Logical Modeling of Biological Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 743

Veröffentlichungsjahr: 2014

Beschreibung

Systems Biology is the systematic study of the interactions between the components of a biological system and studies how these interactions give rise to the function and behavior of the living system. Through this, a life process is to be understood as a whole system rather than the collection of the parts considered separately. Systems Biology is therefore more than just an emerging field: it represents a new way of thinking about biology with a dramatic impact on the way that research is performed. The logical approach provides an intuitive method to provide explanations based on an expressive relational language. This book covers various aspects of logical modeling of biological systems, bringing together 10 recent logic-based approaches to Systems Biology by leading scientists. The chapters cover the biological fields of gene regulatory networks, signaling networks, metabolic pathways, molecular interaction and network dynamics, and show logical methods for these domains based on propositional and first-order logic, logic programming, answer set programming, temporal logic, Boolean networks, Petri nets, process hitting, and abductive and inductive logic programming. It provides an excellent guide for all scientists, biologists, bioinformaticians, and engineers, who are interested in logic-based modeling of biological systems, and the authors hope that new scientists will be encouraged to join this exciting scientific endeavor.

Leseprobe

Contents

Foreword

1 Symbolic Representation and Inference of Regulatory Network Structures

1.1. Introduction: logical modeling and abductive inference in systems biology

1.2. Logical modeling of regulatory networks

1.3. Evaluation of the ARNI approach

1.4. ARNI assisted scientific methodology

1.5. Related work and comparison with non-symbolic approaches

1.6. Conclusions

1.7. Bibliography

2 Reasoning on the Response of Logical Signaling Networks with ASP

2.1. Introduction

2.2. Answer set programming at a glance

2.3. Learn and control logical networks with ASP

2.4. Conclusion

2.5. Acknowledgments

2.6. Bibliography

3 A Logical Model for Molecular Interaction Maps

3.1. Introduction

3.2. Biological background

3.3. Logical model

3.4. Quantifier elimination for restricted formulas

3.5. Reasoning about interactions in metabolic interaction maps

3.6. Conclusion and future work

3.7. Acknowledgments

3.8. Bibliography

4 Analyzing Large Network Dynamics with Process Hitting

4.1. Introduction/state of the art

4.2. Discrete modeling with the process hitting

4.3. Static analysis of discrete dynamics

4.4. Toward a stochastic semantic

4.5. Biological Applications

4.6. Conclusion

4.7. Bibliography

5 ASP for Construction and Validation of Regulatory Biological Networks

5.1. Introduction

5.2. Preliminaries: ASP and biological logical networks

5.3. Temporal logics

5.4. ASP-based analysis of a GRN

5.5. Conclusions

5.6. Acknowledgments

5.7. Appendix on an advanced modeling for taking into additive constraints

5.8. Bibliography

6 Simulation-based Reasoning about Biological Pathways Using Petri Nets and ASP

6.1. Introduction

6.2. Background

6.3. Translating basic Petri net into ASP

6.4. Changing firing semantics

6.5. Extension - reset arcs

6.6. Extension - inhibitor arcs

6.7. Extension - read arcs

6.8. Extension - colored tokens

6.9. Translating Petri nets with colored tokens to ASP

6.10. Extension - priority transitions

6.11. Extension – timed transitions

6.12. Other extensions

6.13. Answering simulation-based reasoning questions

6.14. Related work

6.15. Conclusion

6.16. Bibliography

7 Formal Methods Applied to Gene Network Modeling

7.1. Introduction

7.2. From gene interactions to gene network modeling

7.3. Logic: a tool for multidisciplinarity with experimental sciences

7.4. Thomas and Sifakis should have met

7.5. Consistency of biological hypotheses

7.6. Validation of biological hypotheses

7.7. Conclusion

7.8. Acknowledgments

7.9. Bibliography

8 Temporal Logic Modeling of Dynamical Behaviors: First-Order Patterns and Solvers

8.1. Temporal logic FO-LTL(lin)

8.2. Formula patterns and dedicated solvers

8.3. Study case: coupled model of the cell cycle and the circadian clock

8.4. Related work

8.5. Conclusion

8.6. Bibliography

9 Analyzing SBGN-AF Networks Using Normal Logic Programs

9.1. Introduction

9.2. The systems biology graphical notation

9.3. Normal logic programs

9.4. Translation of SBGN-AF into logic programming

9.5. Boolean modeling of SBGN-AF signaling networks dynamics

9.6. Discussion

9.7. Conclusion

9.8. Bibliography

10 Machine Learning of Biological Networks using Abductive ILP

10.1. Introduction

10.2. Machine learning of metabolic networks applied to predictive toxicology

10.3. Multi-clause learning of metabolic control points

10.4. Learning a causal network from temporal gene expression data

