JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

Highlights d 3D spatial resolution of a fully dynamical whole-cell kinetic model d Detailed single-reaction, single-cell accounting of timedependent ATP costs d Genome-wide mRNA half-lives emerge from lengthdependent kinetics and diffusion d Connections among metabolism, genetic information, and cell growth are revealed

Conditions and parameters affecting the range of bistability of the lac genetic switch in Escherichia coli are examined for a model which includes DNA looping interactions with the lac repressor and a lactose analogue. This stochastic gene-mRNA-protein model of the lac switch describes DNA looping using a third transcriptional state. We exploit the fast bursting dynamics of mRNA by combining a novel geometric burst extension with the finite state projection method. This limits the number of protein/mRNA states, allowing for an accelerated search of the model's parameter space. We evaluate how the addition of the third state changes the bistability properties of the model and find a critical region of parameter space where the phenotypic switching occurs in a range seen in single molecule fluorescence studies. Stochastic simulations show induction in the looping model is preceded by a rare complete dissociation of the loop followed by an immediate burst of mRNA rather than a slower build up of mRNA as in the two-state model. The overall effect of the looped state is to allow for faster switching times while at the same time further differentiating the uninduced and induced phenotypes. Furthermore, the kinetic parameters are consistent with free energies derived from thermodynamic studies suggesting that this minimal model of DNA looping could have a broader range of application.

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Central to all life is the assembly of the ribosome: a coordinated process involving the hierarchical association of ribosomal proteins to the RNAs forming the small and large ribosomal subunits. The process is further complicated by effects arising from the intracellular heterogeneous environment and the location of ribosomal operons within the cell. We provide a simplified model of ribosome biogenesis in slow-growing Escherichia coli. Kinetic models of in vitro small-subunit reconstitution at the level of individual protein/ribosomal RNA interactions are developed for two temperature regimes. The model at low temperatures predicts the existence of a novel 5'→3'→central assembly pathway, which we investigate further using molecular dynamics. The high-temperature assembly network is incorporated into a model of in vivo ribosome biogenesis in slow-growing E. coli. The model, described in terms of reaction-diffusion master equations, contains 1336 reactions and 251 species that dynamically couple transcription and translation to ribosome assembly. We use the Lattice Microbes software package to simulate the stochastic production of mRNA, proteins, and ribosome intermediates over a full cell cycle of 120 min. The whole-cell model captures the correct growth rate of ribosomes, predicts the localization of early assembly intermediates to the nucleoid region, and reproduces the known assembly timescales for the small subunit with no modifications made to the embedded in vitro assembly network.

Viral DNA packaging motors are among the most powerful molecular motors known. A variety of structural, biochemical, and singlemolecule biophysical approaches have been used to understand their mechanochemistry. However, packaging initiation has been difficult to analyze because of its transient and highly dynamic nature. Here, we developed a single-molecule fluorescence assay that allowed visualization of packaging initiation and reinitiation in real time and quantification of motor assembly and initiation kinetics. We observed that a single bacteriophage T4 packaging machine can package multiple DNA molecules in bursts of activity separated by long pauses, suggesting that it switches between active and quiescent states. Multiple initiation pathways were discovered including, unexpectedly, direct DNA binding to the capsid portal followed by recruitment of motor subunits. Rapid succession of ATP hydrolysis was essential for efficient initiation. These observations have implications for the evolution of icosahedral viruses and regulation of virus assembly.virus packaging | single-molecule fluorescence imaging | molecular motors A s part of a virus life cycle, genetic information needs to be incorporated into the newly produced virus particles. Tailed bacteriophages, which probably form the largest biomass of the planet (1), and many eukaryotic viruses such as herpes viruses use powerful ATPase motors to achieve this (2). These motors generate forces as high as 80-100 pN and translocate DNA into a preformed prohead until a DNA condensate of near crystalline density fills the interior (3).The viral packaging motors share a common architecture with the ASCE (additional strand, conserved E) superfamily of multimeric ring ATPases that perform diverse functions such as chromosome segregation (helicases), protein remodeling (chaperones and proteasomes), and cargo transport (dyneins) (4). Although much has been learned about the mechanochemistry of these motors, little is known about how a functional motor is assembled and its activity is initiated. The packaging motors have the difficult task of precisely inserting the end of a viral genome into the capsid at the time of initiation.In a general virus assembly pathway shared by dsDNA viruses, assembly starts at a unique fivefold vertex of the prohead called the portal vertex, which is formed from 12 molecules of the portal protein (5). A protein shell assembles around a protein scaffold and later becomes an empty prohead after the scaffold leaves, or is degraded (6). In most dsDNA bacteriophages as well as herpes viruses a complex of two proteins, known as small and large "terminase" proteins, recognize a specific sequence of DNA in the concatemeric viral genome (e.g., cos site in phage λ and pac site in phage P22) and make a cut to create a dsDNA end (7,8). The small terminase is responsible for binding to the cos or pac site, whereas the large terminase makes the cut. However, phage phi29 and adenoviruses do not require DNA cutting because the genome is a unit-length m...

Ribosomes—the primary macromolecular machines responsible for translating the genetic code into proteins—are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume1 to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells’ lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 minute doubling time) E. coli cells, replication was initiated 42 minutes into the cell cycle and completed after an additional 42 minutes. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of non-ribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome.

Purpose Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. Methods We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. Results A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 – 95.5) and 89.4% (95% CI 81.3 – 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 – 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. Conclusion A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.