New Approaches for Strain Design in Metabolic Engineering
Author Information
Author(s): Kim Joonhoon, Reed Jennifer L., Maravelias Christos T., Ouzounis Christos A.
Primary Institution: University of Wisconsin-Madison
Hypothesis
Can mixed-integer programming improve the efficiency of strain design in metabolic engineering?
Conclusion
The study presents two new bi-level strain design approaches that significantly enhance the efficiency of identifying genetic modifications for improved biochemical production.
Supporting Evidence
- The new methods reduced CPU times for finding optimal strategies from days to minutes.
- SimOptStrain identified novel strategies for producing compounds that previous methods could not.
- BiMOMA efficiently identified knockout strategies for improved production of glutamate and pyruvate.
Takeaway
Scientists created new methods to help bacteria make more useful stuff, like fuels and chemicals, by changing their genes in smarter ways.
Methodology
The study developed two new mixed-integer programming approaches for strain design: SimOptStrain and BiMOMA, which optimize gene deletions and reaction additions simultaneously.
Limitations
The approaches may miss optimal solutions if the bounds on dual variables are too restrictive.
Digital Object Identifier (DOI)
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