title: Statistical Model for Biochemical Network Inference.
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.
authors.affiliation: Department of Mathematics and Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706.
authors.forename: Gheorghe
authors.initials: G
authors.lastname: Craciun
authors.forename: Jaejik
authors.initials: J
authors.lastname: Kim
authors.forename: Casian
authors.initials: C
authors.lastname: Pantea
authors.forename: Grzegorz A
authors.initials: GA
authors.lastname: Rempala