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Stochastic Processes In Cell Biology 1st Edition Paul C Bressloff

  • SKU: BELL-4931704
Stochastic Processes In Cell Biology 1st Edition Paul C Bressloff
$ 31.00 $ 45.00 (-31%)

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Stochastic Processes In Cell Biology 1st Edition Paul C Bressloff instant download after payment.

Publisher: Springer, Springer International Publishing Switzerland
File Extension: PDF
File size: 13.43 MB
Pages: 688
Author: Paul C. Bressloff
ISBN: 9783319084879, 9783319084886, 9783319354941, 3319084879, 3319084887, 3319354949, B00PUM05XA
Language: English
Year: 2014
Edition: 1
Volume: 41

Product desciption

Stochastic Processes In Cell Biology 1st Edition Paul C Bressloff by Paul C. Bressloff 9783319084879, 9783319084886, 9783319354941, 3319084879, 3319084887, 3319354949, B00PUM05XA instant download after payment.

Audience: • Mathematically sophisticated

Main subject categories: • Applied mathematics • Cell biology • Molecular biology

This book develops the theory of continuous and discrete stochastic processes within the context of cell biology. A wide range of biological topics are covered including normal and anomalous diffusion in complex cellular environments, stochastic ion channels and excitable systems, stochastic calcium signaling, molecular motors, intracellular transport, signal transduction, bacterial chemotaxis, robustness in gene networks, genetic switches and oscillators, cell polarization, polymerization, cellular length control, and branching processes. The book also provides a pedagogical introduction to the theory of stochastic process – Fokker Planck equations, stochastic differential equations, master equations and jump Markov processes, diffusion approximations and the system size expansion, first passage time problems, stochastic hybrid systems, reaction-diffusion equations, exclusion processes, WKB methods, martingales and branching processes, stochastic calculus, and numerical methods. This text is primarily aimed at graduate students and researchers working in mathematical biology and applied mathematicians interested in stochastic modeling. Applied probabilists and theoretical physicists should also find it of interest. It assumes no prior background in statistical physics and introduces concepts in stochastic processes via motivating biological applications. The book is highly illustrated and contains a large number of examples and exercises that further develop the models and ideas in the body of the text. It is based on a course that the author has taught at the University of Utah for many years.

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