Systems Biology Modelling and Analysis: Formal Bioinformatics Methods and Tools

ISBN-13: 9781119716532 / Angielski / Twarda / 2022 / 400 str.

de Maria, Elisabetta

Spis treści

List of Contributors xvPreface xixAcknowledgments xxv1 Introduction 1Elisabetta De Maria1.1 Why Writing Models 21.2 Modelling and Validating Biological Systems: Three Steps 41.2.1 Modelling Biological Systems 41.2.2 Specifying Biological Systems 71.2.3 Validating Biological Systems 8References 92 Petri Nets for Systems Biology Modelling and Analysis 15Fei Liu, Hiroshi Matsuno, and Monika Heiner2.1 Introduction 152.2 A Running Example 162.3 Petri Nets 162.3.1 Modelling 172.3.2 Analysis 182.3.3 Applications 202.4 Extended Petri Nets 202.5 Stochastic Petri Nets 202.5.1 Modelling 212.5.2 Stochastic Simulation 212.5.3 CSL Model Checking 222.5.4 Applications 232.6 Continuous Petri Nets 242.6.1 Modelling 242.6.2 Deterministic Simulation 242.6.3 Simulative Model Checking 252.6.4 Applications 272.7 Fuzzy Stochastic Petri Nets 272.7.1 Modelling 272.7.2 Fuzzy Stochastic Simulation 272.7.3 Applications 292.8 Fuzzy Continuous Petri Nets 292.8.1 Modelling 292.8.2 Fuzzy Deterministic Simulation 292.8.3 Applications 302.9 Conclusions 30Acknowledgment 31References 313 Process Algebras in Systems Biology 35Paolo Milazzo3.1 Introduction 353.2 Process Algebras in Concurrency Theory 363.2.1 pi-Calculus 383.3 Analogies between Biology and Concurrent Systems 423.3.1 Elements of Cell Biology 433.3.2 Cell Pathways 443.3.3 "Molecules as Processes" Abstraction 483.4 Process Algebras for Qualitative Modelling 513.4.1 Formal Analysis Techniques 513.5 Process Algebras for Quantitative Modelling 533.5.1 Chemical Kinetics 543.5.2 Stochastic Process Algebras 593.6 Conclusions 61Acknowledgments 61References 624 The Rule-Based Model Approach: A Kappa Model for Hepatic Stellate Cells Activation by TGFB1 69Matthieu Bouguéon, Pierre Boutillier, Jérôme Feret, Octave Hazard, and Nathalie Théret4.1 Introduction 694.1.1 Modelling Systems of Biochemical Interactions 694.1.2 Modelling Languages 704.1.3 Kappa 714.1.3.1 Overview 714.1.3.2 Semantics of Kappa 724.1.3.3 Kappa Ecosystem 734.1.3.4 Main Limitations 754.1.4 Modelling a Population of Hepatic Stellate Cells 764.1.5 Outline 784.2 Kappa 784.2.1 Site Graphs 784.2.1.1 Signature 794.2.1.2 Complexes 814.2.1.3 Patterns 824.2.1.4 Embeddings Between Patterns 844.2.2 Site Graph Rewriting 864.2.2.1 Interaction Rules 864.2.2.2 Reactions Induced by an Interaction Rule 874.2.2.3 Underlying Reaction Network 884.3 Model of Activation of Stellate Cells 914.3.1 Overview of Model 914.3.2 Some Elements of Biochemistry 914.3.2.1 Reaction Half-Time 924.3.2.2 Conversion 934.3.2.3 Production Equilibrium 934.3.2.4 Erlang Distributions 944.3.3 Interaction Rules 944.3.3.1 Behavior of TGFB1 Proteins 954.3.3.2 Renewal of Quiescent HSCs 964.3.3.3 Activation and Differentiation 974.3.3.4 Proliferation of Activated Hepatic Stellate Cells 994.3.3.5 Proliferation of Myofibroblasts 1004.3.3.6 Apoptosis and Senescence of Myofibroblasts 1014.3.3.7 Inactivation of Myofibroblasts 1024.3.3.8 Behavior of Inactivated Hepatic Stellate Cells 1024.3.3.9 Proliferation of Reactivated Cells 1054.3.3.10 Degradation of Reactivated MFB 1064.3.3.11 Behavior of Receptors 1064.3.4 Parameters 