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Biological Modeling and Simulation A Survey of Practical Models, Algorithms, and Numerical Methods

ISBN-10: 0262195844
ISBN-13: 9780262195843
Edition: 2008
Authors: Russell Schwartz
List price: $50.00
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Description: There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational  More...

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Book details

List price: $50.00
Copyright year: 2008
Publisher: MIT Press
Publication date: 7/25/2008
Binding: Hardcover
Pages: 408
Size: 7.50" wide x 9.25" long x 1.00" tall
Weight: 1.738

There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives the aspiring student a bag of tricks that will serve him or her well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered. The text, which grew out of a class taught at Carnegie Mellon University, covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology.

Russell Schwartz is Associate Professor in the Department of Biological Sciences at Carnegie Mellon University.

Complete Contents
Preface
Introduction
Models For Optimization
Classic Discrete Optimization Problems
Hard Discrete Optimization Problems
Case Study: Sequence Assembly
General Continuous Optimization
Constrained Optimization
Simulation And Sampling
Sampling from Probability Distributions
Markov Models
Markov Chain Monte Carlo Sampling
Mixing Times of Markov Models
Continuous-Time Markov Models
Case Study: Molecular Evolution
Discrete Event Simulation
Numerical Integration 1: Ordinary Differential Equations
Numerical Integration 2: Partial Differential Equations
Numerical Integration 3: Stochastic Differential Equations
Case Study: Simulating Cellular Biochemistry
Parameter-Tuning
Parameter-Tuning as Optimization
Expectation Maximization
Hidden Markov Models
Linear System-Solving
Interpolation and Extrapolation
Case Study: Inferring Gene Regulatory Networks
Model Validation
References
Index

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