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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.

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.

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.TeaserA systems biology approach detangles the effect of hypoxic and adipogenic regulators on lipid accumulation in adipocytes.

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.

The Escherichia coli whole-cell modeling project seeks to create the most detailed computational model of an E. coli cell in order to better understand and predict the behavior of this model organism. Details about the approach, framework, and current version of the model are discussed. Currently, the model includes the functions of 43% of characterized genes, with ongoing efforts to include additional data and mechanisms. As additional information is incorporated in the model, its utility and predictive power will continue to increase, which means that discovery efforts can be accelerated by community involvement in the generation and inclusion of data. This project will be an invaluable resource to the E. coli community that could be used to verify expected physiological behavior, to predict new outcomes and testable hypotheses for more efficient experimental design iterations, and to evaluate heterogeneous data sets in the context of each other through deep curation.

Summary Bacterial and viral pathogens affect the homeostasis of their hosts in complex and oftentimes unintuitive ways. Mechanistic insights into these host–pathogen interactions may one day allow us to discover more effective antimicrobials and antiviral therapies. The mechanisms that affect overall infection outcomes in the tissue and organ scales are often dictated by behaviors at the cellular and subcellular scales, such as cell metabolism, reproduction, aggregation, and survival. Computational modeling allows us to determine these large-scale outcomes by modeling smaller-scale phenomena, and this field of “multiscale computational modeling” has recently seen tremendous growth and innovation. In this article, we review recent studies of multiscale computational modeling of infection and their impact on the mechanistic understanding of host–pathogen interactions toward developing better therapies and multiscale modeling frameworks.

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.

Bacterial behavior is the outcome of both molecular mechanisms within each cell and interactions between cells in the context of their environment. Whereas whole-cell models simulate a single cell’s behavior using molecular mechanisms, agent-based models simulate many agents independently acting and interacting to generate complex collective phenomena. To synthesize agent-based and whole-cell modeling, we used a novel model integration software, called Vivarium, to construct an agent-based model of E. coli colonies where each agent is represented by a current source code snapshot from the E. coli Whole-Cell Modeling Project and interacts with other cells in a shared spatial environment. The result is the first “whole-colony” computational model that mechanistically links expression of individual proteins to a population-level phenotype. Simulated colonies exhibit heterogeneous effects on their environments, heterogeneous gene expression, and media-dependent growth. Extending the cellular model with mechanisms of antibiotic susceptibility and resistance, our model also suggested that variation in the expression level of the betalactamase AmpC, and not of the multi-drug efflux pump AcrAB-TolC, was the key mechanistic driver of survival in the presence of nitrocefin. We see this as a significant step forward in the creation of more comprehensive multi-scale models, and it broadens the range of phenomena that can be modeled in mechanistic terms.Author summaryThis work combines several models of molecular and physical processes that impact the physiology and behavior of the common microbe Escherichia coli into a multiscale model. Colonies comprised of multiple individual cells are simulated as they grow and divide—each with complex internal mechanisms, and with physical interactions and molecular diffusion in their environments. The integrative modeling methodology supports the addition of new submodels. The flexibility of this methodology is demonstrated by adding models of antibiotic resistance and simulating the colony’s response to antibiotic treatment.

Bacteriophages (phages) are the most abundant biological entity in the human body, but until recently the role that phages play in human health was not well characterized. Although phages do not cause infections in human cells, phages can alter the severity of bacterial infections by the dissemination of virulence factors amongst bacterial hosts. Recent studies, made possible with advances in genome engineering and microscopy, have uncovered a novel role for phages in the human body – the ability to modulate the physiology of the mammalian cells that can harbor intracellular bacteria. In this review, we synthesize key results on how phages traverse through mammalian cells – including uptake, distribution, and interaction with intracellular receptors – highlighting how these steps in turn influence host cell killing of bacteria. We discuss the implications of the growing field of phage–mammalian cell interactions for phage therapy.

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.

