PHYSICAL INFRASTRUCT: ALGORITHMS FOR STOCHASTIC SIMULASTIONS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Modeling stochastic processes is crucially important when numbers of copies of participating molecules is relatively small and fluctuations due to intrinsic or external noise become significant. One example is intracellular granule trafficking. At the early stages of the Virtual Cell project the Virtual Cell C++ library was enhanced by a capability to simulate spatial Brownian dynamics, where the state of a system is described by location and a state of "particles", a point-like objects. The Virtual Cell particles can undergo random walks in 1-, 2-, or 3 dimensions and simultaneously interact stochastically with continuously distributed species, which are described deterministically by partial differential equations. Later, we further developed our "stochastic" code to account for granule binding to microtubules and motor-driven directed motion. To this end, a new class - contours, or directed curves - was introduced into the Virtual Cell C++ library, which would represent microtubules and filaments. The goal of this project is to provide algorithms necessary for the current effort to make the Virtual Cell stochastic capabilities accessible through the user interface. The idea is to give a user a choice to treat a bio-model either stochastically or deterministically. We envision several types of Monte-Carlo-type modeling: (i) tracking particles (Brownian dynamics, predominantly for spatial models), (ii) "adding noise", and (iii) exact Gillespie-type algorithms.