Modeling gene induction

National Institute Of Diabetes And Digestive And Kidney Diseases

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Abstract

I developed a biochemical model for steroid-mediated gene expression in the presence of various factors. Experiments have found that the dose-response curve for gene expression closely follows a Michaelis-Menten function and that factors can alter both the maximum value and location of half maximum of the function. We showed theoretically that this highly stringent constraint can only occur in a sequence of reactions if factors downstream of receptor-steroid binding interact weakly. The theory can then make precise predictions on the mechanisms and site of action of these cofactors. We used the theory to design a novel competition assay to predict the mechanisms and relative positions of the two cofactors and have applied it to several different factors. We have now augmented the theory to steroid-mediated gene repression and to explaining partial agonist and antagonist action. Thus far, we have validated our theory by making predictions on how cofactors should influence the amount of gene product produced. I am now collaborating with experimentalists to attempt to reconcile the theory with single cell imaging data. To do so, I have developed a stochastic single gene model that is consistent with the previous model. We are currently testing the applicability of the model to both single RNA molecule live cell and FISH data.