Stochastic Modelling for Systems BiologyCRC Press, 18.04.2006 - 280 Seiten Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective. Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications. While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source. |
Inhalt
I | 11 |
II | 11 |
IV | 11 |
V | 11 |
VI | 16 |
VII | 17 |
IX | 19 |
X | 21 |
XLIII | 137 |
XLIV | 139 |
XLV | 145 |
XLVI | 147 |
XLVII | 149 |
XLVIII | 150 |
XLIX | 153 |
L | 157 |
XI | 31 |
XII | 36 |
XIII | 42 |
XIV | 43 |
XV | 45 |
XVI | 56 |
XVII | 64 |
XIX | 65 |
XX | 67 |
XXI | 70 |
XXII | 75 |
XXIII | 77 |
XXIV | 82 |
XXV | 86 |
XXVI | 88 |
XXVII | 89 |
XXVIII | 91 |
XXIX | 92 |
XXX | 93 |
XXXI | 97 |
XXXII | 98 |
XXXIII | 101 |
XXXV | 106 |
XXXVI | 107 |
XXXVII | 108 |
XXXVIII | 109 |
XXXIX | 115 |
XL | 121 |
XLI | 133 |
XLII | 135 |
LI | 160 |
LII | 161 |
LIV | 163 |
LV | 168 |
LVI | 172 |
LVII | 176 |
LVIII | 178 |
LIX | 179 |
LX | 181 |
LXI | 186 |
LXII | 190 |
LXIII | 196 |
LXV | 196 |
LXVI | 200 |
LXVII | 210 |
LXVIII | 214 |
LXX | 215 |
LXXI | 217 |
LXXIII | 221 |
LXXIV | 228 |
LXXV | 232 |
LXXVII | 233 |
LXXVIII | 235 |
LXXIX | 237 |
LXXX | 240 |
LXXXI | 241 |
LXXXII | 245 |
LXXXIII | 249 |
Andere Ausgaben - Alle anzeigen
Stochastic Modelling for Systems Biology, Third Edition Darren J. Wilkinson Eingeschränkte Leseprobe - 2018 |
Stochastic Modelling for Systems Biology, Third Edition Darren J. Wilkinson Eingeschränkte Leseprobe - 2018 |
Häufige Begriffe und Wortgruppen
approximation auto-regulatory Bayes Theorem Bayesian inference biochemical networks Cell Chapter compartment compartment="Cell compute consider context continuous deterministic corresponding denoted density detailed diffusion dimerisation kinetics discrete stochastic models dynamics equation example exponential finite function fx(x gamma gene Gibbs sampler Gillespie algorithm given in Figure hazard independent Initialise integration interval kineticLaw lac operon lactose likelihood linear listOfParameters listOfProducts listOfReactants Markov chain Markov process math MCMC algorithm method normal random quantity Note number of molecules parameters Petri net Petri nets plot Poisson process posterior distribution probability distribution problem Proposition protein random variable rate constants rate laws represents reversible="false RNAP sample path sample space SBML SBML-shorthand Section simple simulated realisation solution species speciesReference stationary distribution statistics stochastic kinetic models stochastic process stochastic rate stochastic simulation straightforward systems biology techniques timestep tion transcription transition kernel update variance vector zero
Beliebte Passagen
Seite 5 - Last, but by no means least, I would like to thank Cindy K.
Verweise auf dieses Buch
Chemical Biophysics: Quantitative Analysis of Cellular Systems Daniel A. Beard,Hong Qian Keine Leseprobe verfügbar - 2008 |