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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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2
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Selvaggio G, Cristellon S, Marchetti L. A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps. Front Mol Biosci 2022; 8:760077. [PMID: 34988115 PMCID: PMC8721169 DOI: 10.3389/fmolb.2021.760077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell-cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.
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Affiliation(s)
- Gianluca Selvaggio
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Serena Cristellon
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Luca Marchetti
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
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3
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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: 1.0] [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.
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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
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4
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Rosales GS, Darias NT. Introduction to Multiscale Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11472-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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5
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Chen L, Kulasiri D, Samarasinghe S. A Novel Data-Driven Boolean Model for Genetic Regulatory Networks. Front Physiol 2018; 9:1328. [PMID: 30319440 PMCID: PMC6167558 DOI: 10.3389/fphys.2018.01328] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 09/03/2018] [Indexed: 11/30/2022] Open
Abstract
A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.
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Affiliation(s)
- Leshi Chen
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Don Kulasiri
- Computational Systems Biology Laboratory, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
| | - Sandhya Samarasinghe
- Integrated Systems Modelling Group, Centre for Advanced Computational Solutions, Lincoln University, Lincoln, New Zealand
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6
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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: 7.7] [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.
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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
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7
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Ben Abdallah E, Folschette M, Roux O, Magnin M. ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks. Algorithms Mol Biol 2017; 12:20. [PMID: 28814968 PMCID: PMC5557630 DOI: 10.1186/s13015-017-0111-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 07/26/2017] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. RESULTS We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. CONCLUSION The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.
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Affiliation(s)
- Emna Ben Abdallah
- École Centrale de Nantes, LS2N UMR CNRS 6004, 1 rue de la Noë, 44321 Nantes, France
| | - Maxime Folschette
- Université de Nantes, LS2N UMR CNRS 6004, 1 rue de la Noë, 44321 Nantes, France
- Université Nice Sophia Antipolis, I3S UMR CNRS 7271, 2000, route des Lucioles, 06900 Nice, France
| | - Olivier Roux
- École Centrale de Nantes, LS2N UMR CNRS 6004, 1 rue de la Noë, 44321 Nantes, France
| | - Morgan Magnin
- École Centrale de Nantes, LS2N UMR CNRS 6004, 1 rue de la Noë, 44321 Nantes, France
- National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430 Japan
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8
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes. PLoS Comput Biol 2016; 12:e1005073. [PMID: 27599298 PMCID: PMC5012701 DOI: 10.1371/journal.pcbi.1005073] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/22/2016] [Indexed: 12/21/2022] Open
Abstract
Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.
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Abou-Jaoudé W, Traynard P, Monteiro PT, Saez-Rodriguez J, Helikar T, Thieffry D, Chaouiya C. Logical Modeling and Dynamical Analysis of Cellular Networks. Front Genet 2016; 7:94. [PMID: 27303434 PMCID: PMC4885885 DOI: 10.3389/fgene.2016.00094] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 05/12/2016] [Indexed: 12/28/2022] Open
Abstract
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
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Affiliation(s)
- Wassim Abou-Jaoudé
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
| | - Pauline Traynard
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
| | - Pedro T. Monteiro
- INESC-ID/Instituto Superior Técnico, University of LisbonLisbon, Portugal
- Instituto Gulbenkian de CiênciaOeiras, Portugal
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen UniversityAachen, Germany
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-LincolnLincoln, NE, USA
| | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research UniversityParis, France
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Nehaniv CL, Rhodes J, Egri-Nagy A, Dini P, Morris ER, Horváth G, Karimi F, Schreckling D, Schilstra MJ. Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0223. [PMID: 26078349 DOI: 10.1098/rsta.2014.0223] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/27/2015] [Indexed: 06/04/2023]
Abstract
Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.
