About me

I am a Research Scientist at Caltech. I was previously a Physics of Living Systems Fellow at Massachusetts Institute of Technology. I hold a Ph.D. in Bioinformatics from Boston University, and an M.S. in Microbial Engineering from the University of Minnesota. My primary focus lies in the study of how the environment shapes the evolution of metabolism at multiple scales, from enzymes to planetary-scale metabolic networks. By exploring the constraints imposed by the environment, I aim to unravel the mechanisms governing the evolution of biochemical systems. My ultimate goal is to shed light on how geochemistry could have transitioned to biochemistry. For further information about me, please refer to my CV, which can be accessed here.

Below are some thoughts and questions that motivate me in these research areas.

Exploring the Origin and Early Evolution of the Biosphere

Understanding the transition from prebiotic chemistry to a complex biochemical network capable of sustaining life on Earth is a profound enigma. I am fascinated by the challenges posed in comprehending the origin and evolution of metabolism, especially given the limited availability of complex biomolecules in ancient environments. While the study of the biosphere’s history has traditionally relied on geochemical signatures in the rock record, recent evidence suggests that valuable clues may still exist within biological systems today. By employing multi-scale network analysis, incorporating physicochemical constraints, and leveraging geochemical support, I aim to “excavate” biochemical “fossils” and reconstruct plausible trajectories of metabolic evolution. Currently, I am working on projects that explore potential paths from ancient geochemical environments to modern-day metabolic networks, utilizing various metabolic modeling techniques.

Machine Learning, Enzyme Discovery, and Biological Dark Matter

In addition to my research on evolutionary biochemistry, I am actively engaged in projects leveraging new machine learning and artificial intelligence (AI) techniques applied to enzyme discovery and the exploration of biological dark matter. Enzymes are vital catalysts that drive numerous biochemical processes in living organisms. However, the vast majority of protein sequences remain poorly characterized due to limitations in traditional experimental techniques. By harnessing the power of machine learning and AI, we can enhance the efficiency and accuracy of protein annotation and enzyme discovery. I am enthusiastic about developing novel algorithms and computational models that leverage large-scale biological data to identify promising enzyme candidates. Through this approach, we can uncover hidden enzymatic activities and potentially unlock new biotechnological applications.