Statistical model for biochemical network inference

Gheorghe Craciun, Jaejik Kim, Casian Pantea, Grzegorz A. Rempala

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 Craciun et al. (2009) 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.

Original languageEnglish (US)
Pages (from-to)121-137
Number of pages17
JournalCommunications in Statistics: Simulation and Computation
Volume42
Issue number1
DOIs
StatePublished - Jan 1 2013

Fingerprint

Biochemical Networks
Statistical Model
Reaction Network
Dimension Reduction
Longitudinal Data
Prediction Error
Statistical method
Statistical methods
Kinetics
Likely
Restriction
Statistical Models

Keywords

  • Algebraic statistical model
  • Biochemical reaction network
  • Dimension reduction
  • Law of mass action
  • Polyhedral geometry

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

Cite this

Statistical model for biochemical network inference. / Craciun, Gheorghe; Kim, Jaejik; Pantea, Casian; Rempala, Grzegorz A.

In: Communications in Statistics: Simulation and Computation, Vol. 42, No. 1, 01.01.2013, p. 121-137.

Research output: Contribution to journalArticle

Craciun, Gheorghe ; Kim, Jaejik ; Pantea, Casian ; Rempala, Grzegorz A. / Statistical model for biochemical network inference. In: Communications in Statistics: Simulation and Computation. 2013 ; Vol. 42, No. 1. pp. 121-137.
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