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. Author manuscript; available in PMC: 2013 Nov 14.
Published in final edited form as: Nat Commun. 2012 Jul 3;3:10.1038/ncomms1928. doi: 10.1038/ncomms1928

Figure 2. Comparison of M- and ME-Models objective functions and assumptions.

Figure 2

(a) M-Models simulate constant cellular composition (biomass) as a function of specific growth rate (μ), whereas ME-Models simulate constant structural composition with variable composition of proteins and transcripts. (b) Linear programming simulations with M-Models are designed to identify the maximum μ that is subject to experimentally measured substrate uptake rates. only biomass yields are predicted as μ enters indirectly as an input through the supplied substrate uptake rate (see the measurement column for M-Models). Importantly, the substrate uptake rate is derived by normalizing to biomass production. Linear programming simulations with ME-Models aim to identify the minimum ribosome production rate required to support an experimentally determined μ. μ enters into the coupling constraints and so it must be supplied (or sampled) as the problem would otherwise be a nonlinear Program (nLP). As all M-Models reactions are contained within the ME-Models, ME-Models can simulate all M-Models objectives in addition to the broad range of objectives associated with macromolecular expression.