10.5. Automatic construction of probabilistic trophic networks

10.6. Related work and discussion

10.7. Conclusions

10.8. Acknowledgments

10.9. Bibliography

List of Authors

Index

First published 2014 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

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© ISTE Ltd 2014The rights of Luis Fariñas del Cerro and Katsumi Inoue to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2014941925

British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-84821-680-8

Foreword

Systems biology is the systematic study of the interactions between the components of a biological system and studies how these interactions give rise to the function and behavior of the living system. As such, a life process is to be understood as a whole system rather than the collection of the parts considered separately. Systems biology is therefore more than just an emerging field: it represents a new way of thinking about biology with a dramatic impact on the way that research is performed.

The logical approach provides an intuitive method to give explanations based on an expressive relational language. For example, logic can represent biological networks such as gene regulatory, signal transduction, and metabolic pathways. Unlike other approaches, this method allows a background theory, observations and hypotheses within a common declarative language, and provides the basis for the three forms of inference, i.e. deduction (prediction), abduction (explanation) and induction (generalization). Although deduction produces logically correct (sound) consequences of the theory, both abduction and induction provide expansion of the logical theory, that is, they augment the original theory by adding new hypotheses related to new discoveries. In the last decade, many new approaches have appeared concerning the logical modeling of biological systems. These works cover many different logics such as modeling languages of biological systems as well as diverse aspects and domains in biology.

Biological systems has thus become one of the main domains of application for logic. Logical modeling of metabolic networks is an example of how logic can help the compression of the very complex biological systems. Whatever the signaling pathway of interest, biologists need a “map” to drive their research. These graphical maps have really exploded with the advent of recent technologies such as high-throughput assay and genomic sequencing. In our post-genomic era, mathematics and informatics are essential tools for analyzing and stocking biological information.

In general, biologists are aware of the need to take a holistic view during all phases of research including data collection, information processing, interpretation, knowledge acquisition, domain discovery, hypothesis generation and subsequent experimental design. Many gene regulation models have been proposed, which range from very abstract models (involving Boolean values only) to very concrete Models (including fully bio-chemical interactions with stochastic kinetics), depending on the biological level to be studied. Abstract models involve less biological details and display only qualitative dynamic behavior. However, they are uniquely capable of being implemented for large sized networks. On the other hand, concrete models describe network dynamics in detail and are closer to biological reality, but they can only be implemented for small sized networks. Various modeling techniques have been developed to solve such a problem. These techniques vary according to the type of network models being constructed and include the following steps: logical modeling, inference and model checking, machine learning approaches and several optimization methods.

In this book, we are interested in such various aspects in logical modeling of biological systems. For this purpose, we collected 10 recent logic-based approaches to systems biology by leading scientists. The chapters cover biological domains of gene regulatory networks, signaling networks, metabolic pathways, molecular interaction and network dynamics, and shows logical methods for these domains based on propositional and first-order logic, logic programming, answer set programming, temporal logic, Boolean networks, Petri nets, process hitting, and abductive and inductive logic programming.

In Chapter 1, Maimari et al. tackle the problem of extraction of integrated signaling-transcriptional networks from experimental data. A general logic-based framework, called Abductive Regulatory Network Inference (ARNI), is then proposed as an abductive inference problem, whose solutions are signed-directed networks that explain how genes are affected during the experiments.

In Chapter 2, Schaub et al. focus on logical signaling networks and automated reasoning to model their response using answer set programming (ASP). To gain control over the system, intervention strategies are inferred with ASP to force a set of target species into a desired steady state.

