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

(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.