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Affiliation(s)
- Chrystopher L Nehaniv
- Royal Society Wolfson Biocomputation Research Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - John Rhodes
- Department of Mathematics, University of California Berkeley, Berkeley, CA 94720, USA
| | - Attila Egri-Nagy
- Royal Society Wolfson Biocomputation Research Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK Centre for Research in Mathematics, University of Western Sydney, Locked Bag 1797, Penrith, New South Wales 2751, Australia
| | - Paolo Dini
- Royal Society Wolfson Biocomputation Research Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Eric Rothstein Morris
- Institute of IT Security and Security Law, University of Passau, Passau 94030, Germany
| | - Gábor Horváth
- Institute of Mathematics, University of Debrecen, Pf. 12. Debrecen, 4010 Hungary
| | - Fariba Karimi
- Royal Society Wolfson Biocomputation Research Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Daniel Schreckling
- Institute of IT Security and Security Law, University of Passau, Passau 94030, Germany
| | - Maria J Schilstra
- Royal Society Wolfson Biocomputation Research Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK
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12
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Mushthofa M, Torres G, Van de Peer Y, Marchal K, De Cock M. ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 2014; 30:3086-92. [PMID: 25028722 DOI: 10.1093/bioinformatics/btu481] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Boolean network models are suitable to simulate GRNs in the absence of detailed kinetic information. However, reducing the biological reality implies making assumptions on how genes interact (interaction rules) and how their state is updated during the simulation (update scheme). The exact choice of the assumptions largely determines the outcome of the simulations. In most cases, however, the biologically correct assumptions are unknown. An ideal simulation thus implies testing different rules and schemes to determine those that best capture an observed biological phenomenon. This is not trivial because most current methods to simulate Boolean network models of GRNs and to compute their attractors impose specific assumptions that cannot be easily altered, as they are built into the system. RESULTS To allow for a more flexible simulation framework, we developed ASP-G. We show the correctness of ASP-G in simulating Boolean network models and obtaining attractors under different assumptions by successfully recapitulating the detection of attractors of previously published studies. We also provide an example of how performing simulation of network models under different settings help determine the assumptions under which a certain conclusion holds. The main added value of ASP-G is in its modularity and declarativity, making it more flexible and less error-prone than traditional approaches. The declarative nature of ASP-G comes at the expense of being slower than the more dedicated systems but still achieves a good efficiency with respect to computational time. AVAILABILITY AND IMPLEMENTATION The source code of ASP-G is available at http://bioinformatics.intec.ugent.be/kmarchal/Supplementary_Information_Musthofa_2014/asp-g.zip.
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Affiliation(s)
- Mushthofa Mushthofa
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Gustavo Torres
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
| | - Yves Van de Peer
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Kathleen Marchal
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven
| | - Martine De Cock
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Department of Plant Systems Biology, VIB Technologiepark 927, Department of Plant Biotechnology and Bioinformatics, Ghent University Technologiepark 927, 9052 Ghent, Belgium, Genomics Research Institute (GRI), University of Pretoria, Private bag X20, Pretoria 0028, South Africa, Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark, Arenberg 20, 3001 Leuven, Belgium, Department of Information Technology, IMinds, Ghent University, Gaston Crommenlaan 8, B-9050 Ghent, Belgium and Center for Web and Data Science, Institute of Technology, University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA-98402, USA
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Ryll A, Bucher J, Bonin A, Bongard S, Gonçalves E, Saez-Rodriguez J, Niklas J, Klamt S. A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models. Biosystems 2014; 124:26-38. [PMID: 25063553 DOI: 10.1016/j.biosystems.2014.07.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 07/11/2014] [Accepted: 07/18/2014] [Indexed: 12/16/2022]
Abstract
Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.
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Affiliation(s)
- A Ryll
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, D-39106 Magdeburg, Germany.
| | - J Bucher
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - A Bonin
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - S Bongard
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - E Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, Cambridge, United Kingdom
| | - J Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, Cambridge, United Kingdom
| | - J Niklas
- Insilico Biotechnology AG, Meitnerstraße 8, D-70563 Stuttgart, Germany
| | - S Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, D-39106 Magdeburg, Germany.
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14
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Mbodj A, Junion G, Brun C, Furlong EEM, Thieffry D. Logical modelling of Drosophila signalling pathways. MOLECULAR BIOSYSTEMS 2014; 9:2248-58. [PMID: 23868318 DOI: 10.1039/c3mb70187e] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A limited number of signalling pathways are involved in the specification of cell fate during the development of all animals. Several of these pathways were originally identified in Drosophila. To clarify their roles, and possible cross-talk, we have built a logical model for the nine key signalling pathways recurrently used in metazoan development. In each case, we considered the associated ligands, receptors, signal transducers, modulators, and transcription factors reported in the literature. Implemented using the logical modelling software GINsim, the resulting models qualitatively recapitulate the main characteristics of each pathway, in wild type as well as in various mutant situations (e.g. loss-of-function or gain-of-function). These models constitute pluggable modules that can be used to assemble comprehensive models of complex developmental processes. Moreover, these models of Drosophila pathways could serve as scaffolds for more complicated models of orthologous mammalian pathways. Comprehensive model annotations and GINsim files are provided for each of the nine considered pathways.
