Name: Stochastic Dynamics for Systems Biology (Chapman & Hall (en Inglés)
Price: 101390.00 CLP
Availability: InStock
Author: Christian Mazza; Michel Benaim
ISBN: 9781466514935

portada Stochastic Dynamics for Systems Biology (Chapman & Hall (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Christian Mazza; Michel Benaim

Año

2014

Idioma

Inglés

N° páginas

274

Encuadernación

Tapa Dura

ISBN13

9781466514935

N° edición

Categorías

Matemáticas aplicadas

Stochastic Dynamics for Systems Biology (Chapman & Hall (en Inglés)

Christian Mazza; Michel Benaim (Autor) · Crc Pr Inc · Tapa Dura

Libro Físico

$ 101.390

$ 168.990

Ahorras: $ 67.600

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Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "Stochastic Dynamics for Systems Biology (Chapman & Hall (en Inglés)"

Stochastic Dynamics for Systems Biology is one of the first books to provide a systematic study of the many stochastic models used in systems biology. The book shows how the mathematical models are used as technical tools for simulating biological processes and how the models lead to conceptual insights on the functioning of the cellular processing system. Most of the text should be accessible to scientists with basic knowledge in calculus and probability theory. The authors illustrate the relevant Markov chain theory using realistic models from systems biology, including signaling and metabolic pathways, phosphorylation processes, genetic switches, and transcription. A central part of the book presents an original and up-to-date treatment of cooperativity. The book defines classical indexes, such as the Hill coefficient, using notions from statistical mechanics. It explains why binding curves often have S-shapes and why cooperative behaviors can lead to ultrasensitive genetic switches. These notions are then used to model transcription rates. Examples cover the phage lambda genetic switch and eukaryotic gene expression. The book then presents a short course on dynamical systems and describes stochastic aspects of linear noise approximation. This mathematical framework enables the simplification of complex stochastic dynamics using Gaussian processes and nonlinear ODEs. Simple examples illustrate the technique in noise propagation in gene networks and the effects of network structures on multistability and gene expression noise levels. The last chapter provides up-to-date results on stochastic and deterministic mass action kinetics with applications to enzymatic biochemical reactions and metabolic pathways.