BackgroundImmune cell therapies continue to have success in treatment of cancers yet face challenges of complexity, cost, toxicity, and low solid-tumor efficacy. Much work has focused on the phenotype characterization and control of ex vivo expanded cells; however, little is known about its relationship to changes in the tumor microenvironment in vivo. Thus, we imaged tumors treated with different phenotype tumor-specific CD8+ T cells with CODEX multiplexed imaging1–4 that is able to visualize 42 antibodies at the same tissue in the tissue (figure 1A). To further probe this data in a systems immunology approach we created a multiscale agent-based model including critical circuits from the T cell-tumor microenvironment interactions (figure 1B).MethodsWe initialized our agent-based models various percentages of either PD1+, PD1-, PDL1+, or PDL1- phenotypes and ran simulations for 72 hours. We also treated PMEL CD8+ T cells with or without 2 hydroxycitrate as a metabolic inhibitor during activation to achieve different input phenotypes of CD8+ T cells for therapeutic adoptive transfer on day 10 following B16-F10 tumors had been established. We performed neighborhood analysis on CODEX multiplexed imaging data by clustering neighboring cell types using a sliding window for neighborhood analysis.ResultsInterestingly, the agent-based modeling indicated that the tumor phenotype switch to decrease proliferation was more effective than direct T cell killing. We observed spatially restricted inflammatory immune fronts when simulating with different initial percentages of PD1+ T cells and also from our CODEX multiplexed imaging. Quantitatively we observe that there is a drastic increase in the PDL1+, MHCI+, Ki67- tumor phenotype that increases with metabolically inhibited T cells. Neighborhood analysis indicated that metabolically treated T cells were able to create distinct immune cell environments that supported productive T cell-tumor interactions and also helped maintain T cell phenotype.ConclusionsThis indicates there is a balance for therapeutic T cell to mitigate chronic tumor exposure while controlling tumor growth through killing and by changing tumor phenotype. We observe T cells create distinct tumor microenvironments that differs significantly based on the starting T cell phenotype. Controlling T cell phenotype to promote productive immune-tumor structures will be critical to maintain T cell functionality and efficacy. In the future we will investigate T cell recruitment of immune structures by similar systems biology technologies.AcknowledgementsJ.W.H. is funded by an ACS Postdoctoral Fellowship (PF-20-032-01-CSM).ReferencesGoltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M, Vazquez G, Black S and Nolan GP, Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell, 174(4):968–981.Schürch CM, Bhate SS, Barlow GL, Phillips DJ, Noti L, Zlobec I, Chu P, Black S, Demeter J, McIlwain DR and Samusik N. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182(5):1341–1359.Black S, Phillips D, Hickey JW, Kennedy-Darling J, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM. and Nolan GP. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nature Protocols 1–36.Kennedy-Darling J, Bhate SS, Hickey JW, Black S, Barlow GL, Vazquez G, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM and Nolan GP. Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. European Journal of Immunology 51(5):1262–1277.Ethics ApprovalAll studies involving mice were approved under Stanford’s APLAC protocol 33502.

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.

Summary Half of the bacteria in the human gut microbiome are lysogens containing integrated prophages, which may activate in stressful immune environments. Although lysogens are likely to be phagocytosed by macrophages, whether prophage activation occurs or influences the outcome of bacterial infection remains unexplored. To study the dynamics of bacteria-phage interactions in living cells—in particular, the macrophage-triggered induction and lysis of dormant prophages in the phagosome—we adopted a tripartite system where murine macrophages engulf E. coli, which are lysogenic with an engineered bacteriophage λ, containing a fluorescent lysis reporter. Pre-induced prophages are capable of lysing the host bacterium and propagating infection to neighboring bacteria in the same phagosome. A non-canonical pathway, mediated by PhoP, is involved with the native λ phage induction inside phagocytosed E. coli. These findings suggest two possible mechanisms by which induced prophages may function to aid the bactericidal activity of macrophages.

