Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism

Joanne M. Savinell, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

183 Citations (Scopus)

Abstract

Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism. A stoichiometric matrix accounting for the major pathways involved in energy and mass transformations in the cell was used in our analysis. The auxiliary parameters of linear optimization, the so-called shadow prices, identify the intermediates and cofactors that cause the growth to be limited on each nutrient. This formalism was used to examine how well the cell balances its needs for carbon, nitrogen, and energy during growth on different substrates. The relative values of glucose and glutamine as nutrients were compared by varying the ratio of rates of glucose to glutamine uptakes, and calculating the maximum growth rate. The optimum value of this ratio is between 2-7, similar to experimentally observed ratios. The theoretical maximum growth rate was calculated for growth on each amino acid, and the amino acids catabolized directly to glutamate were found to be the optimal nutrients. The importance of each reaction in the network can be examined both by selectively limiting the flux through the reaction, and by the value of the reduced cost for that reaction. Some reactions, such as malic enzyme and glutamate dehydrogenase, may be inhibited or deleted with little or no adverse effect on the calculated cell growth rate.

Original languageEnglish
Pages (from-to)421-454
Number of pages34
JournalJournal of Theoretical Biology
Volume154
Issue number4
DOIs
Publication statusPublished - 21 Feb 1992

Bibliographical note

Funding Information:
The authors gratefully acknowledge support for this research from the National Science Foundation grant EET-8712756.

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