An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks

ISBN-13: 9780367752507 / Angielski

Karthik Raman

Opis

This book delivers a comprehensive and insightful account of applying mathematical modelling approaches to very large biological systems and networks—a fundamental aspect of computational systems biology. The book covers key modelling paradigms in detail, while at the same time retaining a simplicity that will appeal to those from less quantitative fields.

Key Features:

• A hands-on approach to modelling
• Covers a broad spectrum of modelling, from static networks to dynamic models and constraint-based models
• Thoughtful exercises to test and enable understanding of concepts
• State-of-the-art chapters on exciting new developments, like community modelling and biological circuit design
• Emphasis on coding and software tools for systems biology

• Companion website featuring lecture videos, figure slides, codes, supplementary exercises, further reading, and appendices: https://ramanlab.github.io/SysBioBook/

An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks is highly multi-disciplinary and will appeal to biologists, engineers, computer scientists, mathematicians and others.

Recenzje

This is a very comprehensive read that provides a solid base in computational biology. The book is structured in 4 parts and 14 chapters which cover all the way from the more basic concepts to advanced material, including the state-of-the-art methodologies in synthetic and systems biology. This is a bedside book for those researchers embarking to do investigation in computational biology and a great office companion for anyone working on systems and synthetic biology.

-- Rodrigo Ledesma Amaro, Lecturer, Imperial College London 

This is a fantastic book.  It offers an elegant introduction to both classical and modern concepts in computational biology.  To the uninitiated, it is a terrific first read, bringing alive the glory of the past and the promise of the future.  To the interested, it handholds and offers a springboard to dive deep.  To the practitioner, it serves as a valuable resource bringing together in a panoramic view many diverse streams that adorn the landscape.

-- Narendra M. Dixit, Professor, Indian Institute of Science

Spis treści

Preface

Introduction to modelling 
1.1 WHAT IS MODELLING? 
1.1.1 What are models? 
1.2 WHYBUILD MODELS? 
1.2.1 Why model biological systems? 
1.2.2 Why systems biology? 
1.3 CHALLENGES IN MODELLING BIOLOGICAL SYSTEMS 
1.4 THE PRACTICE OF MODELLING 
1.4.1 Scope of the model
1.4.2 Making assumptions 
1.4.3 Modelling paradigms 
1.4.4 Building the model 
1.4.5 Model analysis, debugging and (in)validation 
1.4.6 Simulating the model 
1.5 EXAMPLES OF MODELS 
1.5.1 Lotka–Volterra predator–prey model 
1.5.2 SIR model: a classic example 
1.6 TROUBLESHOOTING 
1.6.1 Clarity of scope and objectives 
1.6.2 The breakdown of assumptions 
1.6.3 Ismy model fit for purpose? 
1.6.4 Handling uncertainties 
EXERCISES 
REFERENCES 
FURTHER READING 

Introduction to graph theory 
2.1 BASICS 
2.1.1 History of graph theory 
2.1.2 Examples of graphs 
2.2 WHYGRAPHS? 
2.3 TYPES OF GRAPHS 
2.3.1 Simple vs. non-simple graphs 
2.3.2 Directed vs. undirected graphs 
2.3.3 Weighted vs. unweighted graphs 
2.3.4 Other graph types 
2.3.5 Hypergraphs 
2.4 COMPUTATIONAL REPRESENTATIONS OF GRAPHS 
2.4.1 Data structures 
2.4.2 Adjacency matrix 
2.4.3 The laplacian matrix 
2.5 GRAPH REPRESENTATIONS OF BIOLOGICAL NETWORKS 
2.5.1 Networks of protein interactions and functional associations
2.5.2 Signalling networks 
2.5.3 Protein structure networks 
2.5.4 Gene regulatory networks 
2.5.5 Metabolic networks 
2.6 COMMONCHALLENGES&TROUBLESHOOTING 
2.6.1 Choosing a representation 
2.6.2 Loading and creating graphs 
2.7 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Structure of networks 
3.1 NETWORK PARAMETERS 
3.1.1 Fundamental parameters 
3.1.2 Measures of centrality 
3.1.3 Mixing patterns: assortativity 
3.2 CANONICAL NETWORK MODELS 
3.2.1 Erdos–Rényi (ER) network model 
3.2.2 Small-world networks 
3.2.3 Scale-free networks 
3.2.4 Other models of network generation 
3.3 COMMUNITY DETECTION 
3.3.1 Modularity maximisation 
3.3.2 Similarity-based clustering 
3.3.3 Girvan–Newman algorithm 
3.3.4 Other methods 
3.3.5 Community detection in biological networks 
3.4 NETWORKMOTIFS 
3.4.1 Randomising networks 
3.5 PERTURBATIONS TO NETWORKS 
3.5.1 Quantifying e□fects of perturbation 
3.5.2 Network structure and attack strategies 
3.6 TROUBLESHOOTING 
3.6.1 Is your network really scale-free? 
3.7 SOFTWARE TOOLS 
EXERCISES 
REFERENCES
FURTHER READING 

