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

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Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular NetworksSuurem pilt
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"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

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

 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.

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

This book is highly multi-disciplinary and will appeal to biologists, engineers, computer scientists, mathematicians and others.

Arvustused

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 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

Preface xvii
Chapter 1 Introduction to modelling
1(28)
1.1 What Is Modelling?
1(2)
1.1.1 What are models?
2(1)
1.2 Why Build Models?
3(4)
1.2.1 Why model biological systems?
5(1)
1.2.2 Why systems biology?
6(1)
1.3 Challenges In Modelling Biological Systems
7(2)
1.4 The Practiceofmodelling
9(10)
1.4.1 Scope of the model
10(1)
1.4.2 Making assumptions
11(1)
1.4.3 Modelling paradigms
11(2)
1.4.4 Buildingthe model
13(4)
1.4.5 Model analysis, debugging and (in)validation
17(1)
1.4.6 Simulating the model
18(1)
1.5 Examples Of Models
19(3)
1.5.1 Lotka-Volterra predator-prey model
19(1)
1.5.2 SIR model: A classic example
20(2)
1.6 Troubleshooting
22(7)
1.6.1 Clarity of scope and objectives
22(1)
1.6.2 The breakdown of assumptions
22(1)
1.6.3 Is my model fit for purpose?
23(1)
1.6.4 Handling uncertainties
23(1)
Exercises
23(1)
References
24(2)
Further Reading
26(3)
Part I Static Modelling
Chapter 2 Introduction to graph theory
29(28)
2.1 Basics
29(3)
2.1.1 History of graph theory
29(2)
2.1.2 Examples of graphs
31(1)
2.2 Why Graphs?
32(1)
2.3 Types Ofcraphs
33(6)
2.3.1 Simple vs. non-simple graphs
34(1)
2.3.2 Directed vs. undirected graphs
34(1)
2.3.3 Weighted vs. unweighted graphs
35(1)
2.3.4 Other graph types
35(3)
2.3.5 Hypergraphs
38(1)
2.4 Computational Representations Ofcraphs
39(3)
2.4.1 Data structures
39(1)
2.4.2 Adjacency matrix
39(2)
2.4.3 The Laplacian matrix
41(1)
2.5 Graph Representations Of Biological Networks
42(7)
2.5.1 Networks of protein interactions and functional associations
42(1)
2.5.2 Signalling networks
43(2)
2.5.3 Protein structure networks
45(1)
2.5.4 Gene regulatory networks
45(1)
2.5.5 Metabolic networks
46(3)
2.6 Common Challenges & Troubleshooting
49(2)
2.6.1 Choosing a representation
49(2)
2.6.2 Loading and creating graphs
51(1)
2.7 Software Tools
51(6)
Exercises
52(2)
References
54(2)
Further Reading
56(1)
Chapter 3 Structure of networks
57(34)
3.1 Network Parameters
57(9)
3.1.1 Fundamental parameters
58(4)
3.1.2 Measures of centrality
62(3)
3.1.3 Mixing patterns: Assortativity
65(1)
3.2 Canonical Network Models
66(9)
3.2.1 Erdos-Renyi (ER) network model
67(2)
3.2.2 Small-world networks
69(2)
3.2.3 Scale-free networks
71(3)
3.2.4 Other models of network generation
74(1)
3.3 Community Detection
75(4)
3.3.1 Modularity maximisation
76(1)
3.3.2 Similarity-based clustering
77(1)
3.3.3 Girvan-Newman algorithm
78(1)
3.3.4 Other methods
78(1)
3.3.5 Community detection in biological networks
79(1)
3.4 Network Motifs
79(2)
3.4.1 Randomising networks
80(1)
3.5 Perturbations To Networks
81(2)
3.5.1 Quantifying effects of perturbation
82(1)
3.5.2 Network structure and attack strategies
82(1)
3.6 Troubleshooting
83(1)
3.6.1 Is your network really scale-free?
83(1)
3.7 Softwaretools
84(7)
Exercises
85(1)
References
86(4)
Further Reading
90(1)
Chapter 4 Applications of network biology
91(24)
4.1 Thecentrality-Lethality Hypothesis
92(1)
4.1.1 Predicting essential genes using network measures
92(1)
4.2 Networks and modules In Disease
93(4)
4.2.1 Disease networks
