A stochastic differential equation model for transcriptional regulatory networks.
BACKGROUND
This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution.


RESULTS
We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels.


CONCLUSIONS
When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks.
Climescu-Haulica
Adriana
A
Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, Grenoble, France. adriana.climescu@cea.fr
Quirk
Michelle D
MD
D015870:Gene Expression; D053263:Gene Regulatory Networks; D008954:Models, Biological; D012333:RNA, Messenger / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D013269:Stochastic Processes; D013997:Time Factors
D012333:RNA, Messenger