While viruses affect the flow of elements and energy at a planet-wide scale through lysis, gene transfer, and metabolic reprogramming, they are yet to be included in planetary-scale models of ecosystem function and nutrient cycling. Here, we review recent advances incorporating viruses into ocean models and ask: what barriers remain? To address these challenges, we argue for a new generation of ocean models that are fully representative of the multifaceted influences of viruses across scales of organization. We describe ways to achieve this by integration of existing models built across scales, from molecules to ecosystems and the Earth System. To accelerate these advances, we emphasize the need for systematic, intercalibrated datasets for diverse experimental virus-host systems, wider application of new technologies to monitor in situ viral infections, and new software to integrate models across scales. Resolution of viruses within multi-scale models will open the door to assessing current biological uncertainties related to the impact of viral infection on nutrient retention in the surface ocean, carbon sequestration to depth, and the sensitivity of these processes to climate change.

50 years ago, cell biology was a nascent field. Today, it is a vast discipline whose principles and tools are also applied to other disciplines; vice versa, cell biologists are inspired by other fields. So, the question begs: what is cell biology? The answers are as diverse as the people who define it.

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli’s 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper’s transparent peer review process is included in the supplemental information.

50 years ago, cell biology was a nascent field. Today, it is a vast discipline whose principles and tools are also applied to other disciplines; vice versa, cell biologists are inspired by other fields. So, the question begs: what is cell biology? The answers are as diverse as the people who define it.

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting path-ogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the tran-scription factor hypoxia-inducible factor 1 alpha (HIF-1 alpha). Single-cell dynamics have been shown to be an important determi-nant of cell behavior; however, despite the importance of HIF-1 alpha, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we opti-mized a HIF-1 alpha fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhi-bition, a marker of metabolic change, via HIF-1 alpha activity. We then applied a physiological stimulus known to trigger meta-bolic change, interferon-gamma, and observed heterogeneous, oscil-latory HIF-1 alpha responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1 alpha-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1 alpha activation. Specifically, we found cells with high HIF-1 alpha activation are able to meaning-fully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1 alpha activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1 alpha in single cells and reveals previously unknown principles of HIF-1 alpha activation.

Summary: Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.

Data from simulations used to generate the figures in the paper Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response. To reproduce analyses, extract colony_data.zip in the data folder after cloning the vivarium-ecoli repository. The extracted folder contains the following items: sim_dfs: a folder containing the CSV files that represent a subset of the raw simulation data used for downstream analyses. glc_10000_fluxome.csv: Each row represents a reaction in central carbon metabolism (in same order as listed in validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux (mmol/L/hr). Provided as input to ecoli/analysis/centralCarbonMetabolism.py script to reproduce fluxome validation plot. glc_10000_proteome_avgs.csv: Each row represents a protein monomer (in same order as sim_data.translation.monomer_data["id"] where sim_data is reconstruction/sim_data/kb/validationData.cPickle). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to ecoli/analysis/proteinCountsValidation.py script to reproduce proteome validation plot. glc_10000_expressome.csv: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to ecoli/analysis/antibiotics_colony/subgen_gene_plots/count_subgen.py script to calculate number of sub-generational genes among all genes and antibiotic response genes. glc_10000_total_mrna.json: Mapping of agent IDs for all cells in a baseline glucose simulation (seed 10000) to their average total mRNA count. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. 2C,D. jenner_2013.csv: Data extracted from Fig. 2C of 10.1073/pnas.1216691110. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. S6A. olson_2006.csv: Data extracted from Fig. 2D of 10.1128/AAC.01499-05. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. S6A. lysis_ratios.csv: Data extracted from Fig. 2 of 10.1099/00221287-31-3-339. Used by ecoli/analysis/antibiotics_colony/plot.py to generate Fig. 4N.

Hypoxia-induced upregulation of HIF1α triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1α closely interacts with PPARγ, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results regarding how this interaction controls the excessive lipid accumulation that drives adipocyte dysfunction. To directly address these conflicts, we established a differentiation system that recapitulated prior seemingly opposing observations made across different experimental settings. Using single-cell imaging and coarse-grained mathematical modeling, we show how HIF1α can both promote and repress lipid accumulation during adipogenesis. Our model predicted and our experiments confirmed that the opposing roles of HIF1α are isolated from each other by the positive-feedback-mediated upregulation of PPARγ that drives adipocyte differentiation. Finally, we identify three factors: strength of the differentiation cue, timing of hypoxic perturbation, and strength of HIF1α expression changes that, when considered together, provide an explanation for many of the previous conflicting reports.