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Affiliation(s)
- Abibatou Mbodj
- Technological Advances for Genomics and Clinics (TAGC), INSERM UMR_S 1090, Aix-Marseille Université, Marseille, France.
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15
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Chaouiya C, Bérenguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dräger A, Büchel F, Cokelaer T, Kowal B, Wicks B, Gonçalves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Novère N, Saez-Rodriguez J, Helikar T. SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC SYSTEMS BIOLOGY 2013; 7:135. [PMID: 24321545 PMCID: PMC3892043 DOI: 10.1186/1752-0509-7-135] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 11/26/2013] [Indexed: 05/03/2023]
Abstract
BACKGROUND Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.
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Affiliation(s)
- Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Duncan Bérenguier
- Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Aurélien Naldi
- Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Martijn P van Iersel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Andreas Dräger
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Finja Büchel
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Bryan Kowal
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Benjamin Wicks
- College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Emanuel Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Julien Dorier
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Michel Page
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
- IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, 38040 Grenoble Cedex 9, France
| | - Pedro T Monteiro
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Ioannis Xenarios
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Hidde de Jong
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
| | - Michael Hucka
- Computing and Mathematical sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Denis Thieffry
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS) - UMR CNRS 8197 - INSERM 1024 46 rue d’Ulm, 75230 Paris Cedex 05, France
| | - Nicolas Le Novère
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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16
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Mendes ND, Lang F, Le Cornec YS, Mateescu R, Batt G, Chaouiya C. Composition and abstraction of logical regulatory modules: application to multicellular systems. ACTA ACUST UNITED AC 2013; 29:749-57. [PMID: 23341501 DOI: 10.1093/bioinformatics/btt033] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
MOTIVATION Logical (Boolean or multi-valued) modelling is widely used to study regulatory or signalling networks. Even though these discrete models constitute a coarse, yet useful, abstraction of reality, the analysis of large networks faces a classical combinatorial problem. Here, we propose to take advantage of the intrinsic modularity of inter-cellular networks to set up a compositional procedure that enables a significant reduction of the dynamics, yet preserving the reachability of stable states. To that end, we rely on process algebras, a well-established computational technique for the specification and verification of interacting systems. RESULTS We develop a novel compositional approach to support the logical modelling of interconnected cellular networks. First, we formalize the concept of logical regulatory modules and their composition. Then, we make this framework operational by transposing the composition of logical modules into a process algebra framework. Importantly, the combination of incremental composition, abstraction and minimization using an appropriate equivalence relation (here the safety equivalence) yields huge reductions of the dynamics. We illustrate the potential of this approach with two case-studies: the Segment-Polarity and the Delta-Notch modules.
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Affiliation(s)
- Nuno D Mendes
- IGC, Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, P-2780-156 Oeiras, Portugal
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17
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Gonçalves E, Bucher J, Ryll A, Niklas J, Mauch K, Klamt S, Rocha M, Saez-Rodriguez J. Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models. MOLECULAR BIOSYSTEMS 2013; 9:1576-83. [DOI: 10.1039/c3mb25489e] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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18
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Aguilar-Hidalgo D, Domínguez-Cejudo MA, Amore G, Brockmann A, Lemos MC, Córdoba A, Casares F. A Hh-driven gene network controls specification, pattern and size of the Drosophila simple eyes. Development 2012; 140:82-92. [PMID: 23154412 DOI: 10.1242/dev.082172] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
During development, extracellular signaling molecules interact with intracellular gene networks to control the specification, pattern and size of organs. One such signaling molecule is Hedgehog (Hh). Hh is known to act as a morphogen, instructing different fates depending on the distance to its source. However, how Hh, when signaling across a cell field, impacts organ-specific transcriptional networks is still poorly understood. Here, we investigate this issue during the development of the Drosophila ocellar complex. The development of this sensory structure, which is composed of three simple eyes (or ocelli) located at the vertices of a triangular patch of cuticle on the dorsal head, depends on Hh signaling and on the definition of three domains: two areas of eya and so expression--the prospective anterior and posterior ocelli--and the intervening interocellar domain. Our results highlight the role of the homeodomain transcription factor engrailed (en) both as a target and as a transcriptional repressor of hh signaling in the prospective interocellar region. Furthermore, we identify a requirement for the Notch pathway in the establishment of en maintenance in a Hh-independent manner. Therefore, hh signals transiently during the specification of the interocellar domain, with en being required here for hh signaling attenuation. Computational analysis further suggests that this network design confers robustness to signaling noise and constrains phenotypic variation. In summary, using genetics and modeling we have expanded the ocellar gene network to explain how the interaction between the Hh gradient and this gene network results in the generation of stable mutually exclusive gene expression domains. In addition, we discuss some general implications our model may have in some Hh-driven gene networks.
