Seminars and Colloquia by Series

Modeling malaria development in mosquitoes: How fast can mosquitoes pass on infection?

Series
Mathematical Biology Seminar
Time
Wednesday, February 26, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Lauren ChildsVirginia Tech

The malaria parasite Plasmodium falciparum requires a vertebrate host, such as a human, and a vector host, the Anopheles mosquito, to complete a full life cycle. The portion of the life cycle in the mosquito harbors both the only time of sexual reproduction, expanding genetic complexity, and the most severe bottlenecks experienced, restricting genetic diversity, across the entire parasite life cycle. In previous work, we developed a two-stage stochastic model of parasite diversity within a mosquito, and demonstrated the importance of heterogeneity amongst parasite dynamics across a population of mosquitoes. Here, we focus on the parasite dynamics component to evaluate the first appearance of sporozoites, which is key for determining the time at which mosquitoes first become infectious. We use Bayesian inference techniques with simple models of within-mosquito parasite dynamics coupled with experimental data to estimate a posterior distribution of parameters. We determine that growth rate and the bursting function are key to the timing of first infectiousness, a key epidemiological parameter.

Species diversity and stability: Is there a general positive relationship?

Series
Mathematical Biology Seminar
Time
Wednesday, February 5, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Lin JiangSchool of Biological Sciences, Georgia Tech

The relationship between biodiversity and ecological stability has long interested ecologists. The ongoing biodiversity loss has led to the increasing concern that it may impact ecosystem functioning, including ecosystem stability. Both early conceptual ideas and recent theory suggest a positive relationship between biodiversity and ecosystem stability. While quite a number of empirical studies, particularly experiments that directly manipulated species diversity, support this hypothesis, exceptions are not uncommon. This raises the question of whether there is a general positive diversity-stability relationship.

Literature survey shows that species diversity may not necessarily be an important determinant of ecosystem stability in natural communities. While experiments controlling for other environmental variables often report that ecosystem stability increases with species diversity, these other environmental variables are often more important than species diversity in influencing ecosystem stability. Studies that account for these environmental covariates tend to find a lack of relationship between species diversity and ecosystem stability. An important goal of future studies is to elucidate mechanisms driving the variation in the importance of species diversity in regulating ecosystem stability.

Pollen patterns as a phase transition to modulated phases

Series
Mathematical Biology Seminar
Time
Wednesday, January 29, 2020 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Asja RadjaHarvard University

Pollen grain surface morphologies are famously diverse, and each species displays a unique, replicable pattern. The function of these microstructures, however, has not been elucidated. We show electron microscopy evidence that the templating of these patterns is formed by a phase separation of a polysaccharide mixture on the cell membrane surface. Here we present a Landau theory of phase transitions to ordered states describing all extant pollen morphologies. We show that 10% of all morphologies can be characterized as equilibrium states with a well-defined wavelength of the pattern. The rest of the patterns have a range of wavelengths on the surface that can be recapitulated by exploring the evolution of a conserved dynamics model. We then perform an evolutionary trait reconstruction. Surprisingly, we find that although the equilibrium states have evolved multiple times, evolution has not favored these ordered-polyhedral like shapes and perhaps their patterning is simply a natural consequence of a phase separation process without cross-linkers.  

Comparing high-dimensional neural distributions with computational geometry and optimal transport 

Series
Mathematical Biology Seminar
Time
Wednesday, November 20, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Eva DyerGeorgia Tech (BME & ECE)

In both biological brains and artificial neural networks, the representational geometry - the shape and distribution of activity - at different layers in an artificial network or across different populations of neurons in the brain, can reveal important signatures of the underlying computations taking place. In this talk, I will describe how we are developing strategies for comparing and aligning neural representations, using a combination of tools from computational geometry and optimal transport.

Network reconstruction using computational algebra and gene knockouts

Series
Mathematical Biology Seminar
Time
Wednesday, November 13, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Matthew MacauleyClemson University

I will discuss an ongoing project to reconstruct a gene network from time-series data from a mammalian signaling pathway. The data is generated from gene knockouts and the techniques involve computational algebra. Specifically, one creates an pseudomonomial "ideal of non-disposable sets" and applies a analogue of Stanley-Reisner theory and Alexander duality to it. Of course, things never work as well in practice, due to issue such as noise, discretization, and scalability, and so I will discuss some of these challenges and current progress.

