Description :
This book is aimed at training aspiring computational biologists to handle new and unanticipated problems. It teaches the students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. The text covers models of 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.
“In twenty-first-century biology, modeling has a similar role as the microscope had in earlier centuries; it is arguably the most important research tool for studying complex phenomena and processes in all areas of the life sciences, from molecular biology to ecosystems analysis. Every biologist therefore needs to be familiar with the basic approaches, methods, and assumptions of modeling. Biological Modeling and Simulation is an essential guide that helps biologists explore the fundamental principles of modeling. It should be on the bookshelf of every student and active researcher.”
—MANFRED D. LAUBICHLER
School of Life Sciences, Arizona State University
Content :
Preface. Introduction. I MODELS FOR OPTIMIZATION—Classic Discrete Optimization Problems. Hard Discrete Optimization Problems. Case Study: Sequence Assembly. General Continuous Optimization. Constrained Optimization. II 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. Ordinary Differential Equations. Numerical Integration. Partial Differential Equations. Numerical Integration. Stochastic Differential Equations. Case Study: Simulating Cellular Biochemistry. III 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. No other Books by the same author | |
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