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19
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Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, van Iersel M, Lauffenburger DA, Saez-Rodriguez J. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC SYSTEMS BIOLOGY 2012; 6:133. [PMID: 23079107 PMCID: PMC3605281 DOI: 10.1186/1752-0509-6-133] [Citation(s) in RCA: 121] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Accepted: 09/19/2012] [Indexed: 12/17/2022]
Abstract
Background Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Results Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Conclusions Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
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Affiliation(s)
- Camille Terfve
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
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20
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MacNamara A, Terfve C, Henriques D, Bernabé BP, Saez-Rodriguez J. State-time spectrum of signal transduction logic models. Phys Biol 2012; 9:045003. [PMID: 22871648 DOI: 10.1088/1478-3975/9/4/045003] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.
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Affiliation(s)
- Aidan MacNamara
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
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21
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Abstract
Discrete mathematical formalisms are well adapted to model large biological networks, for which detailed kinetic data are scarce. This chapter introduces the reader to a well-established qualitative (logical) framework for the modelling of regulatory networks. Relying on GINsim, a software implementing this logical formalism, we guide the reader step by step towards the definition and the analysis of a simple model of the lysis-lysogeny decision in the bacteriophage λ.
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22
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Azpeitia E, Benítez M, Vega I, Villarreal C, Alvarez-Buylla ER. Single-cell and coupled GRN models of cell patterning in the Arabidopsis thaliana root stem cell niche. BMC SYSTEMS BIOLOGY 2010; 4:134. [PMID: 20920363 PMCID: PMC2972269 DOI: 10.1186/1752-0509-4-134] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2010] [Accepted: 10/05/2010] [Indexed: 12/15/2022]
Abstract
BACKGROUND Recent experimental work has uncovered some of the genetic components required to maintain the Arabidopsis thaliana root stem cell niche (SCN) and its structure. Two main pathways are involved. One pathway depends on the genes SHORTROOT and SCARECROW and the other depends on the PLETHORA genes, which have been proposed to constitute the auxin readouts. Recent evidence suggests that a regulatory circuit, composed of WOX5 and CLE40, also contributes to the SCN maintenance. Yet, we still do not understand how the niche is dynamically maintained and patterned or if the uncovered molecular components are sufficient to recover the observed gene expression configurations that characterize the cell types within the root SCN. Mathematical and computational tools have proven useful in understanding the dynamics of cell differentiation. Hence, to further explore root SCN patterning, we integrated available experimental data into dynamic Gene Regulatory Network (GRN) models and addressed if these are sufficient to attain observed gene expression configurations in the root SCN in a robust and autonomous manner. RESULTS We found that an SCN GRN model based only on experimental data did not reproduce the configurations observed within the root SCN. We developed several alternative GRN models that recover these expected stable gene configurations. Such models incorporate a few additional components and interactions in addition to those that have been uncovered. The recovered configurations are stable to perturbations, and the models are able to recover the observed gene expression profiles of almost all the mutants described so far. However, the robustness of the postulated GRNs is not as high as that of other previously studied networks. CONCLUSIONS These models are the first published approximations for a dynamic mechanism of the A. thaliana root SCN cellular pattering. Our model is useful to formally show that the data now available are not sufficient to fully reproduce root SCN organization and genetic profiles. We then highlight some experimental holes that remain to be studied and postulate some novel gene interactions. Finally, we suggest the existence of a generic dynamical motif that can be involved in both plant and animal SCN maintenance.
