Inferring epistasis for HIV evolution
ORAL
Abstract
Biological systems such as viruses evolve under natural selection as well as stochastic fluctuations. Often, such natural selection is characterized as a fitness landscape that is closely related to the free energy landscape in statistical physics and can involve pairwise interactions between mutantions. Inferring fitness landscapes is theoretically and practically important to predict patterns of future evolution.
Recent work used a path integral approach to infer the fitness effects of mutations[1], including pairwise epistatic interactions, from evolutionary histories or “paths”[2]. However, naively applying this approach results in computational costs that increase rapidly along with the number of mutations considered, making it difficult to apply to real data in cases with high genetic variation. In this talk, we describe the epistatic effects of mutations inferred from HIV evolution, where many mutations are present, using modified, computationally-efficient approach.
[1] Sohail, M. S., Louie, R. H., McKay, M. R., & Barton, J. P. (2021). MPL resolves genetic linkage in fitness inference from complex evolutionary histories. Nature biotechnology, 39(4), 472-479.
[2] Sohail, M. S., Louie, R. H., Hong, Z., Barton, J. P., & McKay, M. R. (2022). Inferring epistasis from genetic time-series data. Molecular Biology and Evolution.
Recent work used a path integral approach to infer the fitness effects of mutations[1], including pairwise epistatic interactions, from evolutionary histories or “paths”[2]. However, naively applying this approach results in computational costs that increase rapidly along with the number of mutations considered, making it difficult to apply to real data in cases with high genetic variation. In this talk, we describe the epistatic effects of mutations inferred from HIV evolution, where many mutations are present, using modified, computationally-efficient approach.
[1] Sohail, M. S., Louie, R. H., McKay, M. R., & Barton, J. P. (2021). MPL resolves genetic linkage in fitness inference from complex evolutionary histories. Nature biotechnology, 39(4), 472-479.
[2] Sohail, M. S., Louie, R. H., Hong, Z., Barton, J. P., & McKay, M. R. (2022). Inferring epistasis from genetic time-series data. Molecular Biology and Evolution.
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Presenters
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Kai Shimagaki
University of California, Riverside
Authors
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Kai Shimagaki
University of California, Riverside
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John P Barton
Department of Computational and Systems Biology; University of Pittsburgh School of Medicine, University of California, Riverside, University of California, Riverside / University of Pittsburgh School of Medicine