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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.
Auteur
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.
Texte du rabat
*"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."
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.
Contenu
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 efects 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 stif equations
5.6.2 Handling uncertainty
5.7 SOFTWARE TOOLS
EXERCISES
REFERENCES
FURTHER READING
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