An Integrated Systems Engineering Approach to the Modeling of Cellular Dynamics and Bioreactors
Because of the inherent complex regulation mechanisms of living organisms, the modeling and control of bioreactors present unique challenges to control engineers. The key to addressing this challenge is to obtain a model that can adequately describe the dynamics of cellular metabolism. The PIs plan to use Scheffersomyces stipitis as the model system to answer the following engineering and scientific questions: 1. Engineering question: how to effectively model the dynamics of cellular metabolism and then the dynamics of the bioreactor? 2. Scientific question: what are the regulatory mechanisms that govern the transition of S. stipitis from aerobic growth to anaerobic fermentation?
To answer the first question, they propose a computational framework named flux balance analysis guided dynamic programming (FBA-DP) to model the dynamics of cellular metabolism. Complementary to the computational component is an experimental component, where new equipment and experimental procedures have been developed to obtain dynamic process information required by the computational component. To answer the second question, the time course transcriptome measurements will be obtained using next-generation sequencing technology through a continuing collaboration with Prof. Thomas W. Jeffries (UW?Madison). Through the collaboration with Prof. Q. Peter He (Tuskegee Univ.), the dynamic in silico fluxome details with the in vivo trancriptome data via novel multivariate approaches to elucidate the cellular regulatory mechanisms will be integrated.
Intellectual Merits: o By integrating the optimal control theory with flux balance analysis, the FBA-DP framework should be able to provide genome-wide intracellular details of the dynamic cellular metabolism without requiring in vivo enzyme kinetics information; o This project will produce the first ever data set of the time course transcriptome data on S. stipites during the transition from aerobic to anaerobic condition. Such time course data are highly desirable for researchers who are interested in elucidating the dynamics of the gene regulatory network; o By integrating time course in silico fluxome information with in vivo transcriptome information, the PIs will be able to obtain knowledge on the dynamics of various regulatory mechanisms that govern the cellular transition of S. stipitis from aerobic growth to anaerobic fermentation.
Broader Impacts: o This framework can be applied to study cellular metabolism of various microorganisms and other living cells, such as cancer cells, as soon as their metabolic network models are made available. o The discovered knowledge on cellular regulation mechanisms will provide valuable insights on why S. stipitis, like most other yeasts and fungi, cannot grow anaerobically, which is an unsolved fundamental biological question. In addition, such knowledge will enable significant advancement in metabolic engineering for new strain development. o Because of the bioreactor?s wide applicability, this modeling approach has the potential to not only improve manufacturing in a wide range of industries, such as biofuels, biochemical, food and pharmaceutical industries, and but also to improving the nation?s energy security and sustainability. o This research promotes education of engineers and scientists for bioengineering and biotechnology at both graduate and undergraduate levels. In addition, the projects will actively involve minorities and give them research experience in biotechnology and renewable energy areas.
