Search for: All records

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 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 Many microscopic images and simulations of cells give results in different kinds of formats, making it difficult for people lacking computational skills to visualize and interact with them. Minecraft—known for its three-dimensional, open-world, voxel-based environment—offers a unique solution by allowing the direct insertion of voxel-based cellular structures from light microscopy and simulations into its worlds without modification. This integration enables Minecraft players to explore the ultrastructure of cells in a highly immersive and interactive environment. Here, we demonstrate several workflows that can convert images and simulation results into Minecraft worlds. Using the workflows, students can easily import and interact with a variety of cellular content, including bacteria, yeast, and cancer cells. This approach not only opens new avenues for science education but also demonstrates the potential of combining scientific visualization with interactive gaming platforms for facilitating research and improving appreciation of cellular structure for a broad audience. 

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 

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 calculatein silicochromosome 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. 

The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell’s components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell’s spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.