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Affiliation(s)
- Eugenio Azpeitia
- Instituto de Ecología & Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, México DF, México
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23
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Robustness of positional specification by the Hedgehog morphogen gradient. Dev Biol 2010; 342:180-93. [PMID: 20363217 DOI: 10.1016/j.ydbio.2010.03.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2009] [Revised: 02/25/2010] [Accepted: 03/26/2010] [Indexed: 11/20/2022]
Abstract
Spatial gradients of Hedgehog signalling play a central role in many patterning events during animal development, regulating cell fate determination and tissue growth in a variety of tissues and developmental stages. Experimental evidence suggests that many of the proteins responsible for regulating Hedgehog signalling and transport are themselves targets of Hedgehog signalling, leading to multiple levels of feedback within the system. We use mathematical modelling to analyse how these overlapping feedbacks combine to regulate patterning and potentially enhance robustness in the Drosophila wing imaginal disc. Our results predict that the regulation of Hedgehog transport and stability by glypicans, as well as multiple overlapping feedbacks in the Hedgehog response network, can combine to enhance the robustness of positional specification against variability in Hedgehog levels. We also discuss potential trade-offs between robustness and additional features of the Hedgehog gradient, such as signalling range and size regulation.
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24
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Nahmad M, Stathopoulos A. Dynamic interpretation of hedgehog signaling in the Drosophila wing disc. PLoS Biol 2009; 7:e1000202. [PMID: 19787036 PMCID: PMC2744877 DOI: 10.1371/journal.pbio.1000202] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2009] [Accepted: 08/13/2009] [Indexed: 11/19/2022] Open
Abstract
Morphogens are classically defined as molecules that control patterning by acting at a distance to regulate gene expression in a concentration-dependent manner. In the Drosophila wing imaginal disc, secreted Hedgehog (Hh) forms an extracellular gradient that organizes patterning along the anterior-posterior axis and specifies at least three different domains of gene expression. Although the prevailing view is that Hh functions in the Drosophila wing disc as a classical morphogen, a direct correspondence between the borders of these patterns and Hh concentration thresholds has not been demonstrated. Here, we provide evidence that the interpretation of Hh signaling depends on the history of exposure to Hh and propose that a single concentration threshold is sufficient to support multiple outputs. Using mathematical modeling, we predict that at steady state, only two domains can be defined in response to Hh, suggesting that the boundaries of two or more gene expression patterns cannot be specified by a static Hh gradient. Computer simulations suggest that a spatial "overshoot" of the Hh gradient occurs, i.e., a transient state in which the Hh profile is expanded compared to the Hh steady-state gradient. Through a temporal examination of Hh target gene expression, we observe that the patterns initially expand anteriorly and then refine, providing in vivo evidence for the overshoot. The Hh gene network architecture suggests this overshoot results from the Hh-dependent up-regulation of the receptor, Patched (Ptc). In fact, when the network structure was altered such that the ptc gene is no longer up-regulated in response to Hh-signaling activation, we found that the patterns of gene expression, which have distinct borders in wild-type discs, now overlap. Our results support a model in which Hh gradient dynamics, resulting from Ptc up-regulation, play an instructional role in the establishment of patterns of gene expression.
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Affiliation(s)
- Marcos Nahmad
- Department of Control and Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America
- Division of Biology, California Institute of Technology, Pasadena, California, United States of America
| | - Angelike Stathopoulos
- Division of Biology, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
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Naldi A, Berenguier D, Fauré A, Lopez F, Thieffry D, Chaouiya C. Logical modelling of regulatory networks with GINsim 2.3. Biosystems 2009; 97:134-9. [PMID: 19426782 DOI: 10.1016/j.biosystems.2009.04.008] [Citation(s) in RCA: 109] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2009] [Revised: 04/22/2009] [Accepted: 04/25/2009] [Indexed: 01/30/2023]
Abstract
Many important problems in cell biology require the consideration of dense nonlinear interactions between functional modules. The requirement of computer simulation for the understanding of cellular processes is now widely accepted, and a variety of modelling frameworks have been designed to meet this need. Here, we present a novel public release of the Gene Interaction Network simulation suite (GINsim), a software designed for the qualitative modelling and analysis of regulatory networks. The main functionalities of GINsim are illustrated through the analysis of a logical model for the core network controlling the fission yeast cell cycle. The last public release of GINsim (version 2.3), as well as development versions, can be downloaded from the dedicated website (http://gin.univ-mrs.fr/GINsim/), which further includes a model library, along with detailed tutorial and user manual.
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Affiliation(s)
- A Naldi
- TAGC-INSERM U928-Université de la Méditerranée, Campus de Luminy, Case 929, F-13288 Marseille Cedex 9, France
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A Reduction of Logical Regulatory Graphs Preserving Essential Dynamical Properties. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2009. [DOI: 10.1007/978-3-642-03845-7_18] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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