With the availability of the genome-scale metabolic network models, computational procedures that identify mutant strains for over production of biofuels and other valuable chemicals are widely in use. However, there is a significant gap between the complicated results generated from large-scale computational models and biologically meaningful knowledge that can be easily appreciated and directly utilized by biologists. Such a gap limits the potential impact of genome-scale models on metabolic engineering.
This EAGER project will advance the state-of-the-art in systems metabolic engineering through the development of a new systems identification based metabolic flux analysis tool. Such a tool will not only enable us to bridge the aforementioned gap, but also help infer cellular regulatory mechanisms. It is worth noting that although system identification is a well-established field and has been widely used in many areas, its proposed application to metabolic engineering is the first due to the lack of quality data required by the system identification tools. By combining the specially designed in silico perturbation experiments with system identification tools, biologically meaningful information contained in the complex network structure will be extracted and presented in the form that is easily interpretable by biologists. The proposed approach will be validated using E. coli and S. cerevisiae as the model systems with the focus on the production of advanced biofuels.
This EAGER project will be the first to extend the system identification techniques to study genome-scale metabolic networks. The extracted biological information will not only provide insights on cellular regulatory mechanism, but also enable the validation of metabolic network models using existing biological knowledge that are often qualitative in nature. By validating the proposed approach with E. coli and S. cerevisiae on advanced biofuels production, valuable knowledge on how mutant strains regulate their cellular metabolism will be obtained and will serve as part of the foundation for a future regular proposal.
The proposed approach can be applied to study cellular metabolism of various microorganisms as well as other living cells, such as cancer cells, as long as their metabolic network models are available. The discovered knowledge on cellular regulation mechanisms will have significant impact on metabolic engineering, advanced biofuels, applied microbiology, drug discovery and other areas. In addition, the basic idea of the proposed approach can be extended to study other cellular networks, such as gene regulatory networks and signal transduction networks, to extract valuable information from massive amount of data generated through in silico experiments.
