Literature DB >> 11289789

A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering.

C Phalakornkule1, S Lee, T Zhu, R Koepsel, M M Ataai, I E Grossmann, M M Domach.   

Abstract

A mixed-integer linear program (MILP) is described that can enumerate all the ways fluxes can distribute in a metabolic network while still satisfying the same constraints and objective function. The multiple solutions can be used to (1) generate alternative flux scenarios that can account for limited experimental observations, (2) forecast the potential responses to mutation (e.g., new reaction pathways may be used), and (3) (as illustrated) design (13)C NMR experiments such that different potential flux patterns in a mutant can be distinguished. The experimental design is enabled by using the MILP results as an input to an isotopomer mapping matrices (IMM)-based program, which accounts for the network circulation of (13)C from a precursor such as glucose. The IMM-based program can interface to common plotting programs with the result that the user is provided with predicted NMR spectra that are complete with splittings and Lorentzian line-shape features. The example considered is the trafficking of carbon in an Escherichia coli mutant, which has pyruvate kinase activity deleted for the purpose of eliminating acetate production. Similar yields and extracellular measurements would be manifested by the flux alternatives. The MILP-IMM results suggest how NMR experiments can be designed such that the spectra of glutamate for two flux distribution scenarios differ significantly. Copyright 2001 Academic Press.

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Year:  2001        PMID: 11289789     DOI: 10.1006/mben.2000.0165

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  10 in total

Review 1.  Thirteen years of building constraint-based in silico models of Escherichia coli.

Authors:  Jennifer L Reed; Bernhard Ø Palsson
Journal:  J Bacteriol       Date:  2003-05       Impact factor: 3.490

2.  Systems-level engineering of nonfermentative metabolism in yeast.

Authors:  Caleb J Kennedy; Patrick M Boyle; Zeev Waks; Pamela A Silver
Journal:  Genetics       Date:  2009-06-29       Impact factor: 4.562

3.  Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network.

Authors:  Iman Famili; Jochen Forster; Jens Nielsen; Bernhard O Palsson
Journal:  Proc Natl Acad Sci U S A       Date:  2003-10-24       Impact factor: 11.205

4.  Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states.

Authors:  Jennifer L Reed; Bernhard Ø Palsson
Journal:  Genome Res       Date:  2004-09       Impact factor: 9.043

5.  Pyruvate kinase-deficient Escherichia coli exhibits increased plasmid copy number and cyclic AMP levels.

Authors:  Drew S Cunningham; Zhu Liu; Nathan Domagalski; Richard R Koepsel; Mohammad M Ataai; Michael M Domach
Journal:  J Bacteriol       Date:  2009-02-27       Impact factor: 3.490

6.  Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae.

Authors:  Tunahan Cakir; Betül Kirdar; Z Ilsen Onsan; Kutlu O Ulgen; Jens Nielsen
Journal:  BMC Syst Biol       Date:  2007-03-27

7.  Effect of plasmid replication deregulation via inc mutations on E. coli proteome & simple flux model analysis.

Authors:  Jonathan Meade; Patrick Bartlow; Ram Narayan Trivedi; Parvez Akhtar; Mohammad M Ataai; Saleem A Khan; Michael M Domach
Journal:  Microb Cell Fact       Date:  2015-03-08       Impact factor: 5.328

8.  Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes.

Authors:  Michael Binns; Pedro de Atauri; Anestis Vlysidis; Marta Cascante; Constantinos Theodoropoulos
Journal:  BMC Bioinformatics       Date:  2015-02-18       Impact factor: 3.169

9.  Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods.

Authors:  Neema Jamshidi; Anu Raghunathan
Journal:  Front Microbiol       Date:  2015-10-06       Impact factor: 5.640

10.  An objective function exploiting suboptimal solutions in metabolic networks.

Authors:  Edwin H Wintermute; Tami D Lieberman; Pamela A Silver
Journal:  BMC Syst Biol       Date:  2013-10-03
  10 in total

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