(Learning and Inference in Computational Systems Biology (Computational Molecular Biology)) [By: x] [Apr, 2010]

by Neil D. Lawrence

Computational systems biology unifies the mechanistic approach of systemsbiology with the data-driven approach of computational biology. Computationalsystems biology aims to develop algorithms that uncover the structure andparameterization of the underlying mechanistic model--in other words, to answerspecific questions about the underlying mechanisms of a biological system--in aprocess that can be thought of as learning or inference. This volume offersstate-of-the-art perspectives from computational biology, statistics, modeling, andmachine learning on new methodologies for learning and inference in biologicalnetworks.The chapters offer practical approaches to biological inference problemsranging from genome-wide inference of genetic regulation to pathway-specificstudies. Both deterministic models (based on ordinary differential equations) andstochastic models (which anticipate the increasing availability of data from smallpopulations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesianapproach and an overview of current work on Bayesian inference. Taken together, themethods discussed by the experts in Learning and Inference in Computational SystemsBiology provide a foundation upon which the next decade of research in systemsbiology can be built. Florence d'Alch e-Buc, John Angus, Matthew J. Beal, NicholasBrunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li, Kuang Lin, Pedro Mendes, Nicholas A. M.Monk, Eric Mjolsness, Manfred Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild, DarrenWilkinson, Guy Yosiphon

Hardcover

First published February 1, 2010