1084.4 Results 1094.4.1 Static Analysis 1094.4.2 Underlying Reaction Network 1114.4.3 Simulations 1114.5 Conclusion 113References 1165 Pathway Logic: Curation and Analysis of Experiment-Based Signaling Response Networks 127Merrill Knapp, Keith Laderoute, and Carolyn Talcott5.1 Introduction 1275.2 Pathway Logic Overview 1305.3 PL Representation System 1335.3.1 Rewriting Logic and Maude 1335.3.2 Pathway Logic Language 1345.3.3 Petri Net Representation 1405.3.4 Computing with Petri Nets 1425.4 Pathway Logic Assistant 1445.5 Datum Curation and Model Development 1505.5.1 Datum Curation 1505.5.2 Model Development - Inferring Rules 1535.6 STM8 1555.6.1 LPS Response Network 1565.6.2 Combining Network Analyses 1585.6.3 Death Map: A Review Model 1595.6.3.1 Review Map as a Summary of the State of the Art 1635.7 Conclusion 163Acknowledgments 164Appendix 5.A: Summary of STM8 Networks 164References 1686 Boolean Networks and Their Dynamics: The Impact of Updates 173Loïc Paulevé and Sylvain Sené6.1 Introduction 1736.1.1 General Notations and Definitions 1786.2 Boolean Network Framework 1796.2.1 On the Simplicity of Boolean Networks 1796.2.2 Boolean Network Specification 1816.2.3 Boolean Network Dynamics 1836.2.3.1 Updates 1836.2.3.2 Transitions and Trajectories 1856.2.3.3 Updating Mode and Transition Graph 1866.2.3.4 Deterministic Updating Modes 1876.2.3.5 Non-deterministic Updating Modes 1996.3 Biological Case Studies 2086.3.1 Floral Morphogenesis of A. thaliana 2096.3.2 Cell Cycle 2116.3.3 Vegetal and Animal Zeitgebers 2126.3.4 Abstraction of Quantitative Models 2146.4 Fundamental Knowledge 2166.4.1 Structural Properties and Attractors 2166.4.1.1 Fixed Points Stability 2166.4.1.2 Feedback Cycles as Engines of Dynamical Complexity 2176.4.1.3 About Signed Feedback Cycles 2196.4.2 Computational Complexity 2246.4.2.1 Existence of a Fixed Point 2256.4.2.2 Reachability Between Configurations 2276.4.2.3 Limit Configurations 2296.5 Conclusion 2326.5.1 Updating Modes and Time 2326.5.1.1 Modelling Durations 2336.5.1.2 Modelling Precedence 2346.5.1.3 Modelling Causality 2346.5.2 Toward an Updating Mode Hierarchy 2356.5.2.1 Software Tools 2356.5.3 Opening on Intrinsic Simulations 236Acknowledgments 238References 2387 Analyzing Long-Term Dynamics of Biological Networks With Answer Set Programming 251Emna Ben Abdallah, Maxime Folschette, and Morgan Magnin7.1 Introduction 2517.2 State of the Art 2537.2.1 Qualitative Modelling of Biological Systems 2537.2.2 Identifying Attractors: A Major Challenge 2557.2.3 Answer Set Programming for Systems Biology 2577.2.4 Enumerating Attractors of a Biological Model Using Answer Set Programming 2587.3 Basic Notions of Answer Set Programming 2597.3.1 Syntax and Rules 2597.3.2 Predicates 2617.3.3 Scripting 2637.4 Dynamic Modelling Using Asynchronous Automata Networks 2647.4.1 Motivation: Using ASP to Analyze the Dynamics 2647.4.2 Definition of Asynchronous Automata Networks 2647.4.3 Semantics and Dynamics of Asynchronous Automata Networks 2677.4.4 Stable States and Attractors in Asynchronous Automata Networks 2717.5 Encoding into Answer Set Programming 2757.5.1 Translating Asynchronous Automata Networks into Answer Set Programs 2767.5.2 Stable-State Enumeration 2787.5.3 Attractors 2807.5.3.1 Cycle Enumeration 2817.5.3.2 