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Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM.

Small RNAs (sRNAs) play a crucial role in the regulation of bacterial gene expression by silencing the translation of target mRNAs. SgrS is an sRNA that relieves glucose-phosphate stress, or “sugar shock” in E. coli. The power of single cell measurements is their ability to obtain population level statistics that illustrate cell-to-cell variation. Here, we utilize single molecule super-resolution microscopy in single E. coli cells coupled with stochastic modeling to analyze glucose-phosphate stress regulation by SgrS. We present a kinetic model that captures the combined effects of transcriptional regulation, gene replication and chaperone mediated RNA silencing in the SgrS regulatory network. This more complete kinetic description, simulated stochastically, recapitulates experimentally observed cellular heterogeneity and characterizes the binding of SgrS to the chaperone protein Hfq as a slow process that not only stabilizes SgrS but also may be critical in restructuring the sRNA to facilitate association with its target ptsG mRNA.

JCVI-syn3A is a minimal bacterial cell with a 543 kbp genome consisting of 493 genes. For this slow growing minimal cell with a 105 min doubling time, we recently established the essential metabolism including the transport of required nutrients from the environment, the gene map, and genome-wide proteomics. Of the 452 protein-coding genes, 143 are assigned to metabolism and 212 are assigned to genetic information processing. Using genome-wide proteomics and experimentally measured kinetic parameters from the literature we present here kinetic models for the genetic information processes of DNA replication, replication initiation, transcription, and translation which are solved stochastically and averaged over 1,000 replicates/cells. The model predicts the time required for replication initiation and DNA replication to be 8 and 50 min on average respectively and the number of proteins and ribosomal components to be approximately doubled in a cell cycle. The model of genetic information processing when combined with the essential metabolic and cell growth networks will provide a powerful platform for studying the fundamental principles of life.