In Chapter 3, Demolombe et al. propose a logical model based on a fragment of first-order logic capable of describing reactions that appear in a metabolic network. They also propose an efficient automated deduction method that can answer queries by deduction to predict reaction results or by abductive reasoning to find reactions and protein states.

In Chapter 4, Paulevé et al. introduce the process hitting framework, which provides the methodology of constructing the most permissive dynamics and then using successive refinements to fine tune the model. They present static analysis methods designed to identify fixed points or answer successive reachability questions, and introduce the stochastic semantics of Process Hitting too.

In Chapter 5, Rocca et al. present a declarative approach for analyzing and building genetic regulatory networks (GRNs). In their approach, a model checker for linear temporal logic (LTL) and computational tree logic (CTL) formulas is implemented using ASP, and a specification of Thomas GRNs is presented in ASP. Then, a methodology associated with this declarative approach is presented, including consistency repairing and learning of properties from a set of consistent models.

In Chapter 6, Anwar et al. aim to answer to the kind of questions that a biologist may ask to test understanding of the underlying system in college level books on biology. This requires simulation-based reasoning, and Anwar et al. use Petri Nets as the formalism to represent and simulate biological pathways and extend them to model interventions specified in the questions. ASP is then used to simulate Petri Nets and allows us to ease extending them.

In Chapter 7, Bernot et al. consider a partial knowledge setting in which the set of all the possible models are managed at each step of the process according to the current knowledge. This is manipulated by the formal validation activity that suggests new biological experiments in such a way that some kind of completeness can be reached. The methodology proposed in this chapter is discrete modeling of gene networks using a particular temporal logic.

In Chapter 8, Fages et al. describe how quantitative temporal logic formulae can be used to formalize imprecise dynamical behaviors of biological systems, and how such a formal specification of experimental observations can be used to calibrate models to real data, in a more versatile way than with curve fitting algorithms.

In Chapter 9, Rougny et al. consider inference problems on molecular networks that are described in the systems biology graphical notation (SBGN). To analyze such networks with logic-based techniques, it is necessary to translate these networks into logical formalisms. Rougny et al. translate the SBGN active flow language (SBGN-AF) into normal logic programs, and show how this translation can be used to analyze the dynamics of SBGN-AF networks within a Boolean network setting.

In Chapter 10, Tamaddoni-Nezhad et al. review the methods and the main results from their machine learning studies on biological networks. They show how abductive and inductive logic programming (ALP/ILP) have been used in their applications to metabolic inhibition, metabolic regulation, gene expression and trophic networks by comparing different ALP/ILP approaches.

We would like to thank all coauthors for their great contributions and their mutual reviews of chapters as well as inspiring discussions, and are grateful to S. Menasce and J.Ch Pomerol for their great support from the initialization to the publication of this book. We hope that this book will be a good guide for all scientists, biologists, bioinformaticians and engineers, who are interested in logic-based modeling of biological systems, and that new scientists are encouraged to join this exciting scientific endeavor.

Luis FARIÑAS DEL CERROKatsumi INOUEJune 2014

1

Symbolic Representation and Inference of Regulatory Network Structures

Recent results have demonstrated the usefulness of symbolic approaches for addressing various problems in systems biology. One of the fundamental challenges in systems biology is the extraction of integrated signaling-transcriptional networks from experimental data. In this chapter, we present a general logic-based framework, called Abductive Regulatory Network Inference (ARNI), where we formalize the network extraction problem as an abductive inference problem. A general logical model is provided that integrates prior knowledge on molecular interactions and other information for capturing signal-propagation principles and compatibility with experimental data. Solutions to our abductive inference problem define signed-directed networks that explain how genes are affected during the experiments. Using in-silico datasets provided by the dialogue for reverse engineering assessments and methods (DREAM)) consortium, we demonstrate the improved predictive power and complexity of our inferred network topologies compared with those generated by other non-symbolic inference approaches, showing the suitability of our approach for computing complete realistic networks. We also explore how the improved expressiveness together with the modularity and flexibility of the logic-based nature of our approach can support automated scientific discovery where the validity of hypothesized biological ideas can be examined and tested outside the laboratory.