Attractor Enumeration 2857.5.3.3 Python Scripting 2887.6 Case Studies 2907.6.1 Toy Example 2907.6.2 Bacteriophage Lambda 2927.6.3 Benchmarks on Models Coming from the Literature 2937.7 Conclusion 297Acknowledgments 299References 2998 Hybrid Automata in Systems Biology 305Alberto Casagrande, Raffaella Gentilini, Carla Piazza, and Alberto Policriti8.1 Introduction 3058.2 Basics 3078.2.1 Languages and Theories 3088.3 Events 3138.3.1 Temporal Logics 3168.3.2 Model Checking 3188.4 Events and Time 3188.4.1 Hybrid Automata and Gene Regulatory Networks 3198.4.2 Expressibility and Decidability Issues 3238.5 Events, Time, and Uncertainty 3278.6 Conclusions 331Acknowledgement 332References 3329 Kalle Parvinen: Ordinary Differential Equations 339Kalle Parvinen9.1 Introduction 3399.2 Analyzing and Solving Ordinary Differential Equations 3409.2.1 Solving Ordinary Differential Equations Analytically 3409.2.2 Equilibria and Their Stability 3419.2.3 Solving Differential Equations Numerically 3449.3 Mechanistic Derivation of Ordinary Differential Equations 3459.3.1 Elementary Unimolecular Reaction (EUR) 3469.3.2 Elementary Bimolecular Reaction (EBR) 3479.3.3 Elementary Bimolecular Reaction of Two Identical Molecules 3489.3.4 Reaction Networks 3489.4 Classical Lotka-Volterra Differential Equation 3509.4.1 Model Formation and History 3509.4.2 Phase-Plane Analysis and Equilibria 3519.4.3 Constant of Motion 3529.4.4 Average Population Densities 3539.4.5 Effect of Fishing on the Population Densities 3539.5 Model of Killer T-Cell and Cancer Cell Dynamics 3549.5.1 Model Definition 3549.5.1.1 Resource Dynamics 3549.5.1.2 Cancer Cell Dynamics 3559.5.1.3 Killer T-Cell Dynamics 3569.5.2 Model DynamicsWithout Treatment 3579.5.3 Treatment Effects 3589.6 Conclusion 359Acknowledgments 359References 36010 Network Modelling Methods for Precision Medicine 363Elio Nushi, Victor-Bogdan Popescu, Jose-Angel Sanchez Martin, Sergiu Ivanov, Eugen Czeizler, and Ion Petre10.1 Introduction 36310.2 Network Modelling Methods 36410.2.1 Network Centrality Methods 36410.2.1.1 Running Example 36610.2.1.2 Degree Centralities 36610.2.1.3 Proximity Centralities 36810.2.1.4 Path Centrality: Betweenness 37310.2.1.5 Spectral Centralities 37710.2.2 System Controllability Methods 38310.2.2.1 Network Controllability 38410.2.2.2 Minimum Dominating Sets 38710.2.3 Software 38810.2.3.1 NetworkX 38910.2.3.2 Cytoscape 39010.2.3.3 NetControl4BioMed 39010.3 Applications of Network Modelling in Personalized Medicine 39210.3.1 Constructing Personalized Disease Networks 39210.3.2 Analysis Methods 39310.3.3 Results 39810.3.3.1 Structural Controllability Analysis 39810.3.3.2 Minimum Dominating Set Analysis 40610.4 Conclusion 412References 41311 Conclusion 425Elisabetta De MariaIndex 427

O autorze

Elisabetta De Maria, PhD, is an Associate Professor at the Université Côte d'Azur. From 2011-13, she was Coordinator of the International Research Master Program "Computational Biology and Biomedicine" at the University of Nice-Sophia Antipolis. Dr. De Maria has expertise in bioinformatics and computational systems biology and served as program chair of the conferences BIOINFORMATICS 2019, CSBio 2019 (International Conference on Computational Systems-Biology and Bioinformatics), and BIOINFORMATICS 2020.