Algebraic Methods for Inferring Biochemical Networks: a Maximum Likelihood Approach

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dc.creatorCraciun, Gheorghe
dc.creatorPantea, Casian
dc.creatorRempala, Grzegorz A.
dc.date2008-10-03
dc.date2008-10-04
dc.date.accessioned2026-07-07T10:07:25Z
dc.date.available2026-07-07T10:07:25Z
dc.descriptionWe 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 form a "mass transfer"-type model.
dc.description14 pages and 4 figures
dc.identifierhttps://arxiv.org/abs/0810.0561
dc.identifierhttp://arxiv.org/abs/0810.0561
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170627
dc.subjectApplications
dc.subjectMolecular Networks
dc.subjectQuantitative Methods
dc.titleAlgebraic Methods for Inferring Biochemical Networks: a Maximum Likelihood Approach
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