Computational Systems Biology

Inference and Modelling

Paola Lecca, Angela Re, Adaoha Elizabeth Ihekwaba, Ivan Mura, Thanh-Phuong Nguyen

• Publisher: Woodhead Publishing
• Year: 2016
• Print ISBN: 9780081000953
• eBook ISBN: 9780081001158
• Topic: Biological Sciences
• Subtopic: Biology
• Index: Biological Sciences

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems.This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling.The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.- Provides a unified presentation of network inference, analysis, and modeling- Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems- Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology- Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications

Chapter 1

Overview of Biological Network Inference and Modeling of Dynamics

Abstract

In the era of high-throughput experiments, inferring and modelling the dynamics of biological systems are complex tasks. The complexity derives from the large sizes, the presence of competing interactions, stiffness, and non-linearity in the systems under investigation.

Moreover, the dynamics in these systems are typically hybrid — that is, stochastic and deterministic and time irreversible — raising many technical and conceptual challenges: is it possible, at least in principle, to infer the topology and the properties of a biological network from observations of the dynamics? How much and what information do we need to obtain from an experiment to accurately infer a network model? Would it be possible to accurately describe the dynamics of the stochastic interactions without a full stochastic simulation, which is extremely computationally expensive for systems of huge size? How is it possible to infer from partial and noisy observations the trajectory of the system for complete observations?

All these questions are born of the realistic possibility that the inference and modeling of omic-size, complex interaction networks is an underdetermined problem. In this introductory chapter, we present the challenges underdetermination modeling has to face in systems biology and its usefulness in the era of high-throughput experiments and big data collection.

Keywords

Biological networks; Underdetermination; Complex systems; Nonlinearity; Network inference; Computational models; Experimental design.

Chapter Outline

1.1 Introduction to Inference of Topologies, Causalities, and Dynamic Models 2

1.2 The Data 4

1.2.1 Features of Optimal Datasets 4

1.2.2 Performance and Reliability Issues 10

1.1 Introduction to Inference of Topologies, Causalities, and Dynamic Models

The inference of biological networks is the process of deduction of the interactions among the components of a biological system from experimental data. Computational inferential procedures are challenged by the huge size of the datasets made available by current high-throughput experiments. Big data are characterized by high velocity, high volume, and high variety — that is, respectively, by millions of data streams, terabytes of data, and heterogeneous data types concerning the identity and the quantitative features of the components of the systems under investigation (genes, proteins, metabolites, functional complexes, etc.). Experimental determination of the role of these components aiming at building an interaction network and, possibly, its causal topology, is practically unfeasible because to explore such a huge variable and parameter space could take forever. On the other hand, an a priori restriction of the variable and parameter space to be investigated experimentally would preclude the possibility to have a realistic system view of the biological processes involving the components of the system.

However, the computational inference of interaction net...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• About the Authors
• Preface
• Acknowledgments
• Chapter 1: Overview of Biological Network Inference and Modeling of Dynamics
• Chapter 2: Network Inference From Steady-State Data
• Chapter 3: Network Inference From Time-Course Data
• Chapter 4: Network-Based Conceptualization of Observational Data
• Chapter 5: Deterministic Differential Equations
• Chapter 6: Stochastic Differential Equations
• Chapter 7: From Network Inference to the Study of Human Diseases
• Chapter 8: Conclusions
• Bibliography
• Index