Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

2018 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Tenth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: October 27-28, 2018Location: UT Conference Center, KnoxvillePlenary Speaker: Holly Gaff, Biological Sciences, Old Dominion Univ.Featured Speaker: Nina Fefferman, Ecology & Evolutionary Biology, Mathematics, Univ. of Tennessee, Knoxvill