A key turning point in the evolution of life was the transition from single-cell to multicellular organisms and the optimization of fitness via division of labour and specialization. Similarly, microorganisms have evolved equivalent strategies by forming communities or consortia. Division of labour in isogenic microbial populations is often implemented by mechanisms that create or act upon population heterogeneity to diversify functionality. Rational design in synthetic biology, on the other hand, is focused on the engineering of gene circuits with deterministically predictable functionality within single cells. While synthetic biology has certainly come a long way, predictable functionality of circuits in growing microbial populations still remains elusive or limited to tightly constrained operating conditions. We will develop novel mathematical methods to characterize and control the dynamics of synthetic gene circuits within growing microbial populations. We will develop a modelling framework and novel computational methods that take both stochasticity of single-cell processes and consequences of heterogeneity for population dynamics into account. On the mathematical side, this necessitates coupling single-cell stochastic processes to state dependent population processes such as growth or selection. We will develop methods for parameter inference, experimental design and control for such models. This will enable the construction of models that can be used to design synthetic circuits that function as specified within growing populations and that can be deployed to regulate single-cell processes such that desirable dynamics emerge at the scale of populations and consortia. We will apply the methodology for bioproduction problems in which proteins that are hard to fold need to be produced. Overproducing such proteins impairs cellular growth, which creates couplings between single-cell and population processes and raises the need to feedback control production.
