Symbolic Approaches to Modeling and Analysis of Biological Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: ISTE Consignment
• Sprache: Englisch

Seitenzahl: 757

Veröffentlichungsjahr: 2023

Beschreibung

Systems Biology is an approach to biology that involves understanding the complexity of interactions among biological entities within a systemic whole. The goal is to understand the emergence of physiological or functional properties. Symbolic Approaches to Modeling and Analysis of Biological Systems presents contributions of formal methods from computer science for modeling the dynamics of biological systems. It deals more specifically with symbolic methods, i.e. methods that can establish the qualitative properties of models. This book presents different approaches related to semantics, language, modeling and their link with data, and allows us to examine the fundamental problems and challenges that biological systems are facing. The first part of the book presents works that rely on various available data to build models, while the second part gathers contributions surrounding issues of semantics and formal methods.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

Preface

Part 1. Models and Data

Chapter 1. Inference of Gene Regulatory Networks from Multi-scale Dynamic Data

1.1. GRN and differentiation

1.2. Inference of GRN from population data

1.3. Inferring GRNs from single-cell data

1.4. Alternative strategies for GRN inference

1.5. Performance and limitations of GRN inference

1.6. Inference based on the wave of expression concept

1.7. Conclusion

1.8. References

Chapter 2. Combinatorial Optimization Problems for Studying Metabolism

2.1. Dynamics and functionality of a metabolic network

2.2. Understanding the metabolism of non-model organisms: metabolic gap-filling algorithms

2.3. Microbiota metabolism: new optimization problems

2.4. Discrete semantics: a Boolean approximation of metabolic producibility

2.5. Flux semantics

2.6. Comparing semantics: toward a hybrid approach

2.7. Solving gap-filling problems with answer set programming

2.8. Conclusion

2.9. References

Chapter 3. The Challenges of Inferring Dynamic Models from Time Series

3.1. Challenges of learning about time series

3.2. Reconstruction of a regulation network (Boolean network) and its logical rules

3.3. Modeling Thomas networks with delays in ASP

3.4. Promise of machine learning for biology

3.5. References

Chapter 4. Connecting Logical Models to Omics Data

4.1. Introduction

4.2. Logical models: objectives, nature and tools

4.3. Building an influence graph using biological data

4.4. Defining logical rules and refining model parameters using biological data

4.5. Data to validate models and predict behaviors

4.6. Conclusion

4.7. References

Part 2. Formal and Semantic Methods

Chapter 5. Boolean Networks: Formalism, Semantics and Complexity

5.1. Introduction

5.2. Classical semantics of Boolean networks

5.3. Related formalisms

5.4. Guarantees against quantitative models

5.5. Dynamic properties and complexities

5.6. Conclusion

5.7. Acknowledgments

5.8. References

Chapter 6. Computational Logic for Biomedicine and Neurosciences

6.1. Introduction

6.2. Biomedicine in linear logic

6.3. On the use of Coq to model and verify neuronal archetypes

6.4. Conclusion and perspective

6.5. References

Chapter 7. The Cell: A Chemical Analog Calculator

7.1. Introduction

7.2. Chemical reaction networks

7.3. Discrete dynamics and digital calculation

7.4. Continuous dynamics and analog computation

7.5. Turing-completeness of continuous CRNs

7.6. Chemical compiler of calculable functions

7.7. Chemical programming of non-living vesicles

7.8. 10

14

networked analog computers

7.9. References

Chapter 8. Formal Verification Methods for Modeling in Biology: Biological Regulation Networks

8.1. Introduction

8.2. Formalization of René Thomas’s modeling

8.3. Genetically modified Hoare logic

8.4. Temporal logic and CTL

8.5. TotemBioNet

8.6. Hybrid formalism

8.7. Hybrid Hoare logic

8.8. General methodology

8.9. Acknowledgments

8.10. References

Chapter 9. Accessible Pattern Analyses in Kappa Models

9.1. Introduction

9.2. Site graphs

9.3. Rewriting site graphs

9.4. Analysis of reachable patterns

9.5. Analysis using sets of orthogonal patterns

9.6. Conclusion

9.7. References

List of Authors

Index

End User License Agreement

List of Tables

Chapter 3

Table 3.1 Execution time of GULA and PRIDE in seconds for Boolean network case studies with up to 15 variables for synchronous semantics

Chapter 9

Table 9.1 Experimental results (calculated on a MacBook Pro with an Intel Core i7-6567U chip (clocked 3.3 GHz))

Guide

Cover

Table of Contents

Title Page

Copyright Page

Preface

Begin Reading

List of Authors

Index

End User License Agreement

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SCIENCES

Computer Science,

Field Directors – Valérie Berthé and Jean-Charles Pomerol

Bioinformatics,

Subject Heads – Anne Siegel and Hélène Touzet

Symbolic Approaches to Modeling and Analysis of Biological Systems

Coordinated by

Cédric Lhoussaine

Élisabeth Remy

First published 2023 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the under mentioned address:

ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK

www.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA

www.wiley.com

© ISTE Ltd 2023The rights of Cédric Lhoussaine and Élisabeth Remy to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.

