Michael Quintin Successfully Defends His Thesis

Congratulations to Michael Quintin on the successful defense of his thesis, "Computational Biology of microbial Genomes and Metabolism for Engineering and Ecology Applications." Great job Michael!

To learn more about Michael's thesis, read his abstract below:

ABSTRACT

Progress in the biological sciences will increasingly require tools and algorithms that automate research processes, including hypothesis generation, experiment design, data analysis, and the simulation of possible scenarios that could help prioritize experiments and formulate possible fundamental principles of biological dynamics and evolution. Many efforts in computational biology are focused either on processing data that is too large or too complex for an individual researcher to handle, or on formulating mechanistic models that try to explain and predict the behavior of biological systems. This thesis presents work I have done towards the quantitative understanding of how microbial species and communities allocate their metabolic resources for growth and survival. Specifically, I enhanced and modified an existing software platform called COMETS (Computation of Microbial Ecosystems in Time and Space), in order to facilitate in silico experiments of complex microbial communities through dynamic flux balance analysis. The Matlab package I developed serves as a programmatic interface to COMETS, with many applications for the study of spatially structured natural and synthetic communities. I further modified COMETS to allow the simulation of chemical reactions outside of cells, including those carried out by secreted enzymes. This new module allowed me to ask questions about  tradeoffs encountered by microbes that degrade extracellular cellulose through the secretion of costly cellulolytic enzymes. I demonstrate here that for a yeast constitutively expressing cellulase, optimal expression rates are sensitive to the initial amount of cellulose present, and to a wide range of intracellular and environmental parameters.

Major Professor: Daniel Segre