Limit search to available items
Book Cover
E-book
Author Cosentino, Carlo.

Title Feedback control in systems biology / Carlo Cosentino, Declan Bates
Published Boca Raton : CRC Press, 2012
Online access available from:
BIOSCIENCEnetBASE    View Resource Record  
ProQuest Ebook Central Subscription    View Resource Record  

Copies

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
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
Print version record
Subject Feedback control systems.
Biological systems.
Systems biology.
Biological models.
Systems Biology
Feedback, Physiological
Models, Biological
MEDICAL -- Histology.
Feedback control systems.
Biological systems.
Form Electronic book
Author Bates, Declan.
ISBN 9781439816912
1439816913
1283311534
9781283311533
9781466533400
1466533404
0429093314
9780429093319
9786613311535
6613311537