Library of Congress Control Number: 2022947892

British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78945-029-3

ERC code:LS2 Genetics, ’Omics’, Bioinformatics and Systems BiologyLS2_12 BioinformaticsLS2_15 Systems biology

Preface

Cédric LHOUSSAINE1,2 and Élisabeth REMY3

1CRIStAL, CNRS, Université de Lille, France

2Centrale Lille, France

3I2M, CNRS, Aix-Marseille Université, France

Systems biology is an approach that proposes to encompass all the complexity of interactions between biological entities in a systemic whole with the aim of understanding the emergence of physiological or functional properties. It is often opposed to the “reductionist” approach, which consists of understanding the living through its parts studied in isolation, by reducing the “whole” into basic components. Systems biology developed particularly in the early 1990s with the arrival of experimental techniques derived from biotechnology. In fact, the ambitious Human Genome Project and the production of omics data (genomics, proteomics, metabolomics, etc.) have strongly contributed to the emergence of this field by identifying many new molecular interactions. One of the major challenges that arose was the problem of integrating all this information. The use of mathematical and computer tools and methods has proven essential, and systems biology is closely linked to interdisciplinary approaches.

Modeling is a privileged tool in systems biology, allowing the inherent complexity of the systems it studies to be grasped. Alongside mathematics, computer science plays a fundamental role in modeling. It offers a wide range of methods for constructing models from data, analyzes and simulations, and even predicting new hypotheses, which will suggest new experiments.

This book presents contributions of formal methods issued from computer science for modeling the dynamics of biological systems. The methods that mainly interest us are the symbolic methods; those which make it possible to establish the qualitative properties of the models. Where possible, these approaches make it possible to abstract from the quantitative values of the parameters of the models which are often subject to interpretation due to the great variability of the experimental data. The use of parameters is even more problematic when they are simply not identifiable due to too many parameters or insufficient data. Despite an unavoidable loss of precision, these methods allow the extraction of the various qualitative behaviors from system dynamics. Chapters 1 (A. Bonnaffoux) and 4 (J. Béal et al.) explore the inference of models, that is, the construction of qualitative models from different types of data (qualitative or quantitative, at the population or the individual level, etc.).

These considerations on the data and the reliability of the properties lead us to several analogies with computer systems, which have often inspired the symbolic methods developed for biological systems. Computer programs are actually dynamic objects (in their execution), which depend on data (program inputs) and which will potentially interact with other programs. Guaranteeing the proper execution of programs, that is, the absence of bugs or compliance with their specifications, whatever their inputs, also means verifying the reliable properties of programs, with respect to their data. The semantics of programming languages is this field of computer science, which has made it possible to develop numerous specification formalities and proofs of such properties. These include Hoare’s logic and temporal logics used to infer models from kinetic data in Chapter 8 (G. Bernot et al.) and Chapter 3 (T. Ribeiro et al.), and so automatically guaranteeing the satisfaction of the experimental traces by the models obtained. Semantics also studies the degree of abstraction of different modeling languages using powerful abstract interpretation tools. This is addressed in Chapter 5 (L. Paulevé), which introduces a new mode of updating Boolean networks, allowing us to abstract the differential equations semantics. The abstract interpretation is also widely used in Chapter 9 (J. Féret) to statically study the interactions and potential links between the proteins modeled in the Kappa language.

Closely linked to the semantics of programming languages and to mathematics, logical formalisms appear naturally and repeatedly in symbolic modeling methods. As we mentioned above, we find the Hoare and temporal logics, but also the Boolean and multi-valued logics, that are not used to specify properties, but to directly model the dynamics of the system. The inference and the analysis of such models generate a combinatorial explosion that requires focusing on the algorithmic and complexity aspects. Chapter 2 (C. Frioux and A. Siegel) tackles this issue through answer set programming (ASP). Much more expressive logics can also be used for both modeling languages and property languages. In particular, this is the case for linear logics, and more generally from constructive logics, as shown in Chapter 6 (E. De Maria et al.). This constructivism leads us to the computational models widely studied in computer science. In Chapter 7 (F. Fages and F. Molina), the authors question the computational power, in a computational sense, of modeling languages derived from reactions.

Therefore, these nine chapters presenting different approaches related to semantics, language, modeling and their link with data allow us to address fundamental problems and challenges facing systems biology. This book is organized into two parts: the first part includes the contributions that are based on the various accessible data to build models, and the second part works around the questions of semantics and formal methods.

November 2022

PART 1Models and Data