A new probabilistic generative model of parameter inference in biochemical networks

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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS

General Terms:
Algorithms

Keywords:
biochemical networks, maximum likelihood methods, parameter estimation, systems biology