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Feedback control in systems biology /
Feedback Control in Systems Biology.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
2012.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Cosentino, Carlo. | |
| 245 | 1 | 0 | |a Feedback control in systems biology / |c Carlo Cosentino, Declan Bates. |
| 260 | |a Boca Raton : |b CRC Press, |c 2012. | ||
| 300 | |a 1 online resource (xiii, 278 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a 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. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a Feedback Control in Systems Biology. | ||
| 546 | |a English. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Feedback control systems. | |
| 650 | 0 | |a Biological systems. | |
| 650 | 0 | |a Systems biology. | |
| 650 | 0 | |a Biological models. | |
| 650 | 1 | 2 | |a Systems Biology |
| 650 | 2 | 2 | |a Feedback, Physiological |
| 650 | 2 | 2 | |a Models, Biological |
| 650 | 4 | |a Feedback control systems. | |
| 650 | 4 | |a Systems biology. | |
| 650 | 4 | |a Biological models. | |
| 650 | 6 | |a Systèmes à réaction. | |
| 650 | 6 | |a Systèmes biologiques. | |
| 650 | 6 | |a Biologie systémique. | |
| 650 | 6 | |a Modèles biologiques. | |
| 650 | 7 | |a MEDICAL |x Histology. |2 bisacsh | |
| 650 | 7 | |a Systems biology |2 fast | |
| 650 | 7 | |a Biological models |2 fast | |
| 650 | 7 | |a Feedback control systems |2 fast | |
| 650 | 7 | |a Biological systems |2 fast | |
| 700 | 1 | |a Bates, Declan. | |
| 776 | 0 | 8 | |i Print version: |a Cosentino, Carlo. |t Feedback control in systems biology. |d Boca Raton : CRC Press, 2012 |z 9781439816905 |w (DLC) 2011036516 |w (OCoLC)748764434 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781439816912/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t What is feedback control-- |g 1.2. |t Feedback control in biological systems -- |g 1.2.1. |t tryptophan operon feedback control system -- |g 1.2.2. |t polyamine feedback control system -- |g 1.2.3. |t heat shock feedback control system -- |g 1.3. |t Application of control theory to biological systems: A historical perspective -- |t References -- |g 2. |t Linear systems -- |g 2.1. |t Introduction -- |g 2.2. |t State-space models -- |g 2.3. |t Linear time-invariant systems and the frequency response -- |g 2.4. |t Fourier analysis -- |g 2.5. |t Transfer functions and the Laplace transform -- |g 2.6. |t Stability -- |g 2.7. |t Change of state variables and canonical representations -- |g 2.8. |t Characterising system dynamics in the time domain -- |g 2.9. |t Characterising system dynamics in the frequency domain -- |g 2.10. |t Block diagram representations of interconnected systems -- |g 2.11. |t Case Study I: Characterising the frequency dependence of osmo-adaptation in Saccharomyces cerevisiae -- |g 2.11.1. |t Introduction -- |g 2.11.2. |t Frequency domain analysis -- |g 2.11.3. |t Time domain analysis -- |g 2.12. |t Case Study II: Characterising the dynamics of the Dictyostelium external signal receptor network -- |g 2.12.1. |t Introduction -- |g 2.12.2. |t generic structure for ligand-receptor interaction networks -- |g 2.12.3. |t Structure of the ligand-receptor interaction network in aggregating Dictyostelium cells -- |g 2.12.4. |t Dynamic response of the ligand-receptor interaction network in Dictyostelium -- |t References -- |g 3. |t Nonlinear systems -- |g 3.1. |t Introduction -- |g 3.2. |t Equilibrium points -- |g 3.3. |t Linearisation around equilibrium points -- |g 3.4. |t Stability and regions of attractions -- |g 3.4.1. |t Lyapunov stability -- |g 3.4.2. |t Region of attraction -- |g 3.5. |t Optimisation methods for nonlinear systems -- |g 3.5.1. |t Local optimisation methods -- |g 3.5.2. |t Global optimisation methods -- |g 3.5.3. |t Linear matrix inequalities -- |g 3.6. |t Case Study III: Stability analysis of tumour dormancy equilibrium -- |g 3.6.1. |t Introduction -- |g 3.6.2. |t Model of cancer development -- |g 3.6.3. |t Stability of the equilibrium points -- |g 3.6.4. |t Checking inclusion in the region of attraction -- |g 3.6.5. |t Analysis of the tumour dormancy equilibrium -- |g 