Fig. 2: Simulating an open-loop process in repeated batch conditions.

a A schematic showing the function of the process. m_A is spawned and translated to create output p_A using host ribosomes. This process impacts host growth. The total output P comes from the output p_A across an entire population of engineered cells. b A visual depiction of the mutation scheme for this process. Coloured squares represent distinct ‘mutation states’ with different levels of function. Numbers in the squares show the percentage function of a given state relative to the designed level, implemented through differences in the maximal transcription rate ω_A: 100 represents a state functioning as designed, while 0 represents a completely non-functional state where no transcripts m_A are produced. Arrows signify possible transitions between mutation states, with lighter arrows representing mutations which occur less often. c‒f Time-series outputs using an open-loop process with maximal transcription rate \({\omega }_{A}=5\,\,{\mbox{mc}}\,\,{\min }^{-1}\). c Total population-wide circuit output P, plotted both in full (grey) and at the end of each simulation day (green). An ideal system would match an open-loop system in the absence of mutation, maintaining function indefinitely (blue). d Population size N, distributed according to mutation state. Dark green represents a fully-functional (100%) strain. Light green represents a non-functional (0%) strain. e Output per cell p_A according to mutation state over the first day. Dotted lines show maximum outputs. f Growth rate λ according to mutation state over the first day. Dotted lines show maximum growth rates. g For a wide range of processes (varying ω_A between 0.1 and 1000 mc min⁻¹), τ_±10 (black) and τ₅₀ (grey) against initial output P₀. Simulation results are provided as a Source Data file.