Trinity Term 2017
TRINITY TERM 2017
Week 7: (Double Distinguished Speaker Seminar) Friday 9th June, 2.00 pm - 4.30 pm
2.00 pm - 3.00 pm
Speaker: David Swofford, Senior Research Scientist, Biological Sciences, Duke University
Title: Generality and Robustness of the SVDQuartets Method for Phylogenetic Species Tree Estimation
Abstract: Methods for inferring evolutionary trees based on phylogenetic invariants were first proposed nearly three decades ago, but have been virtually ignored by biologists. A new invariants-based method for estimating species trees under the multispecies coalescent model was recently developed by Julia Chifman and Laura Kubatko, building on earlier work by Elizabeth Allman, John Rhodes, and Nicholas Eriksson. This method comes from algebraic statistics and uses singular value decomposition to estimate the rank of matrices of site pattern frequencies. Although the approach shows great promise, its performance on empirical and simulated data sets has not been adequately evaluated.
I will give a general introduction to the SVDQuartets method and present some results from a simulation study currently in progress (collaboration with Laura Kubatko and Colby Long) that demonstrate that SVDQuartets is potentially highly robust to deviations from the standard evolutionary models assumed by other species-tree estimation methods.
3.00 pm - 3.30 pm Tea, coffee and biscuits in the ground floor social area
3.30 pm - 4.30 pm
Speaker: Wim Hordijk, The KLI Institute, Klosterneuburg, Austria
Title: Autocatalytic Sets and the Origin of LifeAbstract: The main paradigm in origin of life research is that of an RNA world, where the idea is that life started with one or a few self-replicating RNA molecules. However, so far nobody has been able to show that RNA can catalyze its own template-directed replication. What has been shown experimentally, though, is that certain sets of RNA molecules can mutually catalyze each other's formation from shorter RNA fragments. In other words, rather than having each RNA molecule replicate itself, they all help each other's formation from basic building blocks, in a self-sustaining network of molecular cooperation.
Such a cooperative molecular network is an instance of an autocatalytic set, a concept that was formalized and studied mathematically and computationally as RAF theory.This theory has shown that autocatalytic sets are highly likely to exist in simple polymer models of chemical reaction networks, and that such sets can, in principle, be evolvable due to their hierarchical structure of many autocatalytic subsets. Furthermore, the framework has been applied succesfully to study real chemical and biological examples of autocatalytic sets.
In this talk I will give a general (and gentle) introduction to RAF theory, present its main results and how they could be relevant to the origin of life, and argue that the framework could possibly also be useful beyond chemistry, such as in analyzing ecosystems or even economic systems.
Week 8: Friday 16th June, 3.30 p.m.
Speaker: Professor Peter Hoff, Department of Statistics, Duke University
Title: Adaptive FAB confidence intervals with constant coverage
Abstract: Confidence intervals for the means of multiple normal populations are often based on a hierarchical normal model. While commonly used interval procedures based on such a model have the nominal coverage rate on average across a population of groups, their actual coverage rate for a given group will be above or below the nominal rate, depending on the value of the group mean.
In this talk I present confidence interval procedures that have constant frequentist coverage rates and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard t-intervals on average across groups.
These intervals are obtained by inverting Bayes-optimal frequentist tests, and so are "frequentist, assisted by Bayes" (FAB). I present some asymptotic optimality results and some extensions to other scenarios, such as linear regression and tensor analysis.