Karna Gowda

Karna Gowda

Karna Gowda

Assistant Professor of Microbiology


(614) 688-3830

900 Riffe Building
496 W. 12th Ave

Areas of Expertise

  • Microbial Ecology & Evolution
  • Quantitative biology
  • Applied Mathematics


  • B.S., University of Illinois Urbana-Champaign, 2008
  • Ph.D., Northwestern University, 2017
  • Postdoc, University of Chicago and University of Illinois Urbana-Champaign, 2017-2023


Research Interests

Microbial communities are the engines that drive the biosphere, playing critical biochemical roles in ecosystems and hosts, from soils to oceans to the human gut. The activities of a community emerge from the collective action of its many constituent parts: molecules, genes, taxa, networks of ecological interactions, and environmental context. Understanding these systems to an extent that allows us to predict how natural communities will respond to climate change and design consortia for the benefit of human health requires developing new “languages” for coming to grips with the staggering complexity. Just as mathematics has proven an unreasonably effective language in the physical sciences, the combination of quantitative experimental approaches and measurements with mathematical tools and reasoning points a way forward to deciphering the biosphere’s most complex phenomena.

Current projects:

  • Division of labor in bacterial denitrification. Much like a factory production line, microbial communities divide the burden of metabolic “labor” over many species that take on different roles. This is often the case in denitrification, a form of anaerobic respiration whereby nitrate is transformed into nitrogen gas through a series of intermediates. Our recent study demonstrates that the division of labor in denitrification is strongly modulated by pH in natural soils. The goal of his project is to understand the physiological basis of this phenomenon through quantitative proteomics, bioinformatics, and mathematical modeling.
  • Monod v. Malthus: environmental controls on gene expression and abundance turnover. Organisms in natural communities can respond to rapid changes in the environment by either modulating expression profiles or by turning over abundances. A vivid example of this occurs in the Earth’s oceans, where entire communities appear to prefer either expression or turnover-based response depending on the temperature of the environment. The goal of this project is to interrogate how characteristics of abiotic environments favor one strategy or the other, using a combination of mathematical modeling, bioinformatics, and experiments on model systems.
  • In situ prediction of microbial community dynamics. The more deeply we measure natural microbiomes, the more complex they appear. Recent global multi-omics surveys of communities in soils, oceans, and human guts reveal a staggering degree of complexity, from a vast collection of sequences of unknown function (“microbial dark matter”) to a level of population heterogeneity that upends our notion of species. How can we formulate and test predictions about community dynamics in these complex circumstances? The goal of this project is to develop generalizable and interpretable machine learning approaches for using multi-omics data from natural microbiomes to make predictions about dynamical properties of the community.


Recent Publications:


Google Scholar