Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology [electronic resource] /
Holcman, David.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.
Includes bibliographical references at the end of each chapters and index.
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.
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.
Biomathematics.
Systems biology.
Biological systems.
Statistical physics.
Physiological, Cellular and Medical Topics.
Systems Biology.
Applications of Nonlinear Dynamics and Chaos Theory.
Systems Biology.