Statistical Model for Biochemical Network Inference

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 Craciun et al. (2009 Craciun , G. , Pantea , C. , Rempala , G. ( 2009 ). Algebraic methods for inferring biochemical networks: A maximum likelihood approach . Computational Biology and Chemistry 33 ( 5 ): 361 – 367 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) 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.