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The most challenging aspect of gene expression data analysis is to process the large and complex data using mathematical models and find biologically relevant information that gives insight to the underlying mechanism. We derived a simple ordinary differential equation-based model using Michaelis–Menten Kinetics to process the microarray data. Different biological systems of experimental rhinovirus infection in humans, atopic CD4 T cell responses in allergens and responses to cancer immunotherapy in mice have been studied. The resulting analysis extracts highly linked target genes, the changes in which might cause changes in the other genes, in other words, potential targets for modulating gene network patterns and emergent biological phenotypes. We illustrate the application of the algorithm to identify novel targets in addition to previously identified targets in different experimental contexts.

Network using Michaelis–Menten kinetics: constructing an algorithm to find target genes from expression data