Over the last decade, multiple studies have shown that signaling proteins activated in different temporal patterns—such as oscillatory, transient, and sustained—can result in distinct gene expression patterns or cell fates. However, the molecular events that ensure appropriate stimulus- and dose-dependent dynamics are not often understood and are difficult to investigate. Here, we used single-cell analysis to dissect the mechanisms underlying the stimulus- and dose-encoding patterns in the innate immune signaling network. We found that Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling dynamics relied on a dose-dependent, auto-inhibitory loop that rendered cells refractory to further stimulation. Using inducible gene expression and optogenetics to perturb the network at different levels, we identified the IL-1R–associated kinase1 (IRAK1) as the dose-sensing node responsible for limiting signal flow during the innate immune response. Although the kinase activity of IRAK1 was not required for signal propagation, it played a critical role in inhibiting the nucleocytoplasmic oscillations of the transcription factor NF-κB. Thus, protein activities that may be “dispensable” from a topological perspective can nevertheless be essential in shaping the dynamic response to the external environment.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of

The technology for building functionally complete or 'whole-cell' biological simulations is rapidly developing. However, the predictive capabilities of these simulations are hindered by the availability of parameter values, which are often difficult or even impossible to obtain experimentally and must therefore be estimated. Using E. coli's glycolytic network as a model system, we describe and apply a new method which can estimate the values of all the system's 102 parameters - fit to observations from studies of proteomics, metabolomics, enzyme kinetics and chemical energetics - and find that the resulting metabolic models are not only well-fit, but also dynamically stable. An analysis of how well parameter values in the network were determined by the training data revealed that over 80% of the parameter values were not well-specified. Moreover, the distribution of well-determined values was biased to a specific part of the network and against certain types of experimental data. Our results also suggest that perturbing the functional, energetic space of parameters (rather than traditional metabolic parameters) is a superior strategy for exploring the space of biological dynamics. The estimated parameter values matched both training data and previously withheld validation data within an order of magnitude for over 85% of the data points; notably, the area of greatest frustration in the network was also the most fully determined. Finally, our estimation method showed that fidelity to physiological observations such as network response time is enforced at the cost of fit to molecular parameter values. In summary, our reformulation enables estimation of accurate, biologically relevant parameters, generates insight into the biology of the simulated network, and appears generalizable to any biochemical network - potentially including whole-cell models.

During an infection, immune cells must identify the specific level of threat posed by a given bacterial input in order to generate an appropriate response. Given that they use a general non-self-recognition system, known as Toll-like receptors (TLRs), to detect bacteria, it remains unclear how they transmit information about a particular threat. To determine whether host cells can use signaling dynamics to transmit contextual information about a bacterial stimulus, we use live-cell imaging to make simultaneous quantitative measurements of host MAPK and NF-κB signaling, two key pathways downstream of TLRs, and bacterial infection and load. This combined, single-cell approach reveals that NF-κB and MAPK signaling dynamics are sufficient to discriminate between (1) pathogen-associated molecular patterns (PAMPs) versus bacteria, (2) extracellular versus intracellular bacteria, (3) pathogenic versus non-pathogenic bacteria, and (4) the presence or absence of features indicating an active intracellular bacterial infection, such as replication and effector secretion.

Our ability to build computational models that account for all known gene functions in a cell has increased dramatically. But why build whole-cell models, and how can they best be used? In this review we enumerate several areas in which whole-cell modeling can significantly impact research and technology.

Most features of NF-κB activation dynamics vary significantly with respect to ligand type and concentration. The distribution of the time between two nuclear entries is an invariant feature in populations but not individual cells, suggesting an additional level of control, which regulates the overall distribution of translocation timing., The activation dynamics of nuclear factor (NF)-κB have been shown to affect downstream gene expression. On activation, NF-κB shuttles back and forth across the nuclear envelope. Many dynamic features of this shuttling have been characterized, and most features vary significantly with respect to ligand type and concentration. Here, we report an invariant feature with regard to NF-κB dynamics in cellular populations: the distribution—the average, as well as the variance—of the time between two nuclear entries (the period). We find that this period is conserved, regardless of concentration and across several different ligands. Intriguingly, the distributions observed at the population level are not observed in individual cells over 20-h time courses. Instead, the average period of NF-κB nuclear translocation varies considerably among single cells, and the variance is much smaller within a cell than that of the population. Finally, analysis of daughter-cell pairs and isogenic populations indicates that the dynamics of the NF-κB response is heritable but diverges over multiple divisions, on the time scale of weeks to months. These observations are contrary to the existing theory of NF-κB dynamics and suggest an additional level of control that regulates the overall distribution of translocation timing at the population level.

