College of Liberal Arts & Sciences

SKIPS: Stochastic Knowledge-based Interaction Pathway Simulator

Wednesday, September 12, 2018

SKIPS: Stochastic Knowledge-based Interaction Pathway Simulator

Wednesday, September 12, 2018

1:00 p.m. Room 202 MRB

Dr. Michael Rowland

Research Biologist at US Army Engineer Research and Development Center Vicksburg, Mississippi

Biological responses across all scales demonstrate some degree of heterogeneity to perturbations due to their inherent noise, and may be modeled
computationally, such as with the Stochastic Simulation Algorithm, to directly incorporate biologically relevant fluctuations. Yet many multiscale
stochastic models are notoriously slow and computationally intensive. An alternative is to model the average response with a deterministic approach
such as chemical kinetics, but such methods often fail to capture the potentially crucial impacts of noise as it compounds across increasing levels of
biological organization. Methods that combine these approaches (e.g., stochastic differential equations) often make broad simplifying assumptions
regarding the type of noise, and may therefore not capture statistical variation in the data with sufficient fidelity. Here we describe the Stochastic
Knowledge-based Interaction Pathway Simulator (SKIPS), a new approach to modeling multiscale stationary biological processes that shortcuts a
formal stochastic simulation by modeling the mean response at each biological level around which conditional Monte Carlo sampling is used to
generate variation statistically consistent with training data. A conditional probability distribution associated with a biological response can thus be
reconstructed using this method for certain input values. We demonstrate the utility of SKIPS via two case studies: a reproduction-related adverse
outcome pathway in the adult female fathead minnow (Pimephales promelas) and the effects of insulin on glycogen synthesis in human cells.



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