Computational methods in systems biology : 19th international conference, CMSB 2021, Bordeaux, France, September 22-24, 2021 : proceedings

This book constitutes the refereed proceedings of the 19th International Conference on Computational Methods in Systems Biology, CMSB 2021, held in Bordeaux, France, September 22–24, 2021.* The 13 full papers and 5 tool papers were carefully reviewed and selected from 32 submissions. The topics of interest include biological process modelling; biological system model verification, validation, analysis, and simulation; high-performance computational systems biology; model inference from experimental data; multi-scale modeling and analysis methods; computational approaches for synthetic biology; machine learning and data-driven approaches; microbial ecology modelling and analysis; methods and protocols for populations and their variability; models, applications, and case studies in systems and synthetic biology. The chapters "Microbial Community Decision Making Models in Batch", "Population design for synthetic gene circuits", "BioFVM-X: An MPI+OpenMP 3-D Simulator for Biological Systems" are published open access under a CC BY license (Creative Commons Attribution 4.0 International License). * The conference was held in a hybrid mode due to the COVID-19 pandemic. Preface Organization Contents Reducing Boolean Networks with Backward Boolean Equivalence 1 Introduction 2 Preliminaries 3 Backward Boolean Equivalence 3.1 Backward Boolean Equivalence and BN Reduction 3.2 Computation of the Maximal BBE 3.3 Relating Dynamics of Original and Reduced BNs 4 Application to BNs from the Literature 4.1 Large Scale Validation of BBE on BNs 4.2 Attractor Analysis of Selected Case Studies 4.3 Comparison with ODE-Based Approach From ch1cardelli2017maximal 5 Related Work 6 Conclusion References Abstraction of Markov Population Dynamics via Generative Adversarial Nets 1 Introduction 2 Background 2.1 Chemical Reaction Networks 2.2 Generative Adversarial Nets 3 GAN-Based Abstraction 3.1 Model Abstraction 3.2 Dataset Generation 3.3 cWCGAN-GP Architecture 3.4 Model Training 4 Experimental Results 4.1 cWCGAN-GP Architecture 4.2 Results 4.3 Discussion 5 Conclusions References Greening R. Thomas' Framework with Environment Variables: A Divide and Conquer Approach 1 Introduction 2 Adding Environment Variables to Thomas' Framework 2.1 Regulatory Network with Multiplexes 2.2 Formulas of Biological Properties and Their Models 2.3 Environmental Regulatory Networks 3 All Environments' Coexistence in Thomas' Framework 3.1 Regulatory Network 3.2 Formula Summing Up all Behavioural Properties 3.3 Application to Pseudomonas æriginosa 4 Divide with Environments, Combine with Intersection 4.1 Regulatory Networks with Environments 4.2 Formulas and Abstraction of Models 4.3 Application to Pseudomonas æriginosa 5 Comparing the Two Approaches 5.1 Theoretical Point of View 5.2 Practical Results 6 Case Study: Cell Metabolism 6.1 Metabolism Regulations According to Environments 6.2 All Environments Coexistence in Thomas' Framework 6.3 Divide with Environments, Combine with Intersection 7 Conclusion References Automated Inference of Production Rules for Glycans 1 Introduction 2 Production of Glycans 3 Motivating Example 4 Modelling of the Synthesis Problem 5 Method for the Synthesis Problem 5.1 SugarSynth in Detail 5.2 EncodeProduce in Detail 6 Experiments 7 Conclusion and Future Work References Compiling Elementary Mathematical Functions into Finite Chemical Reaction Networks via a Polynomialization Algorithm for ODEs 1 Introduction 2 Input Language of Elementary Functions 2.1 Example 2.2 Elementary Functions as Compilation Pipeline Input Language 3 Polynomialization Algorithm for Elementary ODEs 3.1 Polynomialization Algorithm 3.2 Interval of Definition 3.3 Termination 3.4 Complexity 3.5 Remark on the Compilation of the Exponentiation 4 CRN Compilation Pipeline for Elementary Functions 4.1 Detailed Example 4.2 Implementation 5 Evaluation 6 Conclusion and Perspectives References Interpretable Exact Linear Reductions via Positivity 1 Introduction 2 Methods 2.1 Preliminaries on Lumping 2.2 The Nonuniqueness/Interpretability Issue 2.3 Our Approach via Nonnegativity 2.4 Algorithmic Details 3 Case Studies 3.1 Multisite Protein Phosphorylation 3.2 Fc-RI Signaling Pathways 3.3 Jak-Family Protein Tyrosine Kinase Activation 4 