Optimal selection of enzyme levels using large-scale kinetic models

Evgeni V. Nikolaev, Priti Pharkya, Costas D. Maranas, Antonios Armaou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


A hybrid optimization framework is introduced to identify enzyme sets and levels to meet overproduction requirements using kinetic models of metabolism. A simulated annealing algorithm is employed to navigate through the discrete space of enzyme sets while a sequential quadratic programming method is utilized to identify optimal enzyme levels. The framework is demonstrated on a model of E.coli central metabolism for serine biosynthesis. Computational results show that by optimally manipulating relatively small enzyme sets, a substantial increase in serine production can be achieved. The proposed approach thus provides a versatile tool for the elucidation of controlling enzymes with implications in biotechnology.

Original languageEnglish (US)
Title of host publicationProceedings of the 16th IFAC World Congress, IFAC 2005
PublisherIFAC Secretariat
Number of pages6
ISBN (Print)008045108X, 9780080451084
StatePublished - 2005

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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