476079 Metabolic Modeling for Improved Bioprocess Efficiency

Sunday, November 13, 2016
Continental 4 & 5 (Hilton San Francisco Union Square)
Peter St. John, Biosciences Center, National Renewable Energy Laboratory, Golden, CO

Research Interests:

Microbial fermentation plays a critical role in efforts to enable sustainable manufacturing of commodity chemicals, and the economic feasibility of the integrated biorefinery concept hinges sharply on the productivity, yield, and titers that can be achieved by metabolic hosts. Flux balance modeling has emerged as a fundamental tool in pushing biological transformations towards profitability, as it enables model-based design of gene knockout and overexpression efforts. My interests center on the unbiased search of intervention strategies for models of central carbon metabolism, together with the consideration of the dynamic systems which govern metabolic flows within the network.

Postdoctoral Projects: Metabolic modeling of industrially relevant organisms for improved yield and productivity

Under supervision of Yannick Bomble and Gregg Beckham, Biosciences Center, National Renewable Energy Laboratory, Golden CO

PhD Dissertation: ÒMathematical Approaches to Understanding Mammalian Circadian RhythmsÓ

Under supervision of Francis J. Doyle III, Department of Chemical Engineering, University of California, Santa Barbara.

Future Direction:

As a faculty I would like to work towards standardizing and accelerating model-based design of metabolic interventions, particularly in non-model organisms. Industrial bioprocesses predominantly leverage specialized organisms to achieve the desired operating conditions, and while these microbial platforms offer unique advantages over traditional hosts, their relative obscurity slows the design/build/test cycle for determining optimum metabolic manipulations. To speed this process I aim to develop standardized workflows and new computational techniques that exploit the overlap in metabolic functionality between classes of microbes. By extracting general trends in gene regulation and enzyme kinetics and combining this knowledge with organism-specific results, searches for the optimum overexpression or gene knockout could explicitly consider the underlying uncertainty in the gene regulatory network.

In addition to metabolic engineering, my interests extend to bio- and cheminformatics, where machine-learning algorithms can be used to extend patterns and trends to new, unmeasured points. I hope to apply these techniques throughout the pipeline from feedstocks to commodity chemicals, as well as in applications in synthetic biology and pharmaceuticals.

Teaching Interests:

My primary teaching interests lie in computational and analytical methods in dynamic systems, statistics, and process control.  As a teaching assistant I have given several guest lectures in introductory statistics, and have instructed undergraduate and graduate students on how to perform computational analyses in numerous programming languages.

Proposal Experience: Mitsubishi Chemical Fellowship
 recipient

NSF GRFP Honorable Mention

Selected Publications: 312 Citations, h-index 4.

Abel, J.H., Meeker, K., Granados-Fuentes, D., St. John, P.C., Wang, T.J., Bales, B.B., Doyle F.J. III, Herzog, E.D., and L.R. Petzold. Functional network inference of the suprachiasmatic nucleus (2016) PNAS, 113 (16) pp. 4512-4517

St. John, P.C. and F.J. Doyle III. Quantifying stochastic noise in cultured circadian reporter cells (2015), PLoS Computational Biology 11(11): e1004451.

St. John, P.C., Taylor, S.R., Abel, J.H., and F.J. Doyle III. Amplitude metrics for cellular circadian bioluminescence reporters (2014) Biophysical Journal, 107 (11) pp. 2712-2722

St. John, P.C., Hirota, T., Kay, S.A. and F.J. Doyle III. Spatiotemporal separation of PER and CRY posttranslational regulation in the mammalian circadian clock (2014) PNAS, 111 (5) pp. 2040-2045.

St. John, P.C. and F.J. Doyle III. Estimating confidence intervals in predicted responses for oscilla- tory biological models (2013) BMC Systems Biology 7:71.

Hirota, T., Lee, J.W., St. John, P.C., Sawa, M., Iwaisako, K., Noguchi, T., Pongsawakul, P.Y., Son- ntag, T., Welsh, D.K., Brenner, D.A., Doyle, F.J. III, Schultz, P.G., Kay, S.A., Identification of small molecule activators of cryptochrome (2012) Science, 337 (6098) pp. 1094-1097.


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