Bayesian Design for Whole Cell Synthetic Biology Models

ABSTRACT. We present a novel approach to the automated design of large scale biological systems. Our model connects multiple levels of cellular organisation through a hybrid continuous/discrete and deterministic/stochastic approach. At the core of our model, a hybrid dynamical system represents interactions between gene expression and DNA topology. Gene expression then influences the metabolic level of the cell by affecting the flux distribution in a genome-scale metabolic model of {\it Escherichia coli}. We implement a Bayesian optimisation strategy to select optimal parameters which maximally satisfy a temporal logic formula specifying the desired behaviour of the system. Finally, we show preliminary results of a study on the interaction of the master regulator FIS with supercoiling.