Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

Background: The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions. Results: Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatiotemporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Anil Aswani, Soile V. E. Keränen, James Brown, Charless C. Fowlkes, David W. Knowles, Mark D. Biggin, Peter Bickel, Claire J. Tomlin
• Keywords: BMCBI 2010, Business, Expression Levels, Target Gene, Transcription Factors