We are developing rational methodologies to predict, control, and optimize the behavior of cellular genetic systems for bioenergy and pharmaceutical applications. We are creating new genetic parts to dynamically control cellular metabolism for the optimal production of fuels, drugs, and materials. We are eliminating trial-and-error from the engineering of biology while quantitatively studying natural biological function. We are advancing Synthetic Biology.

Synthetic Biology: Bridging the Gap between Engineering and Biology

Research Topics

Predicting and Controlling Gene Expression

Gene expression and regulation ultimately controls all of life's cellular processes: self-replication, metabolism, signal transduction, differentiation, and physiology. We develop biophysical models that predict how much and when an RNA or protein is produced according to a gene's specific DNA sequence. Using model predictions, we computationally design and experimentally modify the DNA inside simple organisms to manipulate gene expression rates and to control its cellular dynamics for biotechnological applications. We combine statistical mechanics, kinetics, and thermodynamics to formulate these models and we test their accuracy through systematic, quantitative experimentation on industrially useful or medically relevant bacteria and eukaryotes. We are also interested in predicting the effect of DNA mutations on human health with the hope that understanding the relationship between DNA sequence and disease will enable the development of effective therapeutics.

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  • The Ribosome Binding Site Calculator: Tunable control of the translation initiation rate and protein expression level in bacteria
  • The Small RNA Calculator: Design of synthetic regulatory RNAs to control chromosomal protein expression
genetic parts

Rational Optimization of Synthetic Genetic Systems

Metabolic pathways and genetic circuits are engineered by assembling together genetic parts from diverse organisms or from entirely synthetic components. We develop systematic methodologies for determining which combination of genetic parts, and their DNA sequences, will yield a desired, targeted behavior. Importantly, these techniques will identify optimal protein expression levels without requiring biophysical knowledge of the proteins' interactions, which are often unavailable. As demonstrations, we employ these techniques in the lab to construct and optimize synthetic genetic systems for the maximal production of high-value chemicals from low-value biorenewable feedstock.

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synthetic biodetoxification pathway