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Past Events CCMB Distinguished Technology Lecture Series: 2006-2007|2007-2008 CCMB Grantsmanship Lecture Series: 2009-2010
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Every day we are surrounded by symmetric objects and patterns. From furniture to flooring, symmetry is the rule. In art, symmetry is pleasing to the eye, and the intricacies of extremely symmetric patterns can entrance an audience. In architecture, symmetric designs are attractive for yet another reason – repetition of a design element means re-use, which ultimately requires less planning and testing. In manufacturing, it is simpler, cheaper and more efficient to repeat a pattern at regular intervals. Even Nature has reasons to use symmetry in her work. John H. Conway and William Thurston adapted Murray MacBeath’s mathematical language for discussing symmetry. Now, the symmetries of a pattern can be defined by a single symbol that we call its signature. With some practice, almost anyone with some knowledge of high-school geometry can read this signature and identify the symmetries it describes. John Conway is the John von Neumann Professor of Mathematics at Princeton University. He is one of the most influential mathematicians working in a wide variety are areas: combinatorial game theory, geometry, geometric topology, group theory, number theory, algebra, algorithmics and theoretical physics. He received numerous awards including the Berwick Prize and was elected a Fellow of the Royal Society. He was the first recipient of the Pólya Prize and won the Nemmers Prize in Mathematics. He is also known for the invention of the Game of Life. Friday,
November 20, 2009 Refreshments will be served at 3:45 pm
Human genetics is currently dominated by the genome-wide association study (GWAS) that measures and evaluates one million or more single nucleotide polymorphisms (SNPs) for their disease associations. The current biostatistical paradigm is to analyze each SNP individually without regard to the rest of the genome or environmental exposure. This agnostic or unbiased approach has not been successful for identifying SNPs with moderate or large effects on disease susceptibility. We present here an alternative bioinformatics strategy for GWAS analysis that focuses on gene-gene and gene-environment interactions and their context in biochemical pathways. Wednesday, November 18, 2009
Abstract:
Tuesday, November 24th, 2009
The role of deleterious mutations in evolution has been much debated. While many researchers believe that any mutation that reduces fitness must impede adaptive evolution, recent studies have shown that this is not always the case. Deleterious mutations may have their fitness effects reversed by a second, sign-epistatic mutation, which can also allow populations to pass through fitness valleys. It is unknown if these sign-epistatic recoveries are fortuitous accidents, or a driving force behind evolution. Using digital organisms, I compared the progress of adaptive evolution when all deleterious mutations were immediately reverted with control treatments in which they were allowed to enter the population. Deleterious mutations reduce fitness over the short term, by definition, and they comprise the majority of mutations in populations of digital organisms, as in biological ones. In my experiments, long-term adaptive evolution was accelerated in those populations in which deleterious mutations were allowed to remain, because some of them served as stepping stones across otherwise impassible fitness valleys, thereby facilitating the evolution of complex features. Wednesday, January 27, 2009
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