Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology / David Holcman, editor.
Holcman, David, editor.
2017
QH324.2 .S76 2017e
Available Online

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Title Stochastic processes, multiscale modeling, and numerical methods for computational cellular biology / David Holcman, editor.
ISBN 9783319626277 (electronic book)
3319626272 (electronic book)
9783319626260
3319626264
DOI https://doi.org/10.1007/978-3-319-62627-7
Published Cham, Switzerland : Springer Nature, [2017]
Language English
Description 1 online resource (xiii, 377 pages) : color illustrations
Item Number 10.1007/978-3-319-62627-7 doi
Call Number QH324.2 .S76 2017e
Dewey Decimal Classification 570.285
510
Summary "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."--Provided by publisher.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from digital title page (viewed November 20, 2017).
Added Author Holcman, David, editor.
Available in Other Form Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology.
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Part I: Stochastic Chemical Reactions
Test Models for Statistical Inference: Two-Dimensional Reaction Systems Displaying Limit Cycle Bifurcations and Bistability
Importance Sampling for Metastable and Multiscale Dynamical Systems
Multiscale Simulation of Stochastic Reaction-diffusion Networks
Part II: Stochastic Numerical Approaches, Algorithms and Coarse-Grained Simulations
Numerical Methods for Ergodic SDEs: When Stochastic Integration Meets Geometric Integration
Stability and Strong Convergence for Spatial Stochastic Kinetics
The T cells in an Ageing Virtual Mouse
Part III: Analysis of Stochastic Dynamical Systems for Modeling Cell Biology
Model reduction for Stochastic Reaction Systems
ZI-closure Scheme: A Method to Solve and Study Stochastic Reaction Networks
Deterministic and Stochastic Becker-Döring Equations: Past and Recent Mathematical Developments
Coagulation-Fragmentation with a Finite Number of Particles: Models, Stochastic Analysis and Applications to Telomere Clustering and Viral Capsid Assembly
A Review of Stochastic and Delay Simulation Approaches in both Time and Space in Computational Cell Biology
Part IV: Diffusion Processes and Stochastic Modeling
Recent Mathematical Models of Axonal Transport
Stochastic Models for Evolving Cellular Populations of Mitochondria: Disease, Development, and Ageing
Modeling and Stochastic Analysis of the Single Photon Response
A Phenomenological Spatial Model for Macro-ecological Patterns in Species-rich Ecosystems.

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