Although kinases are important regulators of many cellular processes, measuring their activity in live cells remains challenging. We have developed kinase translocation reporters (KTRs), which enable multiplexed measurements of the dynamics of kinase activity at a single-cell level. These KTRs are composed of an engineered construct in which a kinase substrate is fused to a bipartite nuclear localization signal (bNLS) and nuclear export signal (NES), as well as to a fluorescent protein for microscopy-based detection of its localization. The negative charge introduced by phosphorylation of the substrate is used to directly modulate nuclear import and export, thereby regulating the reporter's distribution between the cytoplasm and nucleus. The relative cytoplasmic versus nuclear fluorescence of the KTR construct (the C/N ratio) is used as a proxy for the kinase activity in living, single cells. Multiple KTRs can be studied in the same cell by fusing them to different fluorescent proteins. Here, we present a protocol to execute and analyze live-cell microscopy experiments using KTRs. We describe strategies for development of new KTRs and procedures for lentiviral expression of KTRs in a cell line of choice. Cells are then plated in a 96-well plate, from which multichannel fluorescent images are acquired with automated time-lapse microscopy. We provide detailed guidance for a computational analysis and parameterization pipeline. The entire procedure, from virus production to data analysis, can be completed in ∼10 d.

During its lysogenic life cycle, the phage genome is integrated into the host chromosome by site-specific recombination. In this report, we analyze lambda phage integration into noncanonical sites using next-generation sequencing and show that it generates significant genetic diversity by targeting over 300 unique sites in the host Escherichia coli genome. Moreover, these integration events can have important phenotypic consequences for the host, including changes in cell motility and increased antibiotic resistance. Importantly, the new technologies that we developed to enable this study—sequencing secondary sites using next-generation sequencing and then selecting relevant lysogens using clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9-based selection—are broadly applicable to other phage-bacterium systems., IMPORTANCE Bacteriophages play an important role in bacterial evolution through lysogeny, where the phage genome is integrated into the host chromosome. While phage integration generally occurs at a specific site in the host chromosome, it is also known to occur at other, so-called secondary sites. In this study, we developed a new experimental technology to comprehensively study secondary integration sites and discovered that phage can integrate into over 300 unique sites in the host genome, resulting in significant genetic diversity in bacteria. We further developed an assay to examine the phenotypic consequence of such diverse integration events and found that phage integration can cause changes in evolutionarily relevant traits such as bacterial motility and increases in antibiotic resistance. Importantly, our method is readily applicable to other phage-bacterium systems.

The cell-to-cell spread of cytoplasmic constituents such as nonenveloped viruses and aggregated proteins is usually thought to require cell lysis. However, mechanisms of unconventional secretion have been described that bypass the secretory pathway for the extracellular delivery of cytoplasmic molecules. Components of the autophagy pathway, an intracellular recycling process, have been shown to play a role in the unconventional secretion of cytoplasmic signaling proteins. Poliovirus is a lytic virus, although a few examples of apparently nonlytic spread have been documented. Real demonstration of nonlytic spread for poliovirus or any other cytoplasmic constituent thought to exit cells via unconventional secretion requires demonstration that a small amount of cell lysis in the cellular population is not responsible for the release of cytosolic material. Here, we use quantitative time-lapse microscopy to show the spread of infectious cytoplasmic material between cells in the absence of lysis. siRNA-mediated depletion of autophagy protein LC3 reduced nonlytic intercellular viral transfer. Conversely, pharmacological stimulation of the autophagy pathway caused more rapid viral spread in tissue culture and greater pathogenicity in mice. Thus, the unconventional secretion of infectious material in the absence of cell lysis is enabled by components of the autophagy pathway. It is likely that other nonenveloped viruses also use this pathway for nonlytic intercellular spread to affect pathogenesis in infected hosts.

SummaryIncreasing evidence has shown that population dynamics are qualitatively different from single-cell behaviors. Reporters to probe dynamic, single-cell behaviors are desirable yet relatively scarce. Here, we describe an easy-to-implement and generalizable technology to generate reporters of kinase activity for individual cells. Our technology converts phosphorylation into a nucleocytoplasmic shuttling event that can be measured by epifluorescence microscopy. Our reporters reproduce kinase activity for multiple types of kinases and allow for