Deterministic Versus Stochastic Modelling In Biochemistry And Systems Biology

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Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology

Paola Lecca,Ian Laurenzi,Ferenc Jordan
Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology Book PDF

Author : Paola Lecca,Ian Laurenzi,Ferenc Jordan
Publisher : Elsevier
Page : 411 pages
File Size : 49,7 Mb
Release : 2013-04-09
Category : Mathematics
ISBN : 9781908818218

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Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology by Paola Lecca,Ian Laurenzi,Ferenc Jordan Pdf

Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics. Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examples Presents recently developed discrete stochastic formalisms for modelling biological systems and processes Describes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics

Stochastic Modelling for Systems Biology, Third Edition

Darren J. Wilkinson
Stochastic Modelling for Systems Biology Third Edition Book PDF

Author : Darren J. Wilkinson
Publisher : CRC Press
Page : 292 pages
File Size : 53,8 Mb
Release : 2018-12-07
Category : Mathematics
ISBN : 9781351000895

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Stochastic Modelling for Systems Biology, Third Edition by Darren J. Wilkinson Pdf

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.

Stochastic Modelling for Systems Biology, Second Edition

Darren J. Wilkinson
Stochastic Modelling for Systems Biology Second Edition Book PDF

Author : Darren J. Wilkinson
Publisher : CRC Press
Page : 365 pages
File Size : 53,9 Mb
Release : 2011-11-09
Category : Mathematics
ISBN : 9781439837726

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Stochastic Modelling for Systems Biology, Second Edition by Darren J. Wilkinson Pdf

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.

Stochastic Approaches for Systems Biology

Mukhtar Ullah,Olaf Wolkenhauer
Stochastic Approaches for Systems Biology Book PDF

Author : Mukhtar Ullah,Olaf Wolkenhauer
Publisher : Springer Science & Business Media
Page : 290 pages
File Size : 47,5 Mb
Release : 2011-07-15
Category : Mathematics
ISBN : 9781461404781

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Stochastic Approaches for Systems Biology by Mukhtar Ullah,Olaf Wolkenhauer Pdf

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.

Stochastic Models in Biology

Narendra S. Goel,Nira Richter-Dyn
Stochastic Models in Biology Book PDF

Author : Narendra S. Goel,Nira Richter-Dyn
Publisher : Elsevier
Page : 282 pages
File Size : 46,6 Mb
Release : 2016-01-26
Category : Science
ISBN : 9781483278100

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Stochastic Models in Biology by Narendra S. Goel,Nira Richter-Dyn Pdf

Stochastic Models in Biology describes the usefulness of the theory of stochastic process in studying biological phenomena. The book describes analysis of biological systems and experiments though probabilistic models rather than deterministic methods. The text reviews the mathematical analyses for modeling different biological systems such as the random processes continuous in time and discrete in state space. The book also discusses population growth and extinction through Malthus' law and the work of MacArthur and Wilson. The text then explains the dynamics of a population of interacting species. The book also addresses population genetics under systematic evolutionary pressures known as deterministic equations and genetic changes in a finite population known as stochastic equations. The text then turns to stochastic modeling of biological systems at the molecular level, particularly the kinetics of biochemical reactions. The book also presents various useful equations such as the differential equation for generating functions for birth and death processes. The text can prove valuable for biochemists, cellular biologists, and researchers in the medical and chemical field who are tasked to perform data analysis.

Stochastic Modelling for Systems Biology

Darren James Wilkinson
Stochastic Modelling for Systems Biology Book PDF

Author : Darren James Wilkinson
Publisher : Unknown
Page : 360 pages
File Size : 55,7 Mb
Release : 2012
Category : Biological systems
ISBN : 0429152035

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Stochastic Modelling for Systems Biology by Darren James Wilkinson Pdf

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 accessib.

Methods and Models in Mathematical Biology

Johannes Müller,Christina Kuttler
Methods and Models in Mathematical Biology Book PDF

Author : Johannes Müller,Christina Kuttler
Publisher : Springer
Page : 711 pages
File Size : 52,7 Mb
Release : 2015-08-13
Category : Mathematics
ISBN : 9783642272516

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Methods and Models in Mathematical Biology by Johannes Müller,Christina Kuttler Pdf

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.

Identifiability and Regression Analysis of Biological Systems Models

Paola Lecca
Identifiability and Regression Analysis of Biological Systems Models Book PDF

Author : Paola Lecca
Publisher : Springer Nature
Page : 90 pages
File Size : 49,6 Mb
Release : 2020-03-05
Category : Medical
ISBN : 9783030412555

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Identifiability and Regression Analysis of Biological Systems Models by Paola Lecca Pdf

This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

Stochastic Dynamics in Computational Biology

Stefanie Winkelmann,Christof Schütte
Stochastic Dynamics in Computational Biology Book PDF

Author : Stefanie Winkelmann,Christof Schütte
Publisher : Springer Nature
Page : 284 pages
File Size : 47,8 Mb
Release : 2021-01-04
Category : Mathematics
ISBN : 9783030623876

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Stochastic Dynamics in Computational Biology by Stefanie Winkelmann,Christof Schütte Pdf

The aim of this book is to provide a well-structured and coherent overview of existing mathematical modeling approaches for biochemical reaction systems, investigating relations between both the conventional models and several types of deterministic-stochastic hybrid model recombinations. Another main objective is to illustrate and compare diverse numerical simulation schemes and their computational effort. Unlike related works, this book presents a broad scope in its applications, from offering a detailed introduction to hybrid approaches for the case of multiple population scales to discussing the setting of time-scale separation resulting from widely varying firing rates of reaction channels. Additionally, it also addresses modeling approaches for non well-mixed reaction-diffusion dynamics, including deterministic and stochastic PDEs and spatiotemporal master equations. Finally, by translating and incorporating complex theory to a level accessible to non-mathematicians, this book effectively bridges the gap between mathematical research in computational biology and its practical use in biological, biochemical, and biomedical systems.

