Resources allocation explains the differential roles of RBS and promoter strengths in cell mass distribution and optimal protein expression productivity

Design of synthetic genetic circuits without considering the impact of host-circuit interactions results in an inefficient design process and lengthy trial-and-error iterations to appropriately tune the expression levels. Microorganisms have evolved to reach an optimal use of cellular resources. This balance is perturbed by circuit-host interactions resulting from the interaction among the cell environment from which the cell takes substrates, its metabolism, and the needs of exogenous synthetic genetic circuit introduced in the cell host. The resulting competition for common shared cell resources introduces spurious dynamics leading to problems of malfunctioning of the synthetic circuit due to lack of enough cellular resources. Therefore, there is an increasing interest in development of methods for model-based design of synthetic gene circuits considering host-circuit interactions. Here we present a small-size model of gene expression dynamics in bacterial cells accounting for host-circuit interactions. For each gene, the model defines the cellular resources recruitment strength as the key functional coefficient that allows to explain the distribution of resources among the host and the genes of interest and the relationship between the usage of resources and cell growth. This functional coefficient explicitly takes into account the availability of resources and lab-accessible gene characteristics, such as promoter and ribosome binding site (RBS) strengths and capture their interplay with the availability of free cell resources. In spite of its simplicity, the model is able to explain the differential role of promoter and RBS strengths in the distribution of protein mass and the optimal protein expression productivity with remarkable fit to the experimental data from the literature for E. coli. This makes the model amenable for model-based circuit design purposes. Moreover, the model also allows to understand why endogenous ribosomal and non-ribosomal genes have evolved different strategies in the expression space.