantics. Furthermore, the hierarchy of semantics leads us to consider a (positive) Boolean semantics that cannot test the absence of a species, that we compare with the (negative) Boolean semantics with test for absence of a species in gene regulatory networks à la Thomas. We study the monotonicity properties of the positive semantics and derive from them an algorithm to compute attractors in both the positive and negative Boolean semantics. We illustrate our results on models of the literature about the p53/Mdm2 DNA damage repair system, the circadian clock, and the influence of MAPK signaling on cell-fate decision in urinary bladder cancer.
Collapse
|
19
|
Naldi A, Hernandez C, Levy N, Stoll G, Monteiro PT, Chaouiya C, Helikar T, Zinovyev A, Calzone L, Cohen-Boulakia S, Thieffry D, Paulevé L. The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks. Front Physiol 2018; 9:680. [PMID: 29971009 PMCID: PMC6018415 DOI: 10.3389/fphys.2018.00680] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 05/15/2018] [Indexed: 01/07/2023] Open
Abstract
Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.
Collapse
Affiliation(s)
- Aurélien Naldi
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Céline Hernandez
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Nicolas Levy
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
- École Normale Supérieure de Lyon, Lyon, France
| | - Gautier Stoll
- Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France
- Équipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U1138, Paris, France
- Université Pierre et Marie Curie, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer, Villejuif, France
| | - Pedro T. Monteiro
- INESC-ID/Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- Lobachevsky University, Nizhni Novgorod, Russia
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Sarah Cohen-Boulakia
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
| | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Loïc Paulevé
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
| |
Collapse
|
20
|
Naldi A, Hernandez C, Abou-Jaoudé W, Monteiro PT, Chaouiya C, Thieffry D. Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0. Front Physiol 2018; 9:646. [PMID: 29971008 PMCID: PMC6018412 DOI: 10.3389/fphys.2018.00646] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 05/11/2018] [Indexed: 11/13/2022] Open
Abstract
The logical formalism is well adapted to model large cellular networks, in particular when detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step toward the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.
Collapse
Affiliation(s)
- Aurélien Naldi
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Céline Hernandez
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Wassim Abou-Jaoudé
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| | - Pedro T. Monteiro
- INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), École Normale Supérieure, Centre National de la Recherche Scientifique, Institut National de la Sante et de la Recherche Médicale, PSL Université, Paris, France
| |
Collapse
|