Investigating transcriptional regulation of purine biosynthesis metabolic pathways with the Escherichia coli Whole-Cell Model

Escherichia coli is the most studied model bacterium with critical roles in advancing basic science and biotechnology. Decades of research have produced immense amounts of knowledge about E. coli, but this knowledge has yet to be unified into a quantitatively coherent scientific model with holistic, systems-level predictions and understanding. The E. coli Whole-Cell Modeling project aims to create the most detailed mechanistic, computational model of an entire E. coli cell to better understand, predict, and guide experimental discovery. The E. coli Whole-Cell Model (WCM) currently contains only 24 out of more than 300 transcription factors in E. coli. Furthermore, though detailed biochemical knowledge of transcription factor mechanisms and computational studies of transcription factor networks exist, a quantitative understanding of how many transcription factors function within their cellular context, e.g. metabolism, has yet to emerge. For these two purposes, this project aims to add more transcription factors to the E. coli WCM and evaluate their systems-level function. Gene regulation models are parameterized by analyzing gene expression within a large published microarray compendium across numerous environmental conditions and implemented in the WCM with more biochemically realistic assumptions than before. Surprisingly, this study found at least two categories of gene expression: genes that remain relatively constant and unregulated regardless of environment, and genes that roughly occupy only two values of expression in a switch-like manner. This study implements the switch-like gene regulation by the PurR transcription factor of most enzymes in the de novo purine biosynthesis pathway. In this and ongoing work, integrating this with a more kinetic model of metabolism could potentially help investigate how purine biosynthesis matches cellular demand through transcription at different growth rates, levels of purines in the media, and due to internal stochastic fluctuations. These findings offer a more nuanced, quantitative understanding of the role of certain transcription factors in E. coli. Furthermore, they improve the WCM, which aims to eventually become a “digital twin” of E. coli that guides experimental discovery and is useful for biotechnology.