Flux-sum analysis identifies metabolite targets for strain improvement.
BACKGROUND
Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.


RESULTS
In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.


CONCLUSIONS
The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.
Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore. meiyappan_lakshmanan@bti.a-star.edu.sg.
Meiyappan
M
Lakshmanan
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. kimty@kaist.ac.kr.|Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea. kimty@kaist.ac.kr.
Tae Yong
TY
Kim
Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore. bevan.ks.chung@gmail.com.
Bevan K S
BK
Chung
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. leesy@kaist.ac.kr.|Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea. leesy@kaist.ac.kr.
Sang Yup
SY
Lee
Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore. cheld@nus.edu.sg.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117576, Singapore. cheld@nus.edu.sg.|Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore. cheld@nus.edu.sg.
Dong-Yup
DY
Lee
D000085:Acetates / Q000378:metabolism; D003198:Computer Simulation; D004926:Escherichia coli / Q000378:metabolism*; D000431:Ethanol / Q000378:metabolism; D005285:Fermentation; D060847:Metabolic Engineering / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D019802:Succinic Acid / Q000378:metabolism
D000085:Acetates; D000431:Ethanol; D019802:Succinic Acid