A New Evolutionary Gene Regulatory Network Reverse Engineering Tool

2011 ◽  
pp. 13-24 ◽  
Author(s):  
Antonella Farinaccio ◽  
Leonardo Vanneschi ◽  
Paolo Provero ◽  
Giancarlo Mauri ◽  
Mario Giacobini
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Regulatory

scholarly journals Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

2012 ◽  
Vol 8 (7) ◽  
pp. e1002589 ◽  
Author(s):  
Anton Crombach ◽  
Karl R. Wotton ◽  
Damjan Cicin-Sain ◽  
Maksat Ashyraliyev ◽  
Johannes Jaeger
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Developmental Gene ◽  
Gene Regulatory

Nonlinear Stochastic Differential Equations Method for Reverse Engineering of Gene Regulatory Network

2010 ◽  
pp. 219-243
Author(s):  
Adriana Climescu-Haulica ◽  
Michelle Quirk
Keyword(s):  
Reverse Engineering ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Information Criteria ◽  
Selection Strategy ◽  
Forward Selection ◽  
Likelihood Principle ◽  
Gene Regulatory ◽  
Nonlinear Stochastic Differential Equation

In this chapter, we present a method to infer the structure of the gene regulatory network that takes in account both the kinetic molecular interactions and the randomness of data. The dynamics of the gene expression level are fitted via a nonlinear stochastic differential equation (SDE) model. The drift term of the equation contains the transcription rate related to the architecture of the local regulatory network. The statistical analysis of data combines maximum likelihood principle with Akaike Information Criteria (AIC) through a forward selection Strategy to yield a set of specific regulators and their contribution. Tested with expression data concerning the cell cycle for S. Cerevisiae and embryogenesis for the D. melanogaster, this method provides a framework for the reverse engineering of various gene regulatory networks.

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