Applications of network biology 
4.1 THE CENTRALITY–LETHALITY HYPOTHESIS 
4.1.1 Predicting essential genes fromnetworks 
4.2 NETWORKS AND MODULES IN DISEASE 
4.2.1 Disease networks 
4.2.2 Identification of disease modules 
4.2.3 Edgetic perturbation models 
4.3 DIFFERENTIAL NETWORK ANALYSIS 
4.4 DISEASE SPREADING ON NETWORKS 
4.4.1 Percolation-based models 
4.4.2 Agent-based simulations 
4.5 MOLECULAR GRAPHS AND THEIR APPLICATIONS 
4.5.1 Retrosynthesis 
4.6 PROTEIN STRUCTURE, ENERGY & CONFORMATIONAL NETWORKS
4.6.1 Protein folding pathways 
4.7 LINK PREDICTION 
EXERCISES 
REFERENCES 
FURTHER READING 

Introduction to dynamic modelling
5.1 CONSTRUCTING DYNAMIC MODELS 
5.1.1 Modelling a generic biochemical system 
5.2 MASS-ACTION KINETIC MODELS 
5.3 MODELLING ENZYME KINETICS 
5.3.1 The Michaelis–Menten model 
5.3.2 Extending the Michaelis–Menten model 
5.3.3 Limitations of Michaelis–Menten models 
5.3.4 Co-operativity: Hill kinetics 
5.3.5 An illustrative example: a three-node oscillator 
5.4 GENERALISED RATE EQUATIONS 
5.4.1 Biochemical systems theory 
5.5 SOLVING ODES 
5.6 TROUBLESHOOTING 
5.6.1 Handing sti□f equations 
5.6.2 Handling uncertainty 
5.7 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Parameter estimation 
6.1 DATA-DRIVEN MECHANISTIC MODELLING: AN OVERVIEW 
6.1.1 Pre-processing the data 
6.1.2 Model identification 
6.2 SETTING UP AN OPTIMISATION PROBLEM 
6.2.1 Linear regression 
6.2.2 Least squares 
6.2.3 Maximumlikelihood estimation 
6.3 ALGORITHMS FOR OPTIMISATION 
6.3.1 Desiderata 
6.3.2 Gradient-based methods 
6.3.3 Direct search methods 
6.3.4 Evolutionary algorithms 
6.4 POST-REGRESSION DIAGNOSTICS 
6.4.1 Model selection 
6.4.2 Sensitivity and robustness of biological models 
6.5 TROUBLESHOOTING 
6.5.1 Regularisation 
6.5.2 Sloppiness 
6.5.3 Choosing a search algorithm 
6.5.4 Model reduction 
6.5.5 The curse of dimensionality 
6.6 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Discrete dynamic models: Boolean networks 
7.1 INTRODUCTION 
7.2 BOOLEAN NETWORKS: TRANSFER FUNCTIONS 
7.2.1 Characterising Boolean network dynamics 
7.2.2 Synchronous vs. asynchronous updates 
7.3 OTHER PARADIGMS 
7.3.1 Probabilistic Boolean networks 
7.3.2 Logical interaction hypergraphs 
7.3.3 Generalised logical networks 
7.3.4 Petri nets 
7.4 APPLICATIONS 
7.5 TROUBLESHOOTING 
7.6 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Introduction to constraint-based modelling 
8.1 WHAT ARE CONSTRAINTS? 
8.1.1 Types of constraints 
8.1.2 Mathematical representation of constraints 
8.1.3 Why are constraints useful? 
8.2 THE STOICHIOMETRICMATRIX 
8.3 STEADY-STATEMASSBALANCE:FLUXBALANCEANALYSIS (FBA)
8.4 THE OBJECTIVE FUNCTION 
8.4.1 The biomass objective function 
8.5 OPTIMISATION TO COMPUTE FLUX DISTRIBUTION 
8.6 AN ILLUSTRATION 
8.7 FLUX VARIABILITY ANALYSIS (FVA) 
8.8 UNDERSTANDING FBA 
8.8.1 Blocked reactions and dead-end metabolites 
8.8.2 Gaps in metabolic networks 
8.8.3 Multiple solutions
8.8.4 Loops 
8.8.5 Parsimonious FBA (pFBA) 
8.8.6 ATP maintenance fluxes 
8.9 TROUBLESHOOTING 
8.9.1 Zero growth rate 
8.9.2 Objective values vs. flux values 
8.10 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Extending constraint-based approaches 
9.1 MINIMISATION OF METABOLIC ADJUSTMENT (MOMA) 
9.1.1 Fitting experimentally measured fluxes 
9.2 REGULATORY ON-OFF MINIMISATION (ROOM) 
9.2.1 ROOMvs.MoMA 
9.3 BI-LEVEL OPTIMISATIONS 
9.3.1 OptKnock
9.4 INTEGRATING REGULATORY INFORMATION 
9.4.1 Embedding regulatory logic: regulatory FBA (rFBA) 
9.4.2 Informing metabolic models with omic data 
9.4.3 Tissue-specific models 
9.5 COMPARTMENTALISED MODELS 
9.6 DYNAMIC FLUX BALANCE ANALYSIS (dFBA) 
9.7 13C-MFA 
9.8 ELEMENTARY FLUX MODES AND EXTREME PATHWAYS 
9.8.1 Computing EFMs and EPs 
9.8.2 Applications 
EXERCISES 
REFERENCES 
FURTHER READING

