### 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 |

### Fingerprint

### 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

*Communications in Statistics: Simulation and Computation*,

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

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

Research output: Contribution to journal › Article

*Communications in Statistics: Simulation and Computation*, vol. 42, no. 1, pp. 121-137. https://doi.org/10.1080/03610918.2011.633200

}

TY - JOUR

T1 - Statistical model for biochemical network inference

AU - Craciun, Gheorghe

AU - Kim, Jaejik

AU - Pantea, Casian

AU - Rempala, Grzegorz A.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

KW - Algebraic statistical model

KW - Biochemical reaction network

KW - Dimension reduction

KW - Law of mass action

KW - Polyhedral geometry

UR - http://www.scopus.com/inward/record.url?scp=84867344321&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867344321&partnerID=8YFLogxK

U2 - 10.1080/03610918.2011.633200

DO - 10.1080/03610918.2011.633200

M3 - Article

AN - SCOPUS:84867344321

VL - 42

SP - 121

EP - 137

JO - Communications in Statistics: Simulation and Computation

JF - Communications in Statistics: Simulation and Computation

SN - 0361-0918

IS - 1

ER -