Adiabatic coarse-graining and simulations of stochastic biochemical networks.
We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical networks, which rests on elimination of fast chemical species without a loss of information about mesoscopic, non-Poissonian fluctuations of the slow ones. Our approach is similar to the Born-Oppenheimer approximation in quantum mechanics and follows from the stochastic path integral representation of the cumulant generating function of reaction events. In applications with a small number of chemical reactions, it produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, interpretable representation and can be used for high-accuracy, low-complexity coarse-grained numerical simulations. As an example, we derive the coarse-grained description for a chain of biochemical reactions and show that the coarse-grained and the microscopic simulations agree, but the former is 3 orders of magnitude faster.
Sinitsyn
N A
NA
Computer, Computational, and Statistical Sciences Division, Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Hengartner
Nicolas
N
Nemenman
Ilya
I
D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D004735:Energy Transfer; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism; D008565:Membrane Proteins / Q000737:chemistry / Q000378:metabolism; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D013269:Stochastic Processes
D004798:Enzymes; D008565:Membrane Proteins