Deciphering regulatory architectures of microbial genomes from synthetic expression patterns

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various parameters on the experimental data. These parameters include experimental parameters such as mutation rate and library size, as well as biological parameters such as binding site copy number. To that end, we created tens of thousands of synthetic single-cell gene expression outputs using thermodynamic models of gene expression. This makes it possible to imitate the summary statistics used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Our simulations reveal important effects of the parameters on the structure of MPRA data and provide useful insights for optimizing MPRA experimental designs and data interpretation.