### 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 language | English (US) |
---|---|

Pages (from-to) | 121-137 |

Number of pages | 17 |

Journal | Communications in Statistics: Simulation and Computation |

Volume | 42 |

Issue number | 1 |

DOIs | |

State | Published - Jan 1 2013 |

### 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

## Fingerprint Dive into the research topics of 'Statistical model for biochemical network inference'. Together they form a unique fingerprint.

## Cite this

*Communications in Statistics: Simulation and Computation*,

*42*(1), 121-137. https://doi.org/10.1080/03610918.2011.633200