DESC0024840

Scaling bioproduction processes from laboratory and pilot scale systems up to commercial scale is one of the principal problems facing the new bioeconomy, and one towards which the Department of Energy has invested substantial resources. A key component of scaling these processes is developing process control systems to ensure their repeatability, efficiency, and predictability. The research proposed in this project will focus on using mathematical techniques from dynamical systems theory to develop advanced software systems for improving the analysis of the gene expression patterns of one of the most important the organisms used to drive bioproduction processes, Saccharomyces cerevisiae (Ĺyeastĺ). Analysis of gene expression gives unparalleled access to the internal states of the organism during the production process, enabling more precise and efficient control. Techniques from the mathematics of dynamical systems will be used to design algorithms and build software packages that provide comprehensive comparisons of the complex datasets that are produced from gene expression analysis. In Phase I, a library of gene expression data from biofuels fermentations will be compiled and curated, based in part on data that Mimetics has already generated as well as on data from publicly available repositories. Algorithms incorporating mathematical analyses and statistical techniques will be designed, coded, and tested on the datasets in this library. Once the algorithms have been implemented, further analysis will determine the genes whose expression is most significant. Continuation of the research can embed these algorithms in ĹSoftware as a Serviceĺ systems that can be made available by subscription to industry, government, and academia to enable much more rapid design and scale-up of bioproduction systems. Further research can extend the work from Saccharomyces to other fungi and bacteria used in bioproduction. In this way aa suite of software tools will become available to the bioproduction community to enable more rapid progress from the lab to the market.