This work explores the crucial link between biological organization and dynamics in microbes. I have developed and applied different sets of quantitative tools to investigate this theme using a multi-disciplinary approach.

My first project uses flow optimization tools to explore microbial intra-cellular dynamics as a function of resources, cell structure, and trade-offs between major energy production processes. This model explains emergent cell-level properties such as the dependence of bacterial growth rates on cell shape, size patterns in long-term evolutionary experiments, and the intriguing Warburg effect – wherein cells use inefficient anaerobic energy production pathways instead of efficient aerobic processes, despite ample oxygen availability.

Moving to the community-level, I explored the mechanisms by which high levels of diversity are maintained in natural systems. Via a cutting-edge agent-based model that incorporates biologically realistic higher-order interactions, a continuous species space in which microbes interact via antibiotics and defenses, and an evolution-via-mutation regime, I link the trajectory of community assembly to both spatial heterogeneity patterns and diversity dynamics, thereby providing a new perspective on the origin and maintenance of diversity in complex communities.

At the ecosystem-level, I built a dynamical system model to explore how the availability of resources and oxygen—mediated by interactions among microbial functional groups—affect environmental production of major greenhouse gases, like methane, especially on early Earth. This work provides 1) the first ecosystem-based functional explanation of periodic global methane ‘hazes’ in Earth’s early atmosphere and 2) a way to predict oxygen concentrations of ancient environments in a novel way.

Lastly, in research that can involve multiple scales of biological organization, I use novel network science and information theoretic metrics to understand differences in interaction patterns in networks through the lens of degeneracy, noise, and determinism. The tradeoff between degeneracy and determinism can help us define the most informative scale of a network, and this is explored in the project through the development of new openly accessible toolkits.