PhD comprehensive seminar | Nathan Braniff, Characterization of Biological Systems using Time-Varying Optogenetic Stimuli

Thursday, January 11, 2018 1:30 pm - 1:30 pm EST (GMT -05:00)


Nathan Braniff | Applied Math, University of Waterloo


Characterization of Biological Systems using Time-Varying Optogenetic Stimuli


    In traditional engineering domains, mathematical modeling has been used to guide the design process and decrease the experimental effort required to realize functioning products. Metabolic engineers and synthetic biologists have also found models useful in engineering increasingly complex biological systems. However, predictive models, especially of dynamic biological behaviour, are difficult to build and are limited by the available data.
    Optogenetic tools provide a promising approach for efficiently characterizing the time-varying behaviour of biological systems. Such tools enable precise temporal manipulation of gene expression, and can produce rich datasets needed for the calibration of dynamic models. These new experimental tools raise a key question: What is the best way to vary gene expression with optical signals in order to efficiently obtain the data needed to calibrate a predictive model?
    My work addresses this question in a number of ways. I have created a mechanistic model of the CcaS/CcaR optogenetic system to better understand and predict how an optical signal is converted into gene expression changes. I have also reconstructed this system in an E. coli strain to enable further experimental work in the lab. My ongoing efforts focus on implementing Model-based Design of Experiment (MBDOE) methods which can select the most informative time-varying gene expression profile to characterize the dynamics of downstream systems controlled by CcaS/CcaR. As part of this effort, I have constructed models of a metabolic pathway and some common genetic circuits, which we will target with optimized experiments using CcaS/CcaR. Together CcaS/CcaR and MBDOE tools will provide an improved approach to understanding these downstream systems, and allow us to create more predictive models of their dynamic behaviour.