Poster abstracts
Poster number 34 submitted by Carlos Owusu-Ansah
Modeling the metabolic consequences of viral infections
Carlos Owusu Ansah (Biophysics Graduate Program, Ohio State University), Marion Urvoy (Department of Microbiology, Ohio State University), Garrett Smith (Department of Microbiology, Ohio State University), Karna Gowda (Department of Microbiology, Ohio State University), Matt Sullivan (Department of Microbiology, Ohio State University)
Abstract:
My research employs computational modeling to investigate how phages reshape microbial metabolism and impact ecosystem nutrient cycling. This work follows two complementary lines of inquiry: understanding how metabolic mutations confer phage resistance, and modeling how viral infection reprograms host metabolism.
Using genome-scale metabolic models of Cellulophaga baltica, we demonstrated that a frameshift mutation in a serine biosynthesis pathway creates a metabolic bottleneck that reconfigures resource allocation throughout central metabolism. This mutation enables phage resistance while simultaneously altering the host's ecological niche, revealing mechanisms by which phage-host coevolution drives metabolic diversity in microbial communities.
My current research focuses on virocell metabolism—examining how phages reprogram infected cells. By integrating virocell experimental data with metabolic models, I'm developing frameworks to quantify how phages redirect host pathways during infection. A key hypothesis I'm investigating is whether phages induce an anabolic shift that conserves carbon atoms for viral biosynthesis rather than energy production. With approximately 20-30% of marine bacteria infected at any time, these metabolic alterations could significantly impact carbon use efficiency and marine biogeochemical cycles. My ultimate goal is to develop generalizable modeling frameworks applicable across diverse virus-host interactions, providing a quantitative foundation for incorporating viral impacts into biogeochemical models and potentially offering insights into viral pathogenesis that could help develop novel antiviral treatments.
Keywords: metabolic modeling, viruses