Great Lakes Bioinformatics Conference 2012

Keynote Speakers

Updated April 16, 2012
Howard Cash, President
Gene Codes Corporation
Gene Codes Forensics, Inc.
Ann Arbor, MI - USA

Biosketch (.pdf)

Title: Designing Bioinformatics for the Wetware.  Usability Challenges with Massive Amounts of Data.

Abstract:   Gene Codes Corporation began producing DNA sequencing tools that dominated the commercial market starting in the mid-1990s.  Starting in 1997 and especially after 9/11, 2001, the company produced tools that completely changed the standard for DNA analysis software in forensic crime labs around the world.   In neither case did success depend on new discoveries or dramatically more sensitive pattern detection algorithms.  Instead, the Sequencher and M-FISys programs, respectively, raised the game on usability for the bench scientist.

For people in bioinformatics, the changes in data scale have been dramatic over the last several years.  How do we make that information available to the people who will benefit from it?  If a consumer could truly get a full genome in a matter of seconds as portrayed in the dystopian movie, GATTACA, surely they would not want a listing of all of the individual data points.  So where might bioinformatics professional focus in the future?   We will briefly discuss where bioinformatics has come in the days since MolGen, UWGCG and IntelliGenetics and some of the principles that can help tool developers who focus on the data present that information so that end users can grasp its content.

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Michael Lynch, Professor
Department of Biology
Indiana University
Bloomington, IN - USA


Title: Mutation, Drift, and the Evolution of Subcellular Features

Understanding the mechanisms of evolution and the degree to which phylogenetic generalities exist requires information on the rate at which mutations arise and their effects at the molecular and phenotypic levels. Although procuring such data has been technically challenging, high-throughput genomic sequencing is rapidly expanding our knowledge in this area. Most notably, information on spontaneous mutations, now available in a wide variety of organisms, implies an inverse scaling of the mutation rate (per nucleotide site) with the effective population size of a lineage. The argument will be made that this pattern naturally arises as natural selection pushes the mutation rate down to a lower limit set by the power of random genetic drift rather than by intrinsic molecular limitations on repair mechanisms. Additional support for this idea derives from the relative levels of efficiency of DNA polymerases and mismatch-repair enzymes in eukaryotes relative to prokaryotes.

This drift-barrier hypothesis has general implications for all aspects of evolution, including the performance of enzymes and the stability of proteins. The fundamental assumption is that as molecular adaptations become more and more refined, the room for subsequent improvement becomes diminishingly small. If this hypothesis is correct, the population-genetic environment imposes a fundamental constraint on the level of perfection that can be achieved by any molecular adaptation. It also implies that effective neutrality is the expected outcome of natural selection, an idea first suggested by Hartl et al. in 1985.

Although generally viewed as an independent process, mutation also operates as a weak selective force, thereby playing a central role in “nearly neutral” hypotheses in evolution. Most notably, genes and proteins with more complex structures are subject to higher rates of mutational degeneration simply because they are larger mutational targets. However, because the mutation rate is very low at the nucleotide level, the efficiency of such mutation-associated selection becomes of diminishing significance in populations with small effective sizes. Thus, mutationally hazardous genomic and gene-structural features, which may or may not be adaptive, are expected to passively arise in lineages with small effective sizes. This general principle, the mutational-hazard theory, will be illustrated with examples including: 1) the differential expansion of intron numbers in various phylogenetic lineages; and 2) the diversification of protein-architectural features.

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Mercedes Pascual, Professor
Department of Ecology and Evolutionary Biology
Affiliate of the Center for Computational Medicine and Bioinformatics
University of Michigan
Ann Arbor, MI - USA


Title:  Pathogen Diversity from an Ecological Perspective

Abstract: Sequence data is becoming increasingly available for many pathogens in time and space.  This presents the opportunity to describe their population structure at different scales and to address the role of this structure for epidemiology.  In particular, the genetic diversity associated with antigenic phenotypes, the variation recognized by the immune system, is of interest.  Here, conceptual models with a basis in competitive ecological interactions provide a basis to understand whether immune selection (competitive interactions between strains mediated by cross-immunity) structures pathogen populations and how this structure influences in turn the transmission dynamics of pathogens.   In this talk, I present two examples from our work on Plasmodium falciparum malaria and H3N2 influenza respectively, with individual-based models of transmission that track the history of infection of individual hosts and the genetic relatedness of the pathogen.  Empirical analyses and considerations on testing  resulting predictions on this ‘strain theory’ are discussed.

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Russell Schwartz, Professor
Department of Biological Sciences
and Lane Center for Computational Biology
Carnegie Mellon University
Pittsburgh, PA - USA


Learning Population Histories from Genome Variation Data

High throughput sequencing technologies have made it possible to assemble vast libraries of genetic variation data describing how genomes differ from one person to another.  Such data implicitly encode a history of our species at a population level due to the gradual accumulation of mutations in our genomes as we have developed from an initial founder population to the many diverse subpopulations that comprise our species today.  Better characterizing this history could be useful not only to basic research but also to important practical problems in improving the effectiveness of genotype/phenotype association studies and better understanding how we have adapted to historical disease threats or environmental changes.  Reconstructing this population history from the indirect evidence of random mutations is a challenging computational problem, but also an interesting problem from a computational biologist’s perspective because of a long history of different approaches reflecting different biological perspectives and drawing on different classes of computational methods.  In this talk, we will examine new strategies for characterizing population history that attempt to synthesize some of these different perspectives into a more complete model of our history as a species.  This work seeks to combine discrete algorithms and operation research methods arising from the field of phylogenetics with machine learning and statistical sampling methods arising from statistical genetic models of population substructure.  We will examine the motivation behind these approaches and explore their development from a computational perspective.  We will then see what we can learn by applying such methods to real data.  In the process, we will see some of the many ways diverse computing paradigms can contribute to current research in human genetics.

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