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Abstract Minimal bacterial cells such as JCVI‐Syn3A provide a powerful system for uncovering the essential mechanisms of chromosome organization and segregation. Lacking canonical systems such as Min and ParABS, JCVI‐Syn3A relies primarily on structural maintenance of chromosomes (SMC) protein complexes for partitioning. Here, we investigate a four‐dimensional (4D; three spatial dimensions plus time) polymer‐based model of the JCVI‐Syn3A chromosome (543 kbp) that captures replication and partitioning dynamics across the full cell cycle. Our simulations reproduce chromosome segregation mediated by SMC‐driven loop extrusion and reveal how segregation depends on the number of SMC complexes, their translocation speed, and their dwell time on DNA. A systematic parameter scan shows that segregation is strongly predicted by the effective loop coverage, which represents the expected fraction of the chromosome extruded into loops. We generate contact maps for stationary‐phase cells to directly connect our simulations with 3C experiments, and for replicating chromosomes throughout the cell cycle to provide new, testable predictions for synchronized cell populations. Our results suggest that SMC protein complexes and topoisomerases can drive chromosome segregation in minimal cells without additional partitioning systems provided loop extrusion achieves sufficient genomic coverage. 

ABSTRACT Dormant microbial spores provide one of the clearest and most extreme examples of how cells can pause life for extended periods and then reliably restart it. Although bacterial and fungal spores are often grouped under “dormancy,” the physical strategies by which they suspend and resume life differ fundamentally. Here, I compare two canonical systems—Bacillus subtilisendospores andSaccharomyces cerevisiaeascospores—using a dynamical-systems framework from a physicist’s perspective. I propose that dormancy is not simply “low metabolism,” but a dynamical reconfiguration that decouples local molecular clocks from a global biological clock while preserving an intrinsic capacity to resume sustained nonequilibrium dynamics, which I refer to as nonequilibrium capacity. Specifically, inB. subtilisspores, “dry dormancy” is enforced by immobilization: dehydration and material constraints suppress appreciable molecular diffusion and reaction fluxes, arresting global biological time by suppressing local molecular clocks. InS. cerevisiaespores, “wet dormancy” appears to be achieved by throttling: spores remain hydrated, retain molecular mobility, and support some slow irreversible processes such as gene expression, yet global biological time remains arrested because, as I propose, local activity fails to propagate into sustained organism-level progression (e.g., growth and division). Together, these comparisons place dry and wet dormancy as distinct regions of a physical design space defined by hydration, molecular mobility, energetic flux, and cross-scale coupling between local activity and global progression, and motivate quantitative models of dormancy and revival dynamics. 

SUMMARY Cells employ diverse strategies to rapidly adapt to sudden environmental changes. In yeast, cytoprotective solidification in response to starvation and energy depletion (ED) has been reported and associated with extensive mesoscale macromolecular assembly. Yet, the structural and molecular basis underlying such whole-cell level liquid-to-solid phase transitions remain unknown. Here, we use cryo-electron tomography to characterize the subcellular organization of intact yeast cells exposed to ED and other stressors, and to untangle the effects of molecular crowding versus cytoplasmic acidification previously suggested to underpin solidification. We visualize self-assembly of macromolecules and complexes into ordered assemblies and condensates under ED, and quantify ribosome and polysomes concentrations to probe changes in cytoplasmic crowding. Combined with live-cell microscopy, we pinpoint supramolecular assembly induced by acidification, rather than a uniform increase in intracellular crowding, as the structural basis of cytoplasmic solidification that supports yeast cells’ adaptation in response to environmental stresses. 

SUMMARY The choice between cell death (lysis) and viral dormancy (lysogeny) following bacteriophage infection serves as a founding paradigm for the emergence of cellular heterogeneity in a genetically uniform population. The determination of host fate arises through the stochastic transcription from multiple viral genomes present within each cell, but this activity remains hidden from empirical interrogation, which typically stops at the whole-cell level. Here we use parallel sequential fluorescence in situ hybridization (par-seqFISH), followed by spatial clustering of phage-encoded transcripts within each cell, to profile the transcriptional activity of individual phages during synchronized infection ofEscherichia coli(E. coli) by bacteriophage lambda. At the whole-cell level, transcription kinetics capture the developmental choice between lysis and lysogeny, and further demonstrate that viral replication is required for the emergence of diverging fate decisions. Zooming in to the single-phage level illuminates an individuality of viral activity during infection. We find that, while cells pursuing lysogeny display consensus activity of all in-habiting phages, lytic cells may contain phages that exhibit lysogenic activity. These findings support an earlier suggestion that consensus among coinfecting phages is required for cell dormancy. More broadly, our results highlight the need to identify how whole-cell behavior emerges from the activity of physically distinct copies of the same genetic circuit. 

