The fractional contributions of elementary modes to the metabolism of Escherichia coli and their estimation from reaction entropies.
The metabolism of a cell can be viewed as a weighted sum of elementary modes. Due to the multiplicity of modes the identification of the individual weights represents a non-trivial problem. To enable the determination of weighting factors we have identified and implemented two gene deletions in combination with defined growth conditions that limit the metabolism from 4374 original elementary modes to 24 elementary modes for a non-PHB synthesizing control and 40 modes for a PHB synthesizing strain. These remaining modes can be further grouped into five families that have the same overall stoichiometry. Thus, the complexity of the problem is significantly reduced, and weighting factors for each family of modes could be determined from the measurement of accumulation rates of metabolites. Moreover, it is shown that individual weights are inversely correlated with the entropy generated by the operation of the used pathways defined in elementary modes. This suggests that evolution developed cellular regulatory patterns that permit diversity of pathways while favoring efficient pathways with low entropy generation. Furthermore, such correlation provides a rational way of estimating metabolic fluxes based on the thermodynamic properties of elementary modes. This is demonstrated with an example in which experimentally determined, intracellular fluxes are shown to be highly correlated with fluxes computed based on elementary modes and reaction entropies. The analysis suggests that the set of elementary modes can be interpreted analogous to a metabolic ensemble of quantum states of a macroscopic system.
Wlaschin
Aaron P
AP
240 Gortner Laboratory, Department of Chemical Engineering and Materials Science, and BioTechnology Institute, University of Minnesota, 1479 Gortner Avenue, St. Paul, MN 55455/55108, USA.
Trinh
Cong T
CT
Carlson
Ross
R
Srienc
Friedrich
F
D000693:Anaerobiosis / Q000502:physiology; D019149:Bioreactors / Q000382:microbiology*; D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D019277:Entropy; D004926:Escherichia coli / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000378:metabolism*; D006885:Hydroxybutyrates / Q000378:metabolism*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D011091:Polyesters / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology; D000094122:Polyhydroxybutyrates
D029968:Escherichia coli Proteins; D006885:Hydroxybutyrates; D011091:Polyesters; C003182:poly-beta-hydroxybutyrate; D000094122:Polyhydroxybutyrates