In general, the environmental conditions microbes encounter changes rapidly and nutrient availability commonly limits growth. Growing batch cultures, for example, run out of nutrients eventually and growth stops. The allocation modeling framework can account such a dynamics by including metabolic rates which depend on the nutrient concentrations in the environment. In the simplest case, one nutrient source is considered (concentration ) with the metabolic rate depending on the concentration in a Michaelis–Menten manner with a maximal metabolic rate being reached only at high nutrient concentrations (Ai). The dynamics of precursors is given by a balance of synthesis, consumption, and dilution (Aii), replacing the corresponding equation of the simple model in (Figure 1Biv). The modeling of growth further requires the explicit modeling of nutrient concentrations. This dynamics depends on the specifics of the environment and, depending on the environment, can become very complex with multiple sources and sinks affecting the nutrient concentration. Here, we consider a typical batch culture scenario in which cells grow under well-mixed conditions. Nutrients are provided only initially and nutrient concentrations are falling because of consumption (Aiii). (B) Model parameters, dimensions, values, and relevant reference for including nutrient dynamics. (C–E) Resulting temporal variation of nutrient concentrations (C), biomass accumulation (D), and precursor concentration (E) when integrating the model equations and using a parameter set descriptive of E. coli growing in a glucose-minimal medium with a growth rate ≈1 hr-1 and a starting glucose concentration of 10 mM. As experimentally observed, initially abundant nutrients are consumed and biomass accumulates (exponential phase) until nutrients are exhausted and growth stops (saturation phase) (C, D). Importantly, precursor concentrations (E) quickly reach a constant plateau which lasts until nutrients become scarce ( and ). During this transient period (shaded regions), the synthesis of precursors matches the consumption by protein synthesis and dilution, meaning . Given a constant precursor concentration , the translation rate is also constant. As a consequence, the protein pool approaches a steady composition dictated by the allocation parameters (, and ). With precursor concentrations and protein composition remaining constant, the system is in a steady state and biomass accumulates exponentially over time, . This is the steady-state regime we focus on in the main text. Note that the steady state growth regime readily emerges when we consider dilution (see Appendix 1 - Precursor concentrations and the importance of dilution by cell growth). Model parameters are provided in Supplementary file 1. Biomass units are converted to optical density assuming at , there are 109 cells per ml and 109 amino acids per cell. An interactive version of these dynamics can be found on the paper website (cremerlab.github.io/flux_parity).