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

Name: Biological Modeling and Simulation A Survey of Practical Models, Algorithms, and Numerical Methods
Price: 9.19 USD
Availability: InStock
ISBN: 9780262195843

ISBN-10: 0262195844

ISBN-13: 9780262195843

Edition: 2008

Authors: Russell Schwartz

List price: $50.00

30 day, 100% satisfaction guarantee!

Marketplace

3 new & used from $9.19

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

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 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…

Book details

List price: $50.00
Copyright year: 2008
Publisher: MIT Press
Publication date: 7/25/2008
Binding: Hardcover
Pages: 408
Size: 7.25" wide x 9.25" long x 0.94" tall
Weight: 1.738

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