Microbial Strain Design for Biochemical Production Using Mixed-integer Programming Techniques

Joonhoon Kim, Jennifer L. Reed, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review


Metabolic engineering aims to improve biochemical production in a biological system using genetic modifications. To identify these modifications, conventional approaches have focused on local metabolic pathways without considering the global effects on metabolic behavior. Computational approaches using genome-scale models have successfully identified genetic modifications while considering their global effects on metabolism. However, these approaches can take a significant amount of time to find the optimal modifications due to the large size of metabolic networks. In this work, we present mixed-integer programming (MIP) methods and analysis of bounds on dual variables for fast and effective identification of genetic modification strategies. Using the bi-level optimization approach by Kim and Reed, 2010, we demonstrate how our MIP methods significantly improve performance of strain design algorithms.

Original languageEnglish (US)
Pages (from-to)1306-1310
Number of pages5
JournalComputer Aided Chemical Engineering
StatePublished - 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications


  • Metabolic engineering
  • Mixed-integer programming
  • Strain design


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