Perturbations to metabolic networks
10.1 KNOCK-OUTS 
10.1.1 Gene deletions vs. reaction deletions 
10.2 SYNTHETIC LETHALS 
10.2.1 Exhaustive enumeration 
10.2.2 Bi-level optimisation 
10.2.3 Fast-SL: massively pruning the search space 
10.3 OVER-EXPRESSION 
10.3.1 Flux Scanning based on Enforced Objective Flux (FSEOF) 
10.4 OTHER PERTURBATIONS 
10.5 EVALUATING AND RANKING PERTURBATIONS 
10.6 APPLICATIONS OF CONSTRAINT-BASED MODELS 
10.6.1 Metabolic engineering 
10.6.2 Drug target identification 
10.7 LIMITATIONS OF CONSTRAINT-BASED APPROACHES 
10.7.1 Scope of genome-scale metabolic models 
10.7.2 Incorrect predictions 
10.8 TROUBLESHOOTING
10.8.1 Interpreting gene deletion simulations 
10.9 SOFTWARE TOOLS

EXERCISES 
REFERENCES 
FURTHER READING 

Modelling cellular interactions 
11.1 MICROBIAL COMMUNITIES 
11.1.1 Network-based approaches 
11.1.2 Population-based and agent-based approaches 
11.1.3 Constraint-based approaches 
11.2 HOST–PATHOGEN INTERACTIONS (HPIs) 
11.2.1 Network models 
11.2.2 Dynamic models 
11.2.3 Constraint-based models 
11.3 SUMMARY
11.4 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Designing biological circuits 
12.1 WHAT IS SYNTHETIC BIOLOGY? 
12.2 FROMLEGO BRICKS TO BIOBRICKS 
12.3 CLASSIC CIRCUIT DESIGN EXPERIMENTS 
12.3.1 Designing an oscillator: the repressilator 
12.3.2 Toggle switch 
12.4 DESIGNING MODULES 
12.4.1 Exploring the design space 
12.4.2 Systems-theoretic approaches 
12.4.3 Automating circuit design 
12.5 DESIGN PRINCIPLES OF BIOLOGICAL NETWORKS 
12.5.1 Redundancy 
12.5.2 Modularity 
12.5.3 Exaptation 
12.5.4 Robustness 
12.6 COMPUTING WITH CELLS 
12.6.1 Adleman’s classic experiment 
12.6.2 Examples of circuits that can compute 
12.6.3 DNA data storage 
12.7 CHALLENGES 
12.8 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Robustness and evolvability of biological systems 
13.1 ROBUSTNESS IN BIOLOGICAL SYSTEMS 
13.1.1 Key mechanisms 
13.1.2 Hierarchies and protocols 
13.1.3 Organising principles 
13.2 GENOTYPE SPACES AND GENOTYPE NETWORKS 
13.2.1 Genotype spaces 
13.2.2 Genotype–phenotype mapping 
13.3 QUANTIFYING ROBUSTNESS AND EVOLVABILITY 
13.4 SOFTWARE TOOLS 
EXERCISES 
REFERENCES 
FURTHER READING 

Epilogue: The Road Ahead 
Index 325

O autorze

Dr. Karthik Raman is an Associate Professor at the Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras. He co-founded and co-ordinates the Initiative for Biological Systems Engineering and is a core member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI). He has been a researcher in the area of systems biology for the last 15+ years and has been teaching a course on systems biology for the last eight years, to (mostly) engineers from different backgrounds. His lab works on computational approaches to understand and manipulate biological networks, with applications in metabolic engineering and synthetic biology.