Generalized Permutohedra from Probabilistic Graphical Models

Series
Mathematical Biology Seminar
Time
Wednesday, November 6, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Josephine YuGeorgia Tech

A graphical model encodes conditional independence relations among random variables. For an undirected graph these conditional independence relations are represented by a simple polytope known as the graph associahedron, which is a Minkowski sum of standard simplices. We prove that there are analogous polytopes for a much larger class of graphical models.   We construct this polytope as a Minkowski sum of matroid polytopes.  The motivation came from the problem of learning Bayesian networks from observational data.  No background on graphical models will be assumed for the talk.  This is a joint work with Fatemeh Mohammadi, Caroline Uhler, and Charles Wang.

Likelihood challenges for big trees and networks

Series
Mathematical Biology Seminar
Time
Wednesday, October 30, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Claudia Solis-LemusUniversity of Wisconsin-Madison

Usual statistical inference techniques for the tree of life like maximum likelihood and bayesian inference through Markov chain Monte Carlo (MCMC) have been widely used, but their performance declines as the datasets increase (in number of genes or number of species).

I will present two new approaches suitable for big data: one, importance sampling technique for bayesian inference of phylogenetic trees, and two, a pseudolikelihood method for inference of phylogenetic networks.

The proposed methods will allow scientists to include more species into the tree of life, and thus complete a broader picture of evolution.

Go with the Flow: a parameterized approach to RNA transcript assembly

Series
Mathematical Biology Seminar
Time
Wednesday, October 23, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Blair Sullivan School of Computing, University of Utah

A central pervasive challenge in genomics is that RNA/DNA must be reconstructed from short, often noisy subsequences. In this talk, we describe a new digraph algorithm which enables this "assembly" when analyzing high-throughput transcriptomic sequencing data. Specifically, the Flow Decomposition problem on a directed ayclic graph asks for the smallest set of weighted paths that “cover” a flow (a weight function on the edges where the amount coming into any vertex is equal to the amount leaving). We describe a new linear-time algorithm solving *k*-Flow Decomposition, the variant where exactly *k* paths are used. Further, we discuss how we implemented and engineered a general Flow Decomposition solver based on this algorithm, and describe its performance on RNA-sequence data.  Crucially, our solver finds exact solutions while achieving runtimes competitive with a state-of-the-art heuristic, and we discuss the implications of our results on the original model selection for transcript assembly in this setting.

Host metapopulation, disease epidemiology and host evolution

Series
Mathematical Biology Seminar
Time
Wednesday, October 16, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Jing JiaoNIMBioS - University of Tennessee

While most evolutionary studies of host-pathogen dynamics consider pathogen evolution alone or host-pathogen coevolution, for some diseases (e.g., White Nose syndrome in bats), there is evidence that hosts can sometimes evolve more rapidly than their pathogen. In this talk, we will discuss the spatial, temporal, and epidemiological factors may drive the evolutionary dynamics of the host population. We consider a simplified system of two host genotypes that trade off factors of disease robustness and spatial mobility or growth. For diseases that infect hosts for life, we find that migration and disease-driven mortality can have antagonistic effect on host densities when disease selection on hosts is low, but show synergy when selection is high. For diseases that allow hosts to recover with immunity, we explore the conditions under which the disease dies out, becomes endemic, or has periodic outbreaks, and show how these dynamics relate to the relative success of the robust and wild type hosts in the population over time. Overall, we will discuss how combinations of host spatial structure, demography, and epidemiology of infectious disease can significantly influence host evolution and disease prevalence. We will conclude with some profound implications for wildlife conservation and zoonotic disease control.

Partially ordered Reeb graphs, tree decompositions, and phylogenetic networks

Series
Mathematical Biology Seminar
Time
Wednesday, October 9, 2019 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Anastasios StefanouMathematical Biosciences Institute, Ohio State University

Inspired by the interval decomposition of persistence modules and the extended Newick format of phylogenetic networks, we show that, inside the larger category of partially ordered Reeb graphs, every Reeb graph with n leaves and first Betti number s, is equal to a coproduct of at most 2s trees with (n + s) leaves. An implication of this result, is that Reeb graphs are fixed parameter tractable when the parameter is the first Betti number. We propose partially ordered Reeb graphs as a natural framework for modeling time consistent phylogenetic networks.  We define a notion of interleaving distance on partially ordered Reeb graphs which is analogous to the notion of interleaving distance for ordinary Reeb graphs. This suggests using the interleaving distance as a novel metric for time consistent phylogenetic networks.

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