An introduction to computational systems biology : systems-level modelling of cellular networks /: systems-level modelling of cellular networks. (2021)

• Record Type: Book
• Further Information: Note: Karthik Raman.
• Authors: Raman, Karthik
• Contents: 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 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 <br /&g
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2021
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 570.113; Systems biology; Biological systems -- Computer simulation; Computational biology
• Languages: English
• ISBNs: 9780429944512; 9780429944529; 9780429486951
• Related ISBNs: 9781138597327
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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