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

3 Scopus citations

Abstract

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
Pages25-30
Number of pages6
ISBN (Print)008045108X, 9780080451084
DOIs
StatePublished - 2005

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume16
ISSN (Print)1474-6670

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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