Local Identification of Piecewise Deterministic Models of Genetic Networks
HSCC '09 Proceedings of the 12th International Conference on Hybrid Systems: Computation and Control
A Survey on Methods for Modeling and Analyzing Integrated Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parameter estimation for stochastic hybrid models of biochemical reaction networks
Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control
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Motivation: Identification of regulatory networks is typically based on deterministic models of gene expression. Increasing experimental evidence suggests that the gene regulation process is intrinsically random. To ensure accurate and thorough processing of the experimental data, stochasticity must be explicitly accounted for both at the modelling stage and in the design of the identification algorithms. Results: We propose a model of gene expression in prokaryotes where transcription is described as a probabilistic event, whereas protein synthesis and degradation are captured by first-order deterministic kinetics. Based on this model and assuming that the network of interactions is known, a method for estimating unknown parameters, such as synthesis and binding rates, from the outcomes of multiple time-course experiments is introduced. The method accounts naturally for sparse, irregularly sampled and noisy data and is applicable to gene networks of arbitrary size. The performance of the method is evaluated on a model of nutrient stress response in Escherichia coli. Contact: cinquemani@control.ee.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.