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Stochastic processes, multiscale mod...

Holcman, David.

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Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology/ edited by David Holcman.
other author:	Holcman, David.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xiii,377 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computational biology. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-62627-7
ISBN:	9783319626277

Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology
Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology[electronic resource] /edited by David Holcman. - Cham :Springer International Publishing :2017. - xiii,377 p. :ill., digital ;24 cm.

This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected chapters will review fundamental and current topics and approaches to dynamical systems in cellular biology. This text aims to develop improved mathematical and computational methods with which to study biological processes. At the scale of a single cell, stochasticity becomes important due to low copy numbers of biological molecules, such as mRNA and proteins that take part in biochemical reactions driving cellular processes. When trying to describe such biological processes, the traditional deterministic models are often inadequate, precisely because of these low copy numbers. This book presents stochastic models, which are necessary to account for small particle numbers and extrinsic noise sources. The complexity of these models depend upon whether the biochemical reactions are diffusion-limited or reaction-limited. In the former case, one needs to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equations. Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology will appeal to graduate students and researchers in the fields of applied mathematics, biophysics, and cellular biology.

ISBN: 9783319626277

Standard No.: 10.1007/978-3-319-62627-7doiSubjects--Topical Terms:

590653
Computational biology.

LC Class. No.: QH324.2 / .S76 2017

Dewey Class. No.: 570.285

Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology
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245 0 0 $a Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology $h [electronic resource] / $c edited by David Holcman.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xiii,377 p. : $b ill., digital ; $c 24 cm.
520 $a This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected chapters will review fundamental and current topics and approaches to dynamical systems in cellular biology. This text aims to develop improved mathematical and computational methods with which to study biological processes. At the scale of a single cell, stochasticity becomes important due to low copy numbers of biological molecules, such as mRNA and proteins that take part in biochemical reactions driving cellular processes. When trying to describe such biological processes, the traditional deterministic models are often inadequate, precisely because of these low copy numbers. This book presents stochastic models, which are necessary to account for small particle numbers and extrinsic noise sources. The complexity of these models depend upon whether the biochemical reactions are diffusion-limited or reaction-limited. In the former case, one needs to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equations. Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology will appeal to graduate students and researchers in the fields of applied mathematics, biophysics, and cellular biology.
650 0 $a Computational biology. $3 590653
650 0 $a Stochastic processes. $3 520663
650 1 4 $a Mathematics. $3 515831
650 2 4 $a Physiological, Cellular and Medical Topics. $3 891099
650 2 4 $a Systems Biology. $3 928020
650 2 4 $a Applications of Nonlinear Dynamics and Chaos Theory. $3 3134772
700 1 $a Holcman, David. $3 2163213
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-62627-7
950 $a Mathematics and Statistics (Springer-11649)