93(2)
4.2.2 Identification of disease modules
95(2)
4.2.3 Edgetic perturbation models
97(1)
4.3 Differential Network Analysis
97(2)
4.4 Disease Spreading On Networks
99(2)
4.4.1 Percolation-based models
99(1)
4.4.2 Agent-based simulations
100(1)
4.5 Molecular Graphs And Their Applications
101(3)
4.5.1 Retrosynthesis
102(2)
4.6 Protein Structure And Conformational Networks
104(3)
4.6.1 Protein folding pathways
104(3)
4.7 Link Prediction
107(8)
Exercises
107(1)
References
108(4)
Further Reading
112(3)
Part II Dynamic Modelling
Chapter 5 Introduction to dynamic modelling
115(16)
5.1 Constructing Dynamic Models
116(1)
5.1.1 Modelling a generic biochemical system
116(1)
5.2 MASS-Action Kinetic Models
117(1)
5.3 Modelling Enzyme Kinetics
118(6)
5.3.1 The Michaelis-Menten model
118(4)
5.3.2 Co-operativity: Hill kinetics
122(1)
5.3.3 An Illustrative Example: A Three-Node Oscillator
123(1)
5.4 Generalised Rate Equations
124(1)
5.4.1 Biochemical systems theory
125(1)
5.5 Solving ODEs
125(2)
5.6 Troubleshooting
127(1)
5.6.1 Handlingstiff equations
127(1)
5.6.2 Handling uncertainty
127(1)
5.7 Software Tools
128(3)
Exercises
128(1)
References
129(1)
Further Reading
130(1)
Chapter 6 Parameter estimation
131(30)
6.1 Data-Driven Mechanistic Modelling: An Overview
131(3)
6.1.1 Pre-processing the data
133(1)
6.1.2 Model identification
134(1)
6.2 Setting Up An Optimisation Problem
134(5)
6.2.1 Linear regression
135(1)
6.2.2 Least squares
135(3)
6.2.3 Maximum likelihood estimation
138(1)
6.3 Algorithms For Optimisation
139(11)
6.3.1 Desiderata
139(1)
6.3.2 Gradient-based methods
139(1)
6.3.3 Direct search methods
140(3)
6.3.4 Evolutionary algorithms
143(7)
6.4 POST-Regression Diagnostics
150(3)
6.4.1 Model selection
150(1)
6.4.2 Sensitivity and-robustness of biological models
151(2)
6.5 Troubleshooting
153(2)
6.5.1 Regularisation
153(1)
6.5.2 Sloppiness
153(1)
6.5.3 Choosing a search algorithm
154(1)
6.5.4 Model reduction
154(1)
6.5.5 The curse of dimensionality
154(1)
6.6 Software Tools
155(6)
Exercises
155(2)
References
157(3)
Further Reading
160(1)
Chapter 7 Discrete dynamic models: Boolean networks
161(12)
7.1 Introduction
161(1)
7.2 Boolean Networks: Transfer Functions
162(4)
7.2.1 Characterising Boolean network dynamics
164(1)
7.2.2 Synchronous vs. asynchronous updates
165(1)
7.3 Other Paradigms
166(1)
7.3.1 Probabilistic Boolean networks
166(1)
7.3.2 Logical interaction hypergraphs
166(1)
7.3.3 Generalised logical networks
166(1)
7.3.4 Petri nets
166(1)
7.4 Applications
167(1)
7.5 Troubleshooting
167(1)
7.6 Software Tools
167(6)
Exercises
168(1)
References
169(1)
Further Reading
170(3)
Part III Constraint-based Modelling
Chapter 8 Introduction to constraint-based modelling
173(28)
8.1 What Are Constraints?
174(3)
8.1.1 Types of constraints
175(1)
8.1.2 Mathematical representation of constraints
176(1)
8.1.3 Why are constraints useful?
177(1)
8.2 The Stoichiometric Matrix
177(1)
8.3 Steady-State Mass Balance: Flux Balance Analysis
178(3)
8.4 The Objective Function
181(2)
8.4.1 The biomass objective function
182(1)
8.5 Optimisation To Compute Flux Distribution
183(2)
8.6 An Illustration
185(2)
8.7 Flux Variability Analysis (FVA)
187(1)
8.8 Understanding FBA
187(7)
8.8.1 Blocked reactions and dead-end metabolites
189(1)
8.8.2 Gaps in metabolic networks
190(1)
8.8.3 Multiple solutions
191(1)
8.8.4 Loops
191(1)
8.8.5 Parsimonious FBA (pFBA)
192(1)
8.8.6 ATP maintenance fluxes
193(1)
8.9 Troubleshooting
194(1)
8.9.1 Zero growth rate
194(1)
8.9.2 Objective values vs. flux values
194(1)
8.10 Softwaretools
195(6)
Exercises
195(2)
References
197(3)
Further Reading
200(1)
Chapter 9 Extending constraint-based approaches
201(22)
9.1 Minimisation Of Metabolic Adjustment (MoMA)