Abstract In Escherichia coli, inconsistencies between in vitro tRNA aminoacylation measurements and in vivo protein synthesis demands were postulated almost 40 years ago, but have proven difficult to confirm. Whole-cell modeling can test whether a cell behaves in a physiologically correct manner when parameterized with in vitro measurements by providing a holistic representation of cellular processes in vivo. Here, a mechanistic model of tRNA aminoacylation, codon-based polypeptide elongation, and N-terminal methionine cleavage was incorporated into a developing whole-cell model of E. coli. Subsequent analysis confirmed the insufficiency of aminoacyl-tRNA synthetase kinetic measurements for cellular proteome maintenance, and estimated aminoacyl-tRNA synthetase kcats that were on average 7.6-fold higher. Simulating cell growth with perturbed kcats demonstrated the global impact of these in vitro measurements on cellular phenotypes. For example, an insufficient kcat for HisRS caused protein synthesis to be less robust to the natural variability in aminoacyl-tRNA synthetase expression in single cells. More surprisingly, insufficient ArgRS activity led to catastrophic impacts on arginine biosynthesis due to underexpressed N-acetylglutamate synthase, where translation depends on repeated CGG codons. Overall, the expanded E. coli model deepens understanding of how translation operates in an in vivo context.

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

MOTIVATION:This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists. RESULTS:Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales. AVAILABILITY AND IMPLEMENTATION:The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

Hypoxia-induced upregulation of HIF1α triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1α closely interacts with PPARγ, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results how this co-regulation controls the excessive lipid accumulation that drives adipocyte dysfunction. Using single-cell imaging and modeling, we find that, surprisingly, HIF1α both promotes and represses lipid accumulation during adipogenesis. We show that the opposing roles of HIF1α are isolated from each other and depend on when HIF1α increases relative to the positive-feedback mediated upregulation of PPARγ that drives adipocyte differentiation. A theoretical model incorporating our findings resolves conflicting prior results and suggests that three network nodes before and after the isolation step have to be synergistically targeted in therapeutic strategies to revert hypoxia-mediated adipose tissue dysfunction in obesity. Teaser A systems biology approach detangles the effect of hypoxic and adipogenic regulators on lipid accumulation in adipocytes.

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Pooled genetic libraries have improved screening throughput for mapping genotypes to phenotypes. However, selectable phenotypes are limited, restricting screening to outcomes with a low spatiotemporal resolution. Here, we integrated live-cell imaging with pooled library-based screening. To enable intracellular multiplexing, we developed a method called EPICode that uses a combination of short epitopes, which can also appear in various subcellular locations. EPICode thus enables the use of live-cell microscopy to characterize a phenotype of interest over time, including after sequential stimulatory/inhibitory manipulations, and directly connects behavior to the cellular genotype. To test EPICode’s capacity against an important milestone—engineering and optimizing dynamic, live-cell reporters—we developed a live-cell PKA kinase translocation reporter with improved sensitivity and specificity. The use of epitopes as fluorescent barcodes introduces a scalable strategy for high-throughput screening broadly applicable to protein engineering and drug discovery settings where image-based phenotyping is desired.

Growth and environmental responses are essential for living organisms to survive and adapt to constantly changing environments. In order to simulate new conditions and capture dynamic responses to environmental shifts in a developing whole-cell model of E. coli, we incorporated additional regulation, including dynamics of the global regulator guanosine tetraphosphate (ppGpp), along with dynamics of amino acid biosynthesis and translation. With the model, we show that under perturbed ppGpp conditions, small molecule feedback inhibition pathways, in addition to regulation of expression, play a role in ppGpp regulation of growth. We also found that simulations with dysregulated amino acid synthesis pathways provide average amino acid concentration predictions that are comparable to experimental results but on the single-cell level, concentrations unexpectedly show regular fluctuations. Additionally, during both an upshift and downshift in nutrient availability, the simulated cell responds similarly with a transient increase in the mRNA:rRNA ratio. This additional simulation functionality should support a variety of new applications and expansions of the E. coli Whole-Cell Modeling Project.