A System Identification Based Framework for Metabolic Network Analysis
Description:
Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, where they are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information. These models provide a holistic view of the organism’s metabolism and constraint-based metabolic flux analysis methods have been used extensively to study genome-wide cellular metabolic networks. However, due to the scale and complexity involved in genome-scale models, how to validate the model and how to extract mechanistic insights from the model have been challenging. To address this challenge, we have developed a system identification based framework to extract qualitative biological information (such as how different pathways interact with each other) from quantitative numerical results by performing carefully designed in silico experiments. This framework has been applied to compare genome-scale models, to guide the improvement of genome scale models, as well as to enhance phenotype phaseplane analysis.
Funding:
NSF 1264861
Students working on this project:
Graduate: Andy Damiani
Undergraduate: Jeffrey Liu
Kinetic Modeling of Co-Culture Systems with a Novel Bioreactor and Pseudo-Continuous Fermentation
Description:
In view of rising concerns over energy sustainability and global warming, lignocellulosic biofuels have been identified as promising long-term renewable energy sources. Complete substrate utilization is one of the prerequisites to render lignocellulosic biofuel processes economically competitive. For lignocellulosic ethanol, fermentation of hexoses (i.e., glucose, mannose, and galactose) using Saccharomyces cerevisiae is well established on a large scale. However, the conversion of pentoses (i.e., xylose and arabinose) to ethanol is still one of the major barriers of industrializing lignocellulosic ethanol processes. Compared to the use of a single recombinant strain, co-culture of specific microbes that exploits their native capabilities to metabolize different sugar components of lignocellulosic hydrolysates is a promising alternative. Recently, research on using different co-culture systems for lignocellulosic ethanol fermentation has drawn significant interest, mainly due to their flexibility, tunability, and increased resistance to environmental stress. However, up to now, very limited research has been done to systematically investigate the dynamic properties and interaction of co-culture strains due to the lack of effective co-culture equipment and dynamic modeling tools. In this project, we develop a novel membrane separated bioreactor, together with pseudo-continuous fermentation to conduct systematic research on a co-culture system (Saccharomyces cerevisiae and Scheffersomyces stipitis).
Funding:
USDA-AFRI 10252981 and Sun Grant
Students working on this project:
Graduate: Min Hea Kim, Andy Damiani, Kyle Stone
Undergraduate: Tomi Adekoya
Dynamic Modeling of Metabolic Network Systems
Description:
Constraint-based approaches to model cellular metabolism have made significant progress in the last few decades, and hundreds of methods have been developed. Relying on the stoichiometry of the reaction network and an optimization procedure, these methods describe the steady-state of the metabolic network without requiring kinetics information. However, these methods cannot describe the cell’s dynamic behavior, and have difficulties in incorporating the information of cellular regulation. Although flux balance analysis (FBA, the most commonly applied constraint-based approach) has been extended to dynamic versions, limitations exist and further improvement is needed. In this area, we aim to develop a computation framework, named flux balance analysis guided dynamic programming (FBA-DP), to model the dynamics of cellular metabolism. In addition, we have developed new equipment and experimental procedures to achieve precise control of operation condition as well as high-frequency data acquisition required to develop the computational framework.
Funding:
NSF 1264861, NSF-IGERT
Students working on this project:
Graduate: Andy Damiani, Kyle Stone
Statistics Pattern Analysis-Next Generation Process Monitoring Framework
Description:
Manufacturing processes are transitioning from labor-intensive operation towards automation intensive operation. This trend has significantly reduced human intervention to the process and created more “black boxes” where little direct knowledge is available regarding what is going on with the process. At the same time, due to the advances in sensor, control and information technologies, industry of all sizes and all types are sitting on mountains of data. Therefore, it is crucial to extract meaningful information from large and complex data sets in order to assess process performance and improve process efficiency and product quality. At the same time, modern manufacturing processes present some challenges that cannot be readily addressed by existing methods, such as nonlinear dynamics, non-Gaussianity, frequent process changes driven by manufacturing on-demand, as well as overwhelming amount of data-preprocessing required by batch processes. In this area, we develop a next generation process monitoring framework – Statistic Pattern Analysis to help address these challenges. By monitoring the selected statistics of process variables, instead of process variables themselves, the new framework effectively addresses process non-Gaussianity, nonlinearity as well as eliminating/reducing labor intensive data pre-processing.
Funding:
Alabama Innovation Fund & Anderson Foundation and DOE GAANN
Students working on this project:
Graduate: Xiu Wang
Understanding Cellular Metabolism of Methanotrophs
Description:
Natural gas has emerged as a potential major feedstock and a more sustainable resource for the production of fuels and valuable chemical products due to two reasons: (1) the large abundance found in the U.S. and the world, and (2) potential benefit of reducing greenhouse gas (GHG) emissions. Compared to chemical conversion of methane to liquid fuels, usually through the Fischer-Tropsch process, biological methane conversion through methanotrophs offers many advantages: low capital cost, high selectivity at ambient temperature and pressure. However, many challenges exist for a commercially viable biological conversion route due to the low production and low yield. In order to develop a highly efficient bio-conversion route, a better biocatalyst is necessary. In this project, we apply the experimental and computational tools developed by our group to gain better understanding on the cellular metabolism of a methanotroph, which will provide a foundation for developing an improved biocatalyst.
Funding:
DOE GAANN
Students working on this project:
Graduate: Kyle Stone, Andy Damiani
Undergraduate: Krishane Suresh