1.1. Introduction: logical modeling and abductive inference in systems biology

Systems biology is generally concerned with developing formal models that aim to describe the operation of various biological processes. Its study is based on the synthesis of a model or a theory from empirical experimental information. At the cellular level, systems biology aims to build models that describe, at some level of abstraction, the underlying operation of a cell at the genomic and/or protein level. The central challenge is then how to choose an appropriate framework that would (1) enable the construction of a model from experimental data and (2) empower such models with a predictive capability for new information beyond the one used to construct the model.

As in many cases of such scientific exploration, the choice of the framework under which we formulate the model depends on the type of experimental data that is available at the time of the development of the scientific model. In general, at the initial stages of an investigation the available data is usually descriptive and qualitative rather than quantitative. As such we set out to develop a first model, based on some principles that we believe underlie the phenomena, where we are primarily interested in capturing the overall and general interrelation between the concepts of interest. It is then important to require a framework that is (1) high-level close to the human description of the phenomena and thus close to the experimental language, and (2) modular and flexible so that the models can easily be adapted to new information and other changes that might come about.

Under these conditions and requirements for our language, a symbolic or logical framework is particularly suitable. A logical scientific theory normally offers a high-level declarative description that can be understood easily by the expert experimental scientists that provide the experimental data. Logical models are also highly modular where changes can often be isolated to parts of the model without the need for an overall complete reformulation of the model. Furthermore, within a logical approach we can employ abductive reasoning to help in the process of building a theory from experimental data. Abductive reasoning is a formalization of the explanatory scientific reasoning that is typically carried out by human scientists when they think about the phenomena they are studying, either when they are trying to understand their experimental findings, or when they are planning the next set of experiments to help them improve their understanding of the phenomena.

Hence, in choosing a logical approach, we provide a framework that not only responds well to the object level requirement of describing the phenomena, but also to the meta level task of reasoning about the models developed thus far and deciding on their further investigation through new experiments, or indeed new desirable properties and principles that the model must adhere to. For molecular biology, logic is particularly suited as, at least currently, in many cases the theoretical models and experimentation of cell biology are developed following a rationale at the qualitative rather than quantitative level. The nature of much of the experimental data is descriptive with the aim to first understand the qualitative interrelations between the various constituents and processes in the cell.

In this chapter, we have developed a logical model of regulatory cell networks, covering both transcriptional networks and upstream signaling regulatory networks. We have implemented a qualitative model that is based on general biological principles and which exploits current prior knowledge of molecular interactions that are already known. The approach, called ARNI, for abductive inference of regulatory networks, constructs causal signed-directed networks of interactions between genes from high-throughput experimental data. These networks rely on the simple and general underlying principles that signals from the environment propagate along paths of protein interactions to reach the regulatory components of cells (i.e. production of genes) and that genes are under the influence of multiple overlapping inputs, which might be compatible or competitive to each other. The networks also exhibit several important motifs including feedback loops (positive and negative), which allow a gene to control its own expression, and feed-forward loops (coherent or incoherent), whereby a gene has both direct and indirect connections to its target1. Each of these motifs governs fundamental properties of the overall dynamic behaviorof the network such as robustness, oscillations, memory and bistability [ALO 07, YEG 04].

Our construction of regulatory networks relies on abductive reasoning as an automated form of the scientific reasoning of rationalizing the high throughput experimental data. Indeed, the problem of signaling network reconstruction naturally maps to an abductive task. Specifically, (1) gene expression data constitutes the experimental data; (2) the given (partial) knowledge is a logic-based theory governing biological phenomena, as for instance the notions of gene regulation, interactive potential; (3) biological constraints like sign consistency between interacting gene expressions are captured via integrity constraints and (4) sentences about unknown compatible and competitive gene regulations are the abducible information that can be assumed to form a network. Thus, assuming the general possible structure of signaling networks an abductive computation results in the inference of possible signed-directed networks, in terms of compatible and competitive gene regulations, that conform to the available experimental observations.