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ACMD Seminar: Quantifying Flows in Time-Irreversible Markov Chains: Application to a Gene Regulatory Network

Danielle Middlebrooks
University of Maryland-College Park, Applied Mathematics, Statistics and Scientific Computing Program

Thursday, August 22, 2019, 3:00 – 4:00 PM
Building 101, Lecture Room B
Gaithersburg

Thursday, August 22, 2019, 1:00 – 2:00PM
Building 1, Room 1107
Boulder

Host: Paul Patrone

Abstract: Transition path theory (TPT) is a framework used to study the statistical properties of reactive trajectories. Reactive trajectories are those trajectories by which a random walker transits from one subset in the state-space to another disjoint subset. We develop analytical and computational tools based on TPT in order to quantify flows in time-irreversible Markov chains. These tools are applied to a gene regulatory network modeling the dynamics of the Budding Yeast cell cycle.

Bio: Danielle Middlebrooks is a doctoral candidate in the Applied Mathematics, Statistics and Scientific Computing (AMSC) program at the University of Maryland-College Park. She received a B.S. in Mathematics from Spelman College in Atlanta, GA. Her research focuses on Markov chain based methods to analyze complex networks. In 2017, she was awarded a COMBINE (Computation and Mathematics for Biological Networks) fellowship: a National Science Foundation-funded Research Traineeship (NRT) program in network biology at the University of Maryland. 

Note: Visitors from outside NIST must contact Cathy Graham; (301) 975-3800; at least 24 hours in advance.

Created July 26, 2019, Updated November 15, 2019