An Introduction to Computational Systems Biology Systems Level Modelling of Cellular Networks 1st Edition by Karthik Raman ISBN 0367752506 9780367752507

Chapter 1 ▪ Introduction to modelling

1.1 What is modelling?

1.1.1 What are models?

1.2 Why build 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 Is my model fit for purpose?

1.6.4 Handling uncertainties

EXERCISES

References

Further Reading

Part I Static Modelling

Chapter 2 ▪ Introduction to graph theory

2.1 Basics

2.1.1 History of graph theory

2.1.2 Examples of graphs

2.2 Why graphs?

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 functionalhfill penalty -@M 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 Common challenges & troubleshooting

2.6.1 Choosing a representation

2.6.2 Loading and creating graphs

2.7 Software tools

EXERCISES

References

Further Reading

Chapter 3 ▪ 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 Erdős–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 Network motifs

3.4.1 Randomising networks

3.5 Perturbations to networks

3.5.1 Quantifying effects 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

Chapter 4 ▪ Applications of network biology

4.1 The centrality–lethality hypothesis

4.1.1 Predicting essential genes using network measures

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 and conformational networks

4.6.1 Protein folding pathways

4.7 Link prediction

EXERCISES

References

Further Reading

Part II Dynamic Modelling

Chapter 5 ▪ 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–Mentenmodel

5.3.2 Co-operativity: Hill kinetics

5.3.3 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 Handling stiff equations

5.6.2 Handling uncertainty

5.7 Software tools

EXERCISES

References

Further Reading

Chapter 6 ▪ 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 Maximum likelihood 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

Chapter 7 ▪ 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

Part III Constraint-based Modelling

Chapter 8 ▪ 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 stoichiometric matrix

8.3 Steady-state mass balance: Flux Balance Analysis

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

Chapter 9 ▪ 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 ROOM vs. MoMA

9.3 Bi-level optimisation

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

Chapter 10 ▪ 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 Incorrect predictions

10.8 Troubleshooting

10.8.1 Interpreting gene deletion simulations

10.9 Software Tools

EXERCISES

References

Further Reading

Part IV Advanced Topics

Chapter 11 ▪ 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

Chapter 12 ▪ Designing biological circuits

12.1 What is synthetic biology?

12.1.1 From LEGO bricks to biobricks

12.2 Classic circuit design experiments

12.2.1 Designing an oscillator: The repressilator

12.2.2 Toggle switch

12.3 Designing modules

12.3.1 Exploring the design space

12.3.2 Systems-theoretic approaches

12.3.3 Automating circuit design

12.4 Design principles of biological networks

12.4.1 Redundancy

12.4.2 Modularity

12.4.3 Exaptation

12.4.4 Robustness

12.5 Computing with cells

12.5.1 Adleman’s classic experiment

12.5.2 Examples of circuits that can compute

12.5.3 DNA data storage

12.6 Challenges

12.7 Software Tools

EXERCISES

References

Further Reading

Chapter 13 ▪ 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

Chapter 14 ▪ Epilogue: The road ahead

Online-only appendices

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