FSU professor receives NSF grant to create new software tool

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From left to right, Peter Beerli, professor in the Department of Scientific Computing at FSU College of Arts and Sciences, and former FSU postdoctoral researcher Somayeh Mashayekhi. (Florida State University)

The National Science Foundation awarded a professor at Florida State University a grant of $ 410,000 to create a software tool designed to help scientists make more accurate predictions about populations of endangered or commercially exploited animal species.

Peter Beerli, professor in the Department of Scientific Computing at FSU College of Arts and Sciences, will lead the project to develop the new tool, which will help generate more accurate estimates of the population size and genetic diversity of different cash. The new software could help solve problems such as controlling disease outbreaks, improving the regulation of catch quotas for commercial fisheries and preserving endangered species.

Beerli will work with former FSU postdoctoral researcher Somayeh Mashayekhi, now an assistant professor of mathematics at Kennesaw State University in Georgia, to develop the new tool.

“Somayeh and I are very excited because this grant allows us to improve on the work we did while she was here at FSU, and it expands on one of the fundamental theories in my field of genetics. populations, ”Beerli said.

The team plans to build on a mathematical theory they developed in 2019 that generalizes the theory of coalescence, a model that uses the genealogy of a random sample of individuals in a population to make statements about the population according to the descent of common traits.

“Currently our generalization is limited and cannot discuss data from multiple populations,” Beerli said. “We will be expanding our theory and our existing open source inference program, MIGRATE, to include these new findings. “

After using simulated data to test the new software, the team will collaborate with researchers using MIGRATE software to reanalyze a variety of real data sets. This will help researchers to establish correlations between the variability in the number of offspring and the life history of different species.

The new software also aims to solve an important problem that exists with current theory of population genetics: the assumption that a given population exists in a homogeneous environment.

“With natural populations, we know this is not true, as some individuals are lucky and have offspring under conditions where all of them survive, while others may not produce any offspring at all. “Beerli said. “We are proposing a theoretical breakthrough that allows us to measure this heterogeneity in populations. This will lead to better predictions and better maintenance of populations of interest, for example, the maintenance of threatened or commercially interesting species.”

In addition to applications for predicting animal populations, the software will also serve as a valuable new tool in the global fight to stop the spread of various infectious diseases threatening mankind. Beerli cites the different strains of coronavirus in the COVID-19 pandemic as an example of why this improved accuracy is so important in curbing the effects of pathogens in a population.

“This research addresses the hypothesis that the populations studied have a relatively constant number of offspring per generation,” he said. “Scientific observation has shown that this assumption is incorrect. For example, some strains of SARS-CoV-2 are more successful at infecting humans than others, suggesting that the ancestor with a new mutation has many more “offspring” than others. “

Department of Scientific Computing Chairman Gordon Erlebacher said the new software would be a boon to the research community and the resulting improved accuracy will reduce bias in the translation and analysis of data used in political decisions.

“Peter Beerli is world famous in the field of population genetics,” said Erlebacher. “In these days of national emergency and disinformation or disinformation, models that suppress unrealistic assumptions are becoming increasingly relevant. This award will contribute to the department’s mission to train students in advanced modeling and computational techniques with applications in a wide range of fields.


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