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Raman

An Introduction to Computational Systems Biology

Systems-Level Modelling of Cellular Networks

Medium: Buch
ISBN: 978-1-138-59732-7
Verlag: Taylor & Francis Ltd (Sales)
Erscheinungstermin: 31.05.2021
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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.

Produkteigenschaften

  • Artikelnummer: 9781138597327
  • Medium: Buch
  • ISBN: 978-1-138-59732-7
  • Verlag: Taylor & Francis Ltd (Sales)
  • Erscheinungstermin: 31.05.2021
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2021
  • Serie: Chapman & Hall/CRC Computational Biology Series
  • Produktform: Gebunden
  • Gewicht: 658 g
  • Seiten: 358
  • Format (B x H x T): 155 x 239 x 23 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

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 maximisatio