Analysis of Biological Systems

Corrado Priami,Melissa J Morine
Analysis of Biological Systems Book PDF

Author : Corrado Priami,Melissa J Morine
Publisher : World Scientific
Page : 432 pages
File Size : 44,7 Mb
Release : 2015-01-29
Category : Science
ISBN : 9781783266890

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Analysis of Biological Systems by Corrado Priami,Melissa J Morine Pdf

Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their effective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest. Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science. Contents:Algorithmic Systems BiologySetting the ContextSystems and ModelsStatic Modeling TechnologiesDynamic Modeling TechnologiesLanguage-based ModelingDynamic Modeling ProcessSimulationPerspectives and ConclusionsAppendix A: Basic MathAppendix B: Probability and StatisticsAppendix C: Semantics of Modeling Languages Readership: Graduate students in computer science, physics, mathematics or engineering or biology-related fields who want to better understand how to develop and use models of biological systems. Practitioners in systems biology who want to understand algorithmic modeling and algorithmic systems biology. Key Features:The book jointly deals with static (statistical) and dynamic (simulation) technologies making it a strong reference for who wants to approach real systems biology problemsThe content of the book is the result of more than ten years application of the material in university courses and to industrial-level problems in systems pharmacology and systems nutritionThere is no reference work available for the field of language-based modeling that is studied in depth in this bookKeywords:Modeling;Simulation;Network Analysis;Systems Biology;Systems Nutrition;Systems Pharmacology;Stochastic Models;Programming Biology;Multivariate Analysis

Theoretical Physics for Biological Systems

Paola Lecca,Angela Re
Theoretical Physics for Biological Systems Book PDF

Author : Paola Lecca,Angela Re
Publisher : CRC Press
Page : 146 pages
File Size : 44,7 Mb
Release : 2019-01-30
Category : Medical
ISBN : 9781351374323

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Theoretical Physics for Biological Systems by Paola Lecca,Angela Re Pdf

Quantum physics provides the concepts and their mathematical formalization that lend themselves to describe important properties of biological networks topology, such as vulnerability to external stress and their dynamic response to changing physiological conditions. A theory of networks enhanced with mathematical concepts and tools of quantum physics opens a new area of biological physics, the one of systems biological physics.

Stochastic Analysis of Biochemical Systems

David F. Anderson,Thomas G. Kurtz
Stochastic Analysis of Biochemical Systems Book PDF

Author : David F. Anderson,Thomas G. Kurtz
Publisher : Springer
Page : 84 pages
File Size : 50,8 Mb
Release : 2015-04-23
Category : Mathematics
ISBN : 9783319168951

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Stochastic Analysis of Biochemical Systems by David F. Anderson,Thomas G. Kurtz Pdf

This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology. The book should serve well as a supplement for courses in probability and stochastic processes. While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations and elementary probability and who are well-motivated by the applications will find this book of interest. David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other areas of science and technology. These notes are based in part on lectures given by Professor Anderson at the University of Wisconsin – Madison and by Professor Kurtz at Goethe University Frankfurt.

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

David Holcman
Stochastic Processes Multiscale Modeling and Numerical Methods for Computational Cellular Biology Book PDF

Author : David Holcman
Publisher : Springer
Page : 377 pages
File Size : 47,8 Mb
Release : 2017-10-04
Category : Mathematics
ISBN : 9783319626277

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Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology by David Holcman Pdf

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.

Stochastic Dynamics for Systems Biology

Christian Mazza,Michel Benaim
Stochastic Dynamics for Systems Biology Book PDF

Author : Christian Mazza,Michel Benaim
Publisher : CRC Press
Page : 274 pages
File Size : 42,9 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781466514942

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Stochastic Dynamics for Systems Biology by Christian Mazza,Michel Benaim Pdf

Stochastic Dynamics for Systems Biology is one of the first books to provide a systematic study of the many stochastic models used in systems biology. The book shows how the mathematical models are used as technical tools for simulating biological processes and how the models lead to conceptual insights on the functioning of the cellular processing

Introduction to Modeling and Simulation with MATLAB® and Python

Steven I. Gordon,Brian Guilfoos
Introduction to Modeling and Simulation with MATLAB and Python Book PDF

Author : Steven I. Gordon,Brian Guilfoos
Publisher : CRC Press
Page : 192 pages
File Size : 47,7 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9781498773881

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Introduction to Modeling and Simulation with MATLAB® and Python by Steven I. Gordon,Brian Guilfoos Pdf

Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.