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Stochastic chemical reaction networks (CRNs) are used to model real-world phenomena whose semantics can be captured by the notion of different molecules whose counts or concentrations evolve over time according to a set of chemical reactions. They are also used prescriptively by scientists who want to build computers out of biological molecules, as CRNs can do computation and arbitrary CRNs can be implemented using DNA strand displacement cascades. Predicting and understanding the behavior of these systems is challenging, as CRN behavior can depend on molecular count at scales too large for traditional simulation algorithms to capture, and more efficient inexact simulation algorithms may lose qualitative information about CRN behavior.   I will demonstrate an algorithm which simulates CRNs exactly, and with speedup over traditional simulation that scales with molecular count. The algorithm adapts a simulation algorithm from a related model of distributed computing, allowing it to work in the more general CRN setting.

Zoom meeting link: https://ucdavis.zoom.us/j/7269058703