David F. Anderson, University of Wisconsin - Madison

Abstract:

While exact simulation methods exist for discrete-stochastic models of biochemical reaction networks, they are oftentimes too inefficient for use because the number of computations scales linearly with the number of reaction events; thus, approximate algorithms are used. Stochastically modeled reaction networks often have ``natural scales'' and it is crucial that these be accounted for when developing and analyzing approximation methods. We have recently demonstrated this fact by showing that a midpoint type algorithm thought to be no more accurate than an Euler type method is in fact an order of magnitude more accurate in a certain scaling--something previously observed only through examples. I will describe the analysis performed and show why we reach fundamentally different conclusions than previous analyses.