Modeling protein-DNA binding time in Stochastic Discrete Event Simulation of Biological Processes

This paper presents a parametric model to estimate the DNA-protein binding time using the DNA and protein structures and details of the binding site. To understand the stochastic behavior of biological systems, we propose an "in silico" stochastic event based simulation that determines the temporal dynamics of different molecules. This paper presents a parametric model to determine the execution time of one biological function (i.e. simulation event): protein-DNA binding by abstracting the function as a stochastic process of microlevel biological events using probability measure. This probability is coarse grained to estimate the stochastic behavior of the biological function. Our model considers the structural configurations of the DNA, proteins and the actual binding mechanism. We use a collision theory based approach to transform the thermal and concentration gradients of this biological process into the probability measure of DNA-protein binding event. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox. This model can produce acceptable estimates of DNA-protein binding time to be used by our event-based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants