Enzyme cost minimization
Data and software |
Project "Enzyme dynamics and function"
Escherichia coli model As
an example, we constructed a kinetic metabolic model
of E. coli central metabolism and predict its
metabolite and enzyme levels. The enzyme profiles are
shaped by factors such as catalytic constants,
thermodynamic driving forces, enzyme saturation, and
allosteric regulation, and being energetically unfeasible
turns out to be a limiting case of high enzyme
cost. Balance relations between the costs of adjacent
enzymes provide a link to metabolic control analysis.
Aside from predicting the enzyme and flux costs, ECM also
leads to general flux cost functions, which can be used to
bridge the gap between constraint-based and kinetic
Enzyme Cost Minimization was used to build and
analyze a model E. coli central metabolism. Below, the
results can be found in the SBtab table format (for models and
data) and SBML (for
Data We ran ECM three times based on different assumptions about protein
burden: uniform cost (same cost for each enzyme molecule);
size-dependent cost (cost proportional to chain length); and
composition-dependent cost (accounting for the different ATP
investment in different amino acids). It turned out that these choices
have little effect on the prediction accuracy.
The NEOS files can be used for running optimisations on the NEOS
Optimization Server: please unzip the file and upload the individual
files to the NEOS
website for enzyme cost minimization. The fields for files
"moietymet", "moietyval", "alpha", and "A" can be left open.