Stochastic Mechanisms Of Information Flow In Phosphate Economy Of Escherichia Coli

In previous work, we have presented a computational model and experimental results that quantify the dynamic mechanisms of autoregulation in E. coli in response to varying external phosphate levels. In a cycle of deterministic ODE simulations and experimental verification, our model predicts and explores phenotypes with various modifications at the genetic level that can optimise inorganic phosphate intake. Here, we extend our analysis with extensive stochastic simulations at a singlecell level so that noise due to small numbers of certain molecules, e.g., genetic material, can be better observed. For the simulations, we resort to a conservative extension of Gillespie's stochastic simulation algorithm that can be used to quantify the information flow in the biochemical system. Besides the common time series analysis, we present a dynamic visualisation of the time evolution of the model mechanisms in the form of a video, which is of independent interest. We argue that our stochastic analysis of information flow provides insights for designing more stable synthetic applications that are not affected by noise.