Abstract Large-scale clinical genome sequencing yields vast numbers of variants of unknown significance (VUSs). The high frequency of VUSs and the paucity of platforms to characterize their functional impact pose significant challenges for clinical decision making. Here, we present an integrated end-to-end platform, REVi-SCOPE (Rapid evaluation of variants in single cells by optogenetics and prime editing), for characterization of the impact of VUSs on cardiac physiology. Our strategy consists of (1) introduction of variants directly into wild-type (WT) human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) via prime editing; (2) optogenetic assessment of calcium and membrane voltage dynamics in single hiPSC-CMs within the pool of edited and unedited cells; and (3) in situ single-cell genotyping of the phenotyped hiPSC-CMs with single-allele resolution. By optimizing and integrating each of these steps, we created a platform that enables VUS characterization in 10 days. We validated the REVi-SCOPE’s capabilities by analyzing the properties of established arrhythmogenic variants. We then used REVi-SCOPE to reveal the functional impact of a VUS,TRPM4^A320V, identified in a child with a conduction block. Together, our results show that REVi-SCOPE enables functional characterization of VUSs linked to cardiac arrhythmias with unprecedented throughput. 

Abstract Recent advances in whole-cell modeling enable the computational tracking of the temporal evolution of thousands of molecular species across genomic, transcriptomic, proteomic, and metabolomic layers. These models provide a complementary perspective for studying cellular dynamics, offering continuous, system-wide observations that are difficult to obtain from experimental technologies, which are often destructive and yield only static measurements from limited modalities. While whole-cell models generate multi-omic simulation trajectories with high temporal resolution, analyzing and interpreting such complex data remains a major challenge that limits their potential to elucidate cellular dynamics. To address this challenge, we propose COTree, a statistical framework that learns integrated multi-omic representations and constructs a trajectory principal tree to summarize cellular progression patterns. COTree enables a broad range of downstream analyses, including cell classification, fate prediction, developmental time detection, and driver species identification, that provide new insights into how cells develop and differentiate. To demonstrate its practical utility, we apply COTree to a multi-omic trajectory dataset generated from the whole-cell model of JCVI-Syn3A, revealing cell types, characterizing long-term cellular dynamics, and identifying key driver species associated with cell death and replication. 

Abstract Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacteriumProchlorococcus marinusMED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET images as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such asP. marinusMED4. Significance StatementThis work advances 4D whole-cell modeling by presenting the first spatiotemporal simulation of a photosynthetic autotroph using the Lattice Microbes platform. UsingProchlorococcus marinusMED4, we show that subcellular spatial organization of organelles, diffusion constraints, and redox regulation collectively shape central carbon metabolism across orders of magnitude in space and time. Through a perturbation-based strategy that generates multi-omics data sets over time, we construct and validate a spatially and temporally resolved model of MED4, constrained by high-resolution (10 nm) cryo-electron tomography. Our results highlight the importance of localized biochemical reactions and redox-dependent post-translational modification of enzymes in regulating carbon fixation in a noisy environment under light disturbance. This study establishes a spatiotemporal, whole-cell physiology modeling framework as a transformative tool for uncovering multiscale regulatory responses to environmental gradients. 

