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'''Stochastic Modeling and Simulation in Biology''' '''Stochastic Modeling and Simulation in Biology'''
-Kevin Sanft, University of California, Santa Barbara+Kevin Sanft, University of California, Santa Barbara on
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Tuesday, March 27 at 1:30 p.m. in RNS 310 Tuesday, March 27 at 1:30 p.m. in RNS 310
 +
 +'''Abstract:''' Traditional differential equation models of chemical systems work well when the interacting chemical species are present in high concentrations. However, at the cellular scale, many processes in biology are driven by random collisions between molecules with small populations. These processes are inherently stochastic (random) and can display behavior that cannot be captured by deterministic models. In this talk, I will provide an overview of Gillespie's Stochastic Simulation Algorithm (SSA), a simulation method that captures this stochasticity. Unfortunately, the SSA is very computationally expensive. Since Gillespie’s pioneering work, many exact and approximate variations of the SSA have been developed to speed up these simulations. I will describe some of these algorithms and discuss their performance and scaling properties. Finally, I will present some of my recent work developing an efficient multiscale stochastic simulation algorithm.
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Andrew Vlasic, University of Illinois - Urbana, Champaign Andrew Vlasic, University of Illinois - Urbana, Champaign
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Wednesday, March 28 at 3:30 p.m. in RNS 310 Wednesday, March 28 at 3:30 p.m. in RNS 310
 +
 +'''Abstract:''' In 1951, John Nash showed that for any normal form game, there exists at least one strategy that is a best reply to itself. This strategy is known as the Nash Equilibrium and has multiple applications to various disciplines. We will introduce the concept of a Nash Equilibrium and consider how this idea may be applied to biology. In particular, we will discuss a game theoretic population dynamic called the replicator equation and show how the Nash Equilibrium corresponds to the stability of this system. Finally, we will consider applications of this model to populations of bacteria.

Revision as of 03:46, 27 March 2012

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The Department of Mathematics, Statistics, and Computer Science is brimming with activity in three vital disciplines: Mathematics; Statistics; and Computer Science. These fields can team up with almost any other discipline or interest---and also align well with many careers. Check us out!


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Upcoming Colloquia

Stochastic Modeling and Simulation in Biology

Kevin Sanft, University of California, Santa Barbara on Tuesday, March 27 at 1:30 p.m. in RNS 310

Abstract: Traditional differential equation models of chemical systems work well when the interacting chemical species are present in high concentrations. However, at the cellular scale, many processes in biology are driven by random collisions between molecules with small populations. These processes are inherently stochastic (random) and can display behavior that cannot be captured by deterministic models. In this talk, I will provide an overview of Gillespie's Stochastic Simulation Algorithm (SSA), a simulation method that captures this stochasticity. Unfortunately, the SSA is very computationally expensive. Since Gillespie’s pioneering work, many exact and approximate variations of the SSA have been developed to speed up these simulations. I will describe some of these algorithms and discuss their performance and scaling properties. Finally, I will present some of my recent work developing an efficient multiscale stochastic simulation algorithm.


Evolutionary Game Theory and the Replicator Equation

Andrew Vlasic, University of Illinois - Urbana, Champaign Wednesday, March 28 at 3:30 p.m. in RNS 310

Abstract: In 1951, John Nash showed that for any normal form game, there exists at least one strategy that is a best reply to itself. This strategy is known as the Nash Equilibrium and has multiple applications to various disciplines. We will introduce the concept of a Nash Equilibrium and consider how this idea may be applied to biology. In particular, we will discuss a game theoretic population dynamic called the replicator equation and show how the Nash Equilibrium corresponds to the stability of this system. Finally, we will consider applications of this model to populations of bacteria.


Space and Control in Natural Systems

Louis J. Gross, National Institute for Mathematical and Biological Synthesis (NIMBioS), Departments of Ecology and Evolutionary Biology and Mathematics, University of Tennessee, Knoxville

Thursday, March 29 at 7:00 p.m. in RNS 150


See Colloquium Series for info on other upcoming colloquia.