Biology subjects

Thornburg, Z. R.

Publications and source records attributed to Thornburg, Z. R..

5 recordsLinked to original sources

bioRxiv2025

COTree: A Statistical Framework for Deciphering Cell-Resolved Multi-Omics Trajectories

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.

bioinformatics↗

bioRxiv2025

Unraveling the Transcriptional Landscape within a Minimized Bacterium via Comparative Analysis

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-reduced Mycoplasmas, 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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/681674v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@78a9e0org.highwire.dtl.DTLVardef@1d8c9fcorg.highwire.dtl.DTLVardef@1b4e129org.highwire.dtl.DTLVardef@2a8c3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Spatial Dynamics in the Yeast Galactose Switch Revealed Through RDME-ODE Hybrid 4D Simulations

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 of GAL2 mRNA 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.

systems biology↗

bioRxiv2025

Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell

Macromolecular complexes in the genetically minimized bacterium, JCVI-syn3A, support gene expression (RNA polymerase, ribosome, degradosome), metabolism (ABC transporters, ATP synthase) and chromosome dynamics. In this work, we further incorporate the assembly of 21 unique macromolecular complexes into the existing whole-cell kinetic model of Syn3A. The synthesis and translocation of protein subunits in membrane complexes occur through distinct pathways. A range of 2D association rates for membrane complexes were considered to guarantee a high yield of assembly given the existing time scales of gene expression. By alleviating the undesired kinetically trapped intermediates in ATP synthase assembly, the efficiency was improved. The assembly of RNA polymerase, ribosome, and degradosome influence the speed and efficiency of protein synthesis. Collectively, this model predicted time-dependent cellular behaviors consistent with experiments. A machine learning analysis of the time-dependent metabolomics and metabolic fluxes highlighted the effect of introducing complex assembly into our whole-cell model.

biophysics↗

bioRxiv2025

Bringing the Genetically Minimal Cell to Life on a Computer in 4D

We present a whole-cell spatial and kinetic model for the 100 minute cell cycle of the genetically minimal bacterium, JCVI-syn3A. This is the first simulation of a complete cell cycle in 4D including all genetic information processes, metabolic networks, growth, and cell division. Integrating hybrid computational methods, dynamics of the morphological transformations were achieved. Growth is driven by synthesis of lipids and membrane proteins and constrained by new fluorescence imaging data. Chromosome replication and segregation is controlled by essential SMC and topoisomerase proteins in Brownian dynamics simulations with replication rates responding to dNTP pools from metabolism. The model captures the origin to terminus ratio measured in our DNA sequencing and recovers other experimental measurements like doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. Not only do we predict average behavior for partitioning to daughter cells, we predict the heterogeneity among them.

biophysics↗