Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology

Holcman, David (Edited by)

ISBN 10: 3319626264 / ISBN 13: 9783319626260

Verlag: Springer, 2017

Beschreibung

377 pages. 9.25x6.25x1.00 inches. In Stock. Bestandsnummer des Verkäufers x-3319626264

Bibliografische Details

Titel: Stochastic Processes, Multiscale Modeling, ...

Verlag: Springer

Erscheinungsdatum: 2017

Einband: Hardcover

Zustand: Brand New

Über diesen Titel

Inhaltsangabe:

<P>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.</P><P>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.</P><P> </P><P><I>STOCHASTIC PROCESSES, MULTISCALE MODELING, AND NUMERICAL METHODS FOR COMPUTATIONAL CELLULAR BIOLOGY</I> WILL APPEAL TO GRADUATE STUDENTS AND RESEARCHERS IN THE FIELDS OF APPLIED MATHEMATICS, BIOPHYSICS, AND CELLULAR BIOLOGY.</P>

Reseña del editor:

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.