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Taylor and Francis Group, Communications in Statistics - Simulation and Computation, 1(42), p. 121-137

DOI: 10.1080/03610918.2011.633200

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Statistical Model for Biochemical Network Inference

Journal article published in 2012 by Gheorghe Craciun, Jaejik Kim, Casian Pantea, Grzegorz A. Rempala
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This paper is available in a repository.

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Abstract

We describe a statistical method for predicting most likely reactions in a biochemical reaction network from the longitudinal data on species concentrations. Such data is relatively easily available in biochemical laboratories, for instance, via the popular RT-PCR technology. Under the assumed kinetics of the law of mass action, we also propose the data-based algorithms for estimating the prediction errors and for network dimension reduction. The second algorithm allows in particular for the application of the original algebraic inferential procedure described in [4] without the unnecessary restrictions on the dimension of the network stoichiometric space. Simulated examples of biochemical networks are analyzed, in order to assess the proposed methods’ performance.