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Algebraic methods for inferring biochemical networks: A maximum likelihood approach

Paper ID Volume ID Publish Year Pages File Format Full-Text
15240 1396 2009 7 PDF Available
Title
Algebraic methods for inferring biochemical networks: A maximum likelihood approach
Abstract

We present a novel method for identifying a biochemical reaction network based on multiple sets of estimated reaction rates in the corresponding reaction rate equations arriving from various (possibly different) experiments. The current method, unlike some of the graphical approaches proposed in the literature, uses the values of the experimental measurements only relative to the geometry of the biochemical reactions under the assumption that the underlying reaction network is the same for all the experiments. The proposed approach utilizes algebraic statistical methods in order to parametrize the set of possible reactions so as to identify the most likely network structure, and is easily scalable to very complicated biochemical systems involving a large number of species and reactions. The method is illustrated with a numerical example of a hypothetical network arising from a “mass transfer”-type model.

Keywords
92C40; 92C45; 52B70; 62FBiochemical reaction network; Law of mass action; Algebraic statistical model; Polyhedral geometry
First Page Preview
Algebraic methods for inferring biochemical networks: A maximum likelihood approach
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 5, October 2009, Pages 361–367
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering