Seminars and Colloquia by Series

Species network inference under the NMSC

Series
Mathematical Biology Seminar
Time
Wednesday, September 18, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Hector BanosGeorgia Tech

When hybridization plays a role in evolution, networks are necessary to describe species-level relationships. In this talk, we show that most topological features of a level-1 species network (networks with no interlocking cycles) are identifiable from gene tree topologies under the network multispecies coalescent model (NMSC). We also present the theory behind NANUQ, a new practical method for the inference of level-1 networks under the NMSC.

The geometry of phylogenetic tree spaces

Series
Mathematical Biology Seminar
Time
Wednesday, September 11, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Bo Lin Georgia Tech

Phylogenetic trees  are  the fundamental  mathematical  representation  of evolutionary processes in biology. As data objects, they are characterized by the challenges associated with "big data," as well as the  complication that  their  discrete  geometric  structure  results  in  a  non-Euclidean phylogenetic  tree  space,  which  poses  computational  and   statistical limitations.

In this  talk, I  will compare  the geometric  and statistical  properties between a  well-studied framework  -  the BHV  space, and  an  alternative framework that  we  propose, which  is  based on  tropical  geometry.  Our framework exhibits analytic,  geometric, and  topological properties  that are desirable for  theoretical studies in  probability and statistics,  as well  as  increased  computational  efficiency.  I  also  demonstrate  our approach on an example of seasonal influenza data.

Some combinatorics of RNA branching

Series
Mathematical Biology Seminar
Time
Wednesday, September 4, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Christine HeitschGeorgia Tech

Understanding the folding of RNA sequences into three-dimensional structures is one of the fundamental challenges in molecular biology.  For example, the branching of an RNA secondary structure is an important molecular characteristic yet difficult to predict correctly.  However, recent results in geometric combinatorics (both theoretical and computational) yield new insights into the distribution of optimal branching configurations, and suggest new directions for improving prediction accuracy.

Organizational meeting

Series
Mathematical Biology Seminar
Time
Wednesday, August 21, 2019 - 11:00 for 30 minutes
Location
Skiles 006
Speaker
Christine HeitschGeorgia Tech

A brief meeting to discuss the plan for the semester, followed by an informal discussion over lunch (most likely at Ferst Place).

Stochastic models for the transmission and establishment of HIV infection

Series
Mathematical Biology Seminar
Time
Wednesday, March 27, 2019 - 10:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Dan CoombsUBC (visiting Emory)
The likelihood of HIV infection following risky contact is believed to be low. This suggests that the infection process is stochastic and governed by rare events. I will present mathematical branching process models of early infection and show how we have used them to gain insights into the duration of the undetectable phase of HIV infection, the likelihood of success of pre- and post-exposure prophylaxis, and the effects of prior infection with HSV-2. Although I will describe quite a bit of theory, I will try to keep giant and incomprehensible formulae to a minimum.

Inference of evolutionary dynamics of heterogeneous cancer and viral populations

Series
Mathematical Biology Seminar
Time
Wednesday, February 27, 2019 - 11:01 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Pavel SkumsGSU/CDC

Inference of evolutionary dynamics of heterogeneous cancer and viral populations Abstract: Genetic diversity of cancer cell populations and intra-host viral populations is one of the major factors influencing disease progression and treatment outcome. However, evolutionary dynamics of such populations remain poorly understood. Quantification of selection is a key step to understanding evolutionary mechanisms driving cancer and viral diseases. We will introduce a mathematical model and an algorithmic framework for inference of fitness landscapes of heterogeneous populations from genomic data. It is based on a maximal likelihood approach, whose objective is to estimate a vector of clone/strain fitnesses which better fits the observed tumor phylogeny, observed population structure and the dynamical system describing evolution of the population as a branching process. We will discuss our approach to solve the problem by transforming the original continuous maximum likelihood problem into a discrete optimization problem, which could be considered as a variant of scheduling problem with precedent constraints and with non-linear cumulative cost function.

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