Model based identification of transcription factor activity from microarray data
With the increase in volume of gene expression data available from high throughput microarray experiments, much research interest has been directed at building mathematical models of the process of gene regulation. Such models have primarily been used for the so called reverse engineering of regulatory networks; inferring possible regulatory interactions directly from microarray data, for example [1–4]. By using microarray data, all of these techniques make the implicit assumption that there is a direct relationship between the level of mRNA of genes coding for transcription factors (TFs) and the mRNA levels of their gene-targets.
Author: Simon Rogers, University of Glasgow