3.7. |t Case Study IV: Global optimisation of a model of the tryptophan control system against multiple experiment data -- |g 3.7.1. |t Introduction -- |g 3.7.2. |t Model of the tryptophan control system -- |g 3.7.3. |t Model analysis using global optimisation -- |t References -- |g 4. |t Negative feedback systems -- |g 4.1. |t Introduction -- |g 4.2. |t Stability of negative feedback systems -- |g 4.3. |t Performance of negative feedback systems -- |g 4.4. |t Fundamental tradeoffs with negative feedback -- |g 4.5. |t Case Study V: Analysis of stability and oscillations in the p53-Mdm2 feedback system -- |g 4.6. |t Case Study VI: Perfect adaptation via integral feedback control in bacterial chemotaxis -- |g 4.6.1. |t mathematical model of bacterial chemotaxis -- |g 4.6.2. |t Analysis of the perfect adaptation mechanism -- |g 4.6.3. |t Perfect adaptation requires demethylation of active only receptors -- |t References -- |g 5. |t Positive feedback systems -- |g 5.1. |t Introduction -- |g 5.2. |t Bifurcations, bistability and limit cycles -- |g 5.2.1. |t Bifurcations and bistability -- |g 5.2.2. |t Limit cycles -- |g 5.3. |t Monotone systems -- |g 5.4. |t Chemical reaction network theory -- |g 5.4.1. |t Preliminaries on reaction network structure -- |g 5.4.2. |t Networks of deficiency zero -- |g 5.4.3. |t Networks of deficiency one -- |g 5.5. |t Case Study VII: Positive feedback leads to multistability, bifurcations and hysteresis in a MAPK cascade -- |g 5.6. |t Case Study VIII: Coupled positive and negative feedback loops in the yeast galactose pathway -- |t References -- |g 6. |t Model validation using robustness analysis -- |g 6.1. |t Introduction -- |g 6.2. |t Robustness analysis tools for model validation -- |g 6.2.1. |t Bifurcation diagrams -- |g 6.2.2. |t Sensitivity analysis -- |g 6.2.3. |t μ-analysis -- |g 6.2.4. |t Optimisation-based robustness analysis -- |g 6.2.5. |t Sum-of-squares polynomials -- |g 6.2.6. |t Monte Carlo simulation -- |g 6.3. |t New robustness analysis tools for biological systems -- |g 6.4. |t Case Study IX: Validating models of cAMP oscillations in aggregating Dictyostelium cells -- |g 6.5. |t Case Study X: Validating models of the p53-Mdm2 System -- |t References -- |g 7. |t Reverse engineering biomolecular networks -- |g 7.1. |t Introduction -- |g 7.2. |t Inferring network interactions using linear models -- |g 7.2.1. |t Discrete-time vs continuous-time model -- |g 7.3. |t Least squares -- |g 7.3.1. |t Least squares for dynamical systems -- |g 7.3.2. |t Methods based on least squares regression -- |g 7.4. |t Exploiting prior knowledge -- |g 7.4.1. |t Network inference via LMI-based optimisation -- |g 7.4.2. |t MAX-PARSE: An algorithm for pruning a fully connected network according to maximum parsimony -- |g 7.4.3. |t CORE-Net: A network growth algorithm using preferential attachment -- |g 7.5. |t Dealing with measurement noise -- |g 7.5.1. |t Total least squares -- |g 7.5.2. |t Constrained total least squares -- |g 7.6. |t Exploiting time-varying models -- |g 7.7. |t Case Study XI: Inferring regulatory interactions in the innate immune system from noisy measurements -- |g 7.8. |t Case Study XII: Reverse engineering a cell cycle regulatory subnetwork of Saccharomyces cerevisiae from experimental microarray data -- |g 7.8.1. |t PACTLS: An algorithm for reverse engineering partially known networks from noisy data -- |g 7.8.2. |t Results -- |t References -- |g 8. |t Stochastic effects in biological control systems -- |g 8.1. |t Introduction -- |g 8.2. |t Stochastic modelling and simulation -- |g 8.3. |t framework for analysing the effect of stochastic noise on stability -- |g 8.3.1. |t effective stability approximation -- |g 8.3.2. |t computationally efficient approximation of the dominant stochastic perturbation -- |g 8.3.3. |t Analysis using the Nyquist stability criterion -- |g 8.4. |t Case Study XIII: Stochastic effects on the stability of cAMP oscillations in aggregating Dictyostelium cells -- |g 8.5. |t Case Study XIV: Stochastic effects on the robustness of cAMP oscillations in aggregating Dictyostelium cells -- |t References. |
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