Stochastic Modelling In Biology Relevant Mathematical Concepts And Recent Applications PDF Download
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| Author | : Tautu Petre |
| Publisher | : #N/A |
| Total Pages | : 456 |
| Release | : 1990-12-05 |
| Genre | : |
| ISBN | : 9814611921 |
Download Stochastic Modelling In Biology: Relevant Mathematical Concepts And Recent Applications Book in PDF, ePub and Kindle
These proceedings focus on future prospects as well as on the present status in some important areas of applied probability and mathematical biology. Some papers have educational intentions regarding the mathematical modelling of special biological situations. The workshop was the third one in Heidelberg dealing with stochastic modelling in biology, e.g., cell biology, embryology, oncology, epidemiology and genetics.
| Author | : Petre Tăutu |
| Publisher | : |
| Total Pages | : 458 |
| Release | : 1990 |
| Genre | : SCIENCE |
| ISBN | : 9789814540711 |
Download Workshop on Stochastic Modelling in Biology: Relevant Mathematical Concepts and Recent Applications, Heidelberg, Federal Republic of Germany 8-12 August, 1988 Book in PDF, ePub and Kindle
| Author | : Petre Tăutu |
| Publisher | : World Scientific Publishing Company |
| Total Pages | : 464 |
| Release | : 1990 |
| Genre | : Science |
| ISBN | : |
Download Workshop on Stochastic Modelling in Biology: Relevant Mathematical Concepts and Recent Applications, Heidelberg, August 8-12, 1988 Book in PDF, ePub and Kindle
These proceedings focus on future prospects as well as on the present status in some important areas of applied probability and mathematical biology. Some papers have educational intentions regarding the mathematical modelling of special biological situations. The workshop was the third one in Heidelberg dealing with stochastic modelling in biology, e.g., cell biology, embryology, oncology, epidemiology and genetics.
| Author | : Mostafa Bachar |
| Publisher | : Springer |
| Total Pages | : 216 |
| Release | : 2012-10-19 |
| Genre | : Mathematics |
| ISBN | : 3642321577 |
Download Stochastic Biomathematical Models Book in PDF, ePub and Kindle
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.
| Author | : Darren J. Wilkinson |
| Publisher | : CRC Press |
| Total Pages | : 296 |
| Release | : 2006-04-18 |
| Genre | : Mathematics |
| ISBN | : 9781584885405 |
Download Stochastic Modelling for Systems Biology Book in PDF, ePub and Kindle
Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective. Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications. While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.
| Author | : Mukhtar Ullah |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 319 |
| Release | : 2011-07-12 |
| Genre | : Mathematics |
| ISBN | : 1461404789 |
Download Stochastic Approaches for Systems Biology Book in PDF, ePub and Kindle
This textbook focuses on stochastic analysis in systems biology containing both the theory and application. While the authors provide a review of probability and random variables, subsequent notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of stochastic framework for modeling subcellular biochemical systems. In particular, the authors make an effort to show how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property. The text contains many illustrations, examples and exercises to illustrate the ideas and methods that are introduced. Matlab code is also provided where appropriate. Additionally, the cell cycle is introduced as a more complex case study. Senior undergraduate and graduate students in mathematics and physics as well as researchers working in the area of systems biology, bioinformatics and related areas will find this text useful.
| Author | : Darren J. Wilkinson |
| Publisher | : CRC Press |
| Total Pages | : 365 |
| Release | : 2011-11-09 |
| Genre | : Mathematics |
| ISBN | : 1439837724 |
Download Stochastic Modelling for Systems Biology, Second Edition Book in PDF, ePub and Kindle
Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. New in the Second Edition All examples have been updated to Systems Biology Markup Language Level 3 All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation Simple modelling of "extrinsic" and "intrinsic" noise An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
| Author | : Marius Iosifescu |
| Publisher | : |
| Total Pages | : 340 |
| Release | : 1973 |
| Genre | : Mathematics |
| ISBN | : |
Download Stochastic Processes and Applications in Biology and Medicine: Theory Book in PDF, ePub and Kindle
A stray dog is constantly frustrated in her quest for a bone. Turning the narrower pages reveals hidden portions of the illustrations.
| Author | : Darren J. Wilkinson |
| Publisher | : CRC Press |
| Total Pages | : 292 |
| Release | : 2018-12-07 |
| Genre | : Mathematics |
| ISBN | : 1351000896 |
Download Stochastic Modelling for Systems Biology, Third Edition Book in PDF, ePub and Kindle
Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
| Author | : Wan Frederic Y M |
| Publisher | : World Scientific |
| Total Pages | : 476 |
| Release | : 2019-08-29 |
| Genre | : Mathematics |
| ISBN | : 981327462X |
Download Stochastic Models In The Life Sciences And Their Methods Of Analysis Book in PDF, ePub and Kindle
Biological processes are evolutionary in nature and often evolve in a noisy environment or in the presence of uncertainty. Such evolving phenomena are necessarily modeled mathematically by stochastic differential/difference equations (SDE), which have been recognized as essential for a true understanding of many biological phenomena. Yet, there is a dearth of teaching material in this area for interested students and researchers, notwithstanding the addition of some recent texts on stochastic modelling in the life sciences. The reason may well be the demanding mathematical pre-requisites needed to 'solve' SDE.A principal goal of this volume is to provide a working knowledge of SDE based on the premise that familiarity with the basic elements of a stochastic calculus for random processes is unavoidable. Through some SDE models of familiar biological phenomena, we show how stochastic methods developed for other areas of science and engineering are also useful in the life sciences. In the process, the volume introduces to biologists a collection of analytical and computational methods for research and applications in this emerging area of life science. The additions broaden the available tools for SDE models for biologists that have been limited by and large to stochastic simulations.