202(2)
9.1.1 Fitting experimentally measured fluxes
203(1)
9.2 Regulatoryon-Offminimisation (ROOM)
204(1)
9.2.1 ROOMvs.MoMA
205(1)
9.3 BI-Level Optimisation
205(1)
9.3.1 OptKnock
205(1)
9.4 Integrating Regulatory Information
206(3)
9.4.1 Embedding regulatory logic Regulatory FBA (rFBA)
206(1)
9.4.2 Informing metabolic models with omic data
207(2)
9.4.3 Tissue-specific models
209(1)
9.5 Compartmentalised Models
209(1)
9.6 Dynamic Flux Balance Analysis (dFBA)
210(2)
9.7 13C-MFA
212(1)
9.8 Elementary Flux Modes And Extreme Pathways
213(10)
9.8.1 Computing EFMs and EPs
216(1)
9.8.2 Applications
216(1)
Exercises
216(2)
References
218(4)
Further Reading
222(1)
Chapter 10 Perturbations to metabolic networks
223(26)
10.1 Knock-Outs
224(1)
10.1.1 Gene deletions vs. reaction deletions
225(1)
10.2 Synthetic Lethals
225(7)
10.2.1 Exhaustive enumeration
226(1)
10.2.2 Bi-level optimisation
227(2)
10.2.3 Fast-SL Massively pruningthe search space
229(3)
10.3 Over-Expression
232(1)
10.3.1 Flux Scanning based on Enforced Objective Flux (FSEOF)
232(1)
10.4 Other Perturbations
233(1)
10.5 Evaluating And Ranking Perturbations
233(1)
10.6 Applications Of Constraint-Based Models
234(3)
10.6.1 Metabolic engineering
235(1)
10.6.2 Drug target identification
236(1)
10.7 Limitations Of Constraint-Based Approaches
237(2)
10.7.1 Incorrect predictions
237(2)
10.8 Troubleshooting
239(1)
10.8.1 Interpreting gene deletion simulations
239(1)
10.9 Softwaretools
239(10)
Exercises
240(1)
References
241(5)
Further Reading
246(3)
Part IV Advanced Topics
Chapter 11 Modelling cellular interactions
249(26)
11.1 Microbialcommunities
249(11)
11.1.1 Network-based approaches
250(5)
11.1.2 Population-based and agent-based approaches
255(1)
11.1.3 Constraint-based approaches
256(4)
11.2 Host-Pathogen Interactions (HPIs)
260(5)
11.2.1 Network models
260(2)
11.2.2 Dynamic models
262(1)
11.2.3 Constraint-based models
263(2)
11.3 Summary
265(1)
11.4 Softwaretools
265(10)
Exercises
266(1)
References
267(7)
Further Reading
274(1)
Chapter 12 Designing biological circuits
275(18)
12.1 What Is Synthetic Biology?
275(2)
12.1.1 From LEGO bricks to biobricks
276(1)
12.2 Classic Circuit Design Experiments
277(3)
12.2.1 Designing an oscillator: The repressilator
278(1)
12.2.2 Toggle switch
279(1)
12.3 Designing Modules
280(2)
12.3.1 Exploringthe design space
280(2)
12.3.2 Systems-theoretic approaches
282(1)
12.3.3 Automating circuit design
282(1)
12.4 Design Principles Of Biological Networks
282(2)
12.4.1 Redundancy
283(1)
12.4.2 Modularity
283(1)
12.4.3 Exaptation
284(1)
12.4.4 Robustness
284(1)
12.5 Computing With Cells
284(2)
12.5.1 Adleman's classic experiment
285(1)
12.5.2 Examples of circuits that can compute
285(1)
12.5.3 DNA data storage
286(1)
12.6 Challenges
286(1)
12.7 Softwaretools
287(6)
Exercises
287(1)
References
288(4)
Further Reading
292(1)
Chapter 13 Robustness and evolvability of biological systems
293(16)
13.1 Robustness In Biological Systems
294(4)
13.1.1 Key mechanisms
294(1)
13.1.2 Hierarchies and protocols
295(1)
13.1.3 Organising principles
296(2)
13.2 Genotype Spaces And Genotype Networks
298(3)
13.2.1 Genotype spaces
298(1)
13.2.2 Genotype-phenotype mapping
298(3)
13.3 Quantifying Robustness And Evolvability
301(4)
13.4 Softwaretools
305(4)
Exercises
305(1)
References
306(1)
Further Reading
307(2)
Chapter 14 Epilogue: The road ahead
309(18)
Online-only appendices
325(2)
Appendix A Introduction to key biological concepts
Appendix B Reconstruction of biological networks
Appendix C Databases for systems biology
Appendix D Software tools compendium
Appendix E MATLAB for systems biology
Index 327
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.
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