Abstract Eukaryotic cells are spatially organized into functionally-distinct compartments. This three-dimensional(3D) organization generates intracellular heterogeneities that can modulate regulatory dynamics. Despite this knowledge of subcellular organization, most quantitative gene-regulation models still assume a well-mixed environment in which molecules can react regardless of their spatial positions. Here, we use the well-established galactose switch in budding yeast (Saccharomyces cerevisiae) to develop spatially-resolved models that integrate experimentally-derived intracellular architectures, including chromosome organization, the endoplasmic reticulum (ER) and spatially distinct ribosome populations. We implement a hybrid stochastic–deterministic framework in which gene expression is modeled using a reaction–diffusion master equation that enforces locality (i.e., reactions occur only when molecules are in physical proximity), while metabolic and transport processes are captured by ordinary differential equations. Guided by electron microscopy and biochemical constraints, we quantify how accounting for intracellular spatial organization alters regulatory predictions in the galactose switch. We show that chromosome geometry has little effect on Gal2p output, whereas ER-associated translation reduces Gal2p delivery to the plasma membrane; the largest decrease of Gal2p abundance occurs when translation ofGAL2mRNA is restricted to a population of ribosomes physically bound to the ER. Together, these results demonstrate that more realistic 3D cellular architectures and local reaction rules can qualitatively change regulatory predictions, motivating integration of intracellular organization in future whole-cell models. Author SummaryEukaryotic cells are highly organized spaces with distinct subcellular compartments and heterogeneous distributions of molecules which influence how cells function. Yet, most computational models of gene regulation assume a spatially homogeneous intracellular environment. To quantify how intracellular architecture influences regulatory predictions, we developed spatially resolved models of the galactose switch in budding yeast, a well-characterized gene regulatory system controlling the response to extracellular galactose. We compared conventional well-stirred simulations with models that explicitly incorporate experimentally informed intracellular organization, including chromosome positioning, endoplasmic reticulum geometry, and functionally distinct ribosome populations. Incorporating these spatial features substantially altered the dynamics of predicted gene activity, protein production, and intracellular sugar levels. Our results demonstrate that the three-dimensional organization of eukaryotic cells can significantly change regulatory outcomes of computational models, underscoring the need for integrating realistic spatial architectures in future models of eukaryotic gene regulation. 

Stochastic nature of gene expression leads to the complex formation of the bacterial transcriptome and proteome. In contrast to typical transcriptome studies, we employ a near wild-type, Syn1.0, of the naturally genome-reducedMycoplasmas, and the dramatically further genome-reduced JCVI-syn3A thus avoiding additional contributions from many non-essential cellular functions. To aid in profiling the transcriptional landscape within these bacteria, we present a bioinformatic analysis of the genetic sequence motifs implicated in modulating the stochastic gene expression events, coupled with genome-wide short-read (Illumina) and long-read (Oxford Nanopore Technologies and Pacfic Biosciences) RNA sequencing. The bioinformatic analysis coupled with information from structural studies assigns strengths of the Shine-Dalgarno signatures and identifies both transcription initiation and termination sites, leading to predictions of RNA isoforms in Syn1.0 (and related organisms). The long-read and short-read RNA sequencing characterized the predicted transcriptional activity, and the long-read methods provide direct insight into the RNA isoform complexity within Syn1.0. Comparison of the RNA sequencing results with that of the bioinformatic analysis highlights the inability of bioinformatics alone to capture the results of bacterial transcription without including effects of RNA degradation. This study emphasizes the need for comparative analysis and potential dangers of genome reduction, exemplified through the discovery of altered gene expression patterns of JCVI-syn1.0 and JCVI-syn3A, achieved via the union of our transcriptome study with their proteomics data. Analysis of the transcriptomics data sets through a Jupyter notebook allows any genomic region to be easily examined. Table of Content Image 

SUMMARY Proper function in a bacterial cell relies on intrinsic cell size regulation. The molecular mechanisms underlying how bacteria maintain their cell size remain unclear. The conserved regulator DnaA, the initiator of chromosome replication, is associated to size regulation by controlling the number of origins of replication (oriC) per cell. In this study, we identify and characterize a new mechanism in which DnaA modulates cell size independently oforiC-copy number. By altering the levels of DnaA without impacting chromosome replication, we demonstrate that DnaA’s activity as a transcription factor can slow down cell elongation rate resulting in cells that are ∼20% smaller. We identify the peptidoglycan biosynthetic enzyme MurD as a key player of cell size regulation inCaulobacter crescentusand in the evolutionarily distant bacteriumEscherichia coli. Collectively, our findings provide mechanistic insights to the complex regulation of cell size in bacteria.