Conclusion References Explainable Artificial Neural Network for Recurrent Venous Thromboembolism Based on Plasma Proteomics 1 Introduction 2 Materials and Methods 2.1 MARTHA Study 2.2 Proposed Workflow 3 Results 3.1 MARTHA Study 3.2 Constructing and Validation of the ANN 3.3 Post-hoc Explainability of ANN 4 Conclusion References Neural Networks to Predict Survival from RNA-seq Data in Oncology 1 Introduction 2 Models 2.1 The Cox Model 2.2 Neural Networks 3 Simulations 3.1 Generation of Survival Times 3.2 Simulation with the Cox - Weibull Model 3.3 Simulation with the AH - Log-Normal Model 3.4 Metrics 4 Results 4.1 Simulation Study 4.2 Application on Real Datasets 5 Discussions A Appendix: Supplementary Results A.1 Simulation from the AFT - Log-Normal Model A.2 Simulation Study References Microbial Community Decision Making Models in Batch and Chemostat Cultures 1 Introduction 2 Concepts 2.1 Chemostat vs Batch Environment 2.2 Implications for Coexistence 2.3 Implications for Decision Making 3 Community Models 3.1 General Consortium Models 3.2 Rational Agents 3.3 Rational Community 4 Applications 4.1 Prisoners Dilemma 4.2 Coexistence Microbial Consortium 5 Discussion References Learning Boolean Controls in Regulated Metabolic Networks: A Case-Study 1 Introduction 2 Background: Regulated Metabolic Networks 2.1 Coupling Metabolic and Regulatory Networks 2.2 Dynamic rFBA 3 Boolean Abstraction of Dynamic rFBA 3.1 Boolean Metabolic Steady States 3.2 Boolean Dynamics 4 Inference of Regulations from rFBA Time Series 4.1 Approximation as a Boolean Satisfiability Problem 4.2 Implementation in Answer-Set Programming 5 Case Study 6 Discussion A Binarized Metabolic Steady State B Experiments and Simulations References Population Design for Synthetic Gene Circuits 1 Introduction 2 Population Design Framework 3 Case Study: Design of a Transcriptional Controller 3.1 Overview 3.2 Individual Model 3.3 Population Model 3.4 Design Problem 3.5 Sampling the Individual Parameters 3.6 Sampling the Population Parameters 4 Discussion 5 Conclusion References Nonlinear Pattern Matching in Rule-Based Modeling Languages 1 Introduction 2 Rule-Based Modeling 3 Nonlinear Patterns in the Wild 4 Linear Pattern Matching 4.1 Abstract Syntax 4.2 Pattern Matching Semantics 4.3 Algorithm 5 Nonlinear Pattern Matching with Expressions 5.1 Abstract Syntax 5.2 Pattern Matching Semantics 6 Benchmarks 7 Discussion and Conclusion References Protein Noise and Distribution in a Two-Stage Gene-Expression Model Extended by an mRNA Inactivation Loop 1 Introduction 2 Model Formulation 3 Factorial Cumulant Generating Function 4 Protein Variability 5 Special-Function Representation 6 Marginal Distributions 7 Conclusion References Aeon 2021: Bifurcation Decision Trees in Boolean Networks 1 Introduction 2 Methods 3 Case Study 4 Conclusion References LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales 1 Introduction 2 Model 3 Reduction Algorithm 4 Applications 4.1 Connection Between Topology and Dynamics 4.2 Design of Slow Transients 5 Conclusion References Ppsim: A Software Package for Efficiently Simulating and Visualizing Population Protocols 1 Introduction 2 Usage of the Ppsim Tool 3 Speed Comparison with Other CRN Simulators 4 Issues with Other Speedup Methods 5 Conclusion References Web-Based Structural Identifiability Analyzer 1 Introduction and Related Work 2 Input-Output Specification 3 Use Cases for Structural Identifiability Toolbox 3.1 Globally Identifiable Example (Two-Species Competition Model) 3.2 Locally Identifiable Model (SIRS Model with Forcing) 3.3 Identifiable Combination of Non-identifiable Parameters (Tumor Targeting) 3.4 System with a Non-identifiable Parameter (Lotka-Volterra Model) 3.5 Refining Multi-experiment Identifiability Bound (Slow-Fast Ambiguity in a Chemical Reaction Network) A Details on the Underlying Algorithms B Systems in Structural Identifiability Toolbox Input Form B.1 Example from Sect.3.2 B.2 Example from Sect.3.3 B.3 Example from Sect.3.4 B.4 Example from Sect.3.5 B.5 Example of Speedup with Bypasses References BioFVM-X: An MPI+OpenMP 3-D Simulator for Biological Systems 1 Introduction 2 Related Work 3 Internal Design and Domain Partitioning 4 Experiments 5 Conclusion and Future Work A 1-D Pure x-Domain Decomposition B Mapping Basic Agents to a Voxel C Extended Results D Correctness Checking References Author Index