| Description |
1 online resource (xiii, 278 pages) |
| Contents |
880-01 Introduction -- Linear systems -- Nonlinear systems -- Negative feedback systems -- Positive feedback systems -- Model validation using robustness analysis -- Reverse engineering biomolecular networks -- Stochastic effects in biological control systems |
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880-01/(S Machine generated contents note: 1. Introduction -- 1.1. What is feedback control-- 1.2. Feedback control in biological systems -- 1.2.1. tryptophan operon feedback control system -- 1.2.2. polyamine feedback control system -- 1.2.3. heat shock feedback control system -- 1.3. Application of control theory to biological systems: A historical perspective -- References -- 2. Linear systems -- 2.1. Introduction -- 2.2. State-space models -- 2.3. Linear time-invariant systems and the frequency response -- 2.4. Fourier analysis -- 2.5. Transfer functions and the Laplace transform -- 2.6. Stability -- 2.7. Change of state variables and canonical representations -- 2.8. Characterising system dynamics in the time domain -- 2.9. Characterising system dynamics in the frequency domain -- 2.10. Block diagram representations of interconnected systems -- 2.11. Case Study I: Characterising the frequency dependence of osmo-adaptation in Saccharomyces cerevisiae -- 2.11.1. Introduction -- 2.11.2. Frequency domain analysis -- 2.11.3. Time domain analysis -- 2.12. Case Study II: Characterising the dynamics of the Dictyostelium external signal receptor network -- 2.12.1. Introduction -- 2.12.2. generic structure for ligand-receptor interaction networks -- 2.12.3. Structure of the ligand-receptor interaction network in aggregating Dictyostelium cells -- 2.12.4. Dynamic response of the ligand-receptor interaction network in Dictyostelium -- References -- 3. Nonlinear systems -- 3.1. Introduction -- 3.2. Equilibrium points -- 3.3. Linearisation around equilibrium points -- 3.4. Stability and regions of attractions -- 3.4.1. Lyapunov stability -- 3.4.2. Region of attraction -- 3.5. Optimisation methods for nonlinear systems -- 3.5.1. Local optimisation methods -- 3.5.2. Global optimisation methods -- 3.5.3. Linear matrix inequalities -- 3.6. Case Study III: Stability analysis of tumour dormancy equilibrium -- 3.6.1. Introduction -- 3.6.2. Model of cancer development -- 3.6.3. Stability of the equilibrium points -- 3.6.4. Checking inclusion in the region of attraction -- 3.6.5. Analysis of the tumour dormancy equilibrium -- 3.7. Case Study IV: Global optimisation of a model of the tryptophan control system against multiple experiment data -- 3.7.1. Introduction -- 3.7.2. Model of the tryptophan control system -- 3.7.3. Model analysis using global optimisation -- References -- 4. Negative feedback systems -- 4.1. Introduction -- 4.2. Stability of negative feedback systems -- 4.3. Performance of negative feedback systems -- 4.4. Fundamental tradeoffs with negative feedback -- 4.5. Case Study V: Analysis of stability and oscillations in the p53-Mdm2 feedback system -- 4.6. Case Study VI: Perfect adaptation via integral feedback control in bacterial chemotaxis -- 4.6.1. mathematical model of bacterial chemotaxis -- 4.6.2. Analysis of the perfect adaptation mechanism -- 4.6.3. Perfect adaptation requires demethylation of active only receptors -- References -- 5. Positive feedback systems -- 5.1. Introduction -- 5.2. Bifurcations, bistability and limit cycles -- 5.2.1. Bifurcations and bistability -- 5.2.2. Limit cycles -- 5.3. Monotone systems -- 5.4. Chemical reaction network theory -- 5.4.1. Preliminaries on reaction network structure -- 5.4.2. Networks of deficiency zero -- 5.4.3. Networks of deficiency one -- 5.5. Case Study VII: Positive feedback leads to multistability, bifurcations and hysteresis in a MAPK cascade -- 5.6. Case Study VIII: Coupled positive and negative feedback loops in the yeast galactose pathway -- References -- 6. Model validation using robustness analysis -- 6.1. Introduction -- 6.2. Robustness analysis tools for model validation -- 6.2.1. Bifurcation diagrams -- 6.2.2. Sensitivity analysis -- 6.2.3. μ-analysis -- 6.2.4. Optimisation-based robustness analysis -- 6.2.5. Sum-of-squares polynomials -- 6.2.6. Monte Carlo simulation -- 6.3. New robustness analysis tools for biological systems -- 6.4. Case Study IX: Validating models of cAMP oscillations in aggregating Dictyostelium cells -- 6.5. Case Study X: Validating models of the p53-Mdm2 System -- References -- 7. Reverse engineering biomolecular networks -- 7.1. Introduction -- 7.2. Inferring network interactions using linear models -- 7.2.1. Discrete-time vs continuous-time model -- 7.3. Least squares -- 7.3.1. Least squares for dynamical systems -- 7.3.2. Methods based on least squares regression -- 7.4. Exploiting prior knowledge -- 7.4.1. Network inference via LMI-based optimisation -- 7.4.2. MAX-PARSE: An algorithm for pruning a fully connected network according to maximum parsimony -- 7.4.3. CORE-Net: A network growth algorithm using preferential attachment -- 7.5. Dealing with measurement noise -- 7.5.1. Total least squares -- 7.5.2. Constrained total least squares -- 7.6. Exploiting time-varying models -- 7.7. Case Study XI: Inferring regulatory interactions in the innate immune system from noisy measurements -- 7.8. Case Study XII: Reverse engineering a cell cycle regulatory subnetwork of Saccharomyces cerevisiae from experimental microarray data -- 7.8.1. PACTLS: An algorithm for reverse engineering partially known networks from noisy data -- 7.8.2. Results -- References -- 8. Stochastic effects in biological control systems -- 8.1. Introduction -- 8.2. Stochastic modelling and simulation -- 8.3. framework for analysing the effect of stochastic noise on stability -- 8.3.1. effective stability approximation -- 8.3.2. computationally efficient approximation of the dominant stochastic perturbation -- 8.3.3. Analysis using the Nyquist stability criterion -- 8.4. Case Study XIII: Stochastic effects on the stability of cAMP oscillations in aggregating Dictyostelium cells -- 8.5. Case Study XIV: Stochastic effects on the robustness of cAMP oscillations in aggregating Dictyostelium cells -- References |
| Summary |
Feedback Control in Systems Biology |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record |
| Subject |
Feedback control systems.
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Biological systems.
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Systems biology.
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Biological models.
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Systems Biology
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Feedback, Physiological
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Models, Biological
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MEDICAL -- Histology.
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Systems biology
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Biological models
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Feedback control systems
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Biological systems
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| Form |
Electronic book
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| Author |
Bates, Declan.
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| ISBN |
9781439816912 |
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1439816913 |
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1283311534 |
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9781283311533 |
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9781466533400 |
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1466533404 |
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0429093314 |
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9780429093319 |
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9786613311535 |
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6613311537 |
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