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A New Probabilistic Generative Model of Parameter Inference in Biochemical Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the 2009 ACM Symposium on Applied Computing
Place of PublicationNew York, NY, USA
PublisherACM
Pages758-765
Number of pages8
DOIs
Publication statusPublished - 2009

Publication series

NameSAC '09
PublisherACM

Abstract

We present a new method for estimating rate coefficients and level of noise in models of biochemical networks from noisy observations of concentration levels at discrete time points. Its probabilistic formulation, based on maximum likelihood estimation, is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions, such as a fully Bayesian treatment of the parameter inference and automated model selection strategies based on the comparison between marginal likelihoods of different models. We developed KInfer (Knowlegde Inference), a tool implementing our inference model. KInfer is downloadable for free at http://www.cosbi.eu.

    Research areas

  • biochemical networks, maximum likelihood methods, parameter estimation, systems biology

ID: 21882076