This New Computational Tool Might Change How We Study Pathogens

A new analysis tool developed by the scientists of Florida State University may signal a new era in the study of population genetics.

The model that they have developed could help revolutionize the way researchers investigate the spread and distribution of dangerous, fast-evolving disease vectors by incorporating advanced mathematical strategies.

This sophisticated, breakthrough research was an interdisciplinary collaboration between Somayeh Mashayekhi, postdoctoral mathematician, and Peter Beerli, computational biologist, both in FSU's Department of Scientific Computing. These findings were published in the journal Proceedings of the National Academy of Sciences.

Beerli said that theirs was the first application of fractional calculus to population genetics and that this will help them to give better estimates of quantities that may be important to combat pathogens.

Called the f-coalescent for its novel use of fractional calculus, the team’s model follows in the lineage of a similar but more limited model called the n-coalescent. Proposed by the British mathematician John Kingman in 1982, the n-coalescent allowed scientists to make statistical statements about a population's past using data collected in the present.

"The n-coalescent introduced a retrospective view of relationships among individuals," Beerli said.

To make probabilistic statements about the origins of different gene variants within that population, this allowed the researchers to use genomic samples from a population. With it, the scientists can now get an unprecedentedly rigorous insight into the scenarios and interactions that helped shape variability in a species over time.

But for all its groundbreaking theoretical advantages, the n-coalescent had one major hindrance: The model operated under the assumption that populations are homogeneous. That is, it assumed each individual shared identical experience, with the same adversities that threaten their survival and the same benefits that give them a competitive leg up.

This is where the FSU team's new f-coalescent advances on its predecessor. The model developed by them allows for increased environmental heterogeneity, specifically in location and time intervals. Better pictures of different genetic variations were yielded-- information that is critical in the analysis of pathogens that evolve rapidly in response to different environments.

Beerli and Mashayekhi, in their study, applied the f-coalescent to three real datasets: the complete genome data of an H1N1 influenza virus strain, mitochondria sequence data of humpback whales, and mitochondrial data of a malaria parasite.

Beerli says that they want to expand the theory beyond a single population as well as include immigration mode.

National Science Foundation funded the research.

By: Preeti Narula

Content: https://www.sciencedaily.com/releases/2019/03/190325101413.htm