|Stochastic Systems Group|
Game Theory, Biology and the DNA Binding Game
We propose a game-theoretic approach to learn and predict coordinate binding of multiple DNA binding regulators. The framework implements resource constrained allocation of proteins to local neighborhoods as well as to sites themselves, and explicates coordinate and competitive binding relations among proteins with affinity to the site or region. More specifically, our model allows us to make numerical predictions of the (global) arrangements of proteins near the neighborhood of sites, as well as the (local) binding of proteins to sites under many different conditions and perturbations to the system at a large scale.
This talk will emphasize the mathematical and computational foundations of the new modeling approach. I will start by formally presenting our proposed model: the DNA Binding game. I will establish its ability to make predictions under any perturbations by showing that an equilibrium exists in any instantiation of the game. I will present in some detail a simple iterative algorithm that monotonically converges to an equilibrium of the game (thus providing a constructive proof of existence). Time permitting, I will show a small-scale illustration of our approach on a well-known biological subsystem: lambda-phage. I will conclude by briefly discussing work in progress on learning games from data to address large-scale biological problems.
Joint work with Luis Perez-Breva, Chen-Hsiang Yeang and Tommi Jaakkola.
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