Stochastic Biomathematical Models

Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models and model-based analysis of data. The key topics of modern biomathematics are covered: ecology, epidemiology, biochemistry, regulatory networks, neuronal networks and population genetics. A variety of mathematical methods are introduced, ranging from ordinary and partial differential equations to stochastic graph theory and branching processes. A special emphasis is placed on the interplay between stochastic and deterministic models.

A stray dog is constantly frustrated in her quest for a bone. Turning the narrower pages reveals hidden portions of the illustrations.

This is a new book in biomathematics, which includes new models of stochastic non-linear biological systems and new results for these systems. These results are based on the new results for non-linear difference and differential equations in random media. This book contains: -New stochastic non-linear models of biological systems, such as biological systems in random media: epidemic, genetic selection, demography, branching, logistic growth and predator-prey models; -New results for scalar and vector difference equations in random media with applications to the stochastic biological systems in 1); -New results for stochastic non-linear biological systems, such as averaging, merging, diffusion approximation, normal deviations and stability; -New approach to the study of stochastic biological systems in random media such as random evolution approach.

This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. Balancing theory and applications, the authors use stochastic methods and concrete examples to model real-world problems from engineering, biomathematics, biotechnology, and finance. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. The book will be of interest to students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, physics, and engineering.

This book on modelling and simulation in biomathematics will be invaluable to researchers who are interested in the emerging areas of the field. Graduate students in related areas as well as lecturers will also find it beneficial. Some of the chapters have been written by distinguished experts in the field. Sample Chapter(s). Chapter 1: Detecting Mosaic Structures in DNA Sequence Alignments (1,349 KB). Contents: Detecting Mosaic Structures in DNA Sequence Alignments (D Husmeier); Application of Statistical Methodology and Model Design to Socio-Behaviour of HIV Transmission (J Oluwoye); A Stochastic Model Incorporating HIV Treatments for a Heterosexual Population: Impact on Threshold Conditions (R J Gallop et al.); Modeling and Identification of the Dynamics of the MF-Influenced Free-Radical Transformations in Lipid-Modeling Substances and Lipids (J Bentsman et al.); Computer Simulation of Self-Reorganization in Biological Cells (D Greenspan); Modelling Biological Gel Contraction by Cells: Consequences of Cell Traction Forces Distribution and Initial Stress (S Ramtani); Peristaltic Transport of Physiological Fluids (J C Misra & S K Pandey); Mathematical Modelling of DNA Knots and Links (J C Misra & S Mukherjee); Using Monodomain Computer Models for the Simulation of Electric Fields During Excitation Spread in Cardiac Tissue (G Plank); Flow in Tubes with Complicated Geometries with Special Application to Blood Flow in Large Arteries (G Jayaraman); Mathematical Modeling in Reproductive Biomedicine (S Sharma & S K Guha); Image Theory and Applications in Bioelectromagnetics (P D Einziger et al.); Dynamics of Humanoid Robots: Geometrical and Topological Duality (V G Ivancevic); The Effects of Body Composition on Energy Expenditure and Weight Dynamics During Hypophagia: A Setpoint Analysis (F P Kozusko); Mathematical Models in Population Dynamics and Ecology (R Diluo); Modelling in Bone Biomechanics (J C Misra & S Samanta). Readership: Graduate students, academic and researchers in biomathematics, mathematical biology, mathematical modeling, biotechnology, biocomputing, biophysics, bioengineering and mechanics."

This volume is a revised and enlarged version of Chapter 3 of. a book with the same title, published in Romanian in 1968. The revision resulted in a new book which has been divided into two of the large amount of new material. The whole book parts because is intended to introduce mathematicians and biologists with a strong mathematical background to the study of stochastic processes and their applications in biological sciences. It is meant to serve both as a textbook and a survey of recent developments. Biology studies complex situations and therefore needs skilful methods of abstraction. Stochastic models, being both vigorous in their specification and flexible in their manipulation, are the most suitable tools for studying such situations. This circumstance deter mined the writing of this volume which represents a comprehensive cross section of modern biological problems on the theory of stochastic processes. Because of the way some specific problems have been treat ed, this volume may also be useful to research scientists in any other field of science, interested in the possibilities and results of stochastic modelling. To understand the material presented, the reader needs to be acquainted with probability theory, as given in a sound introductory course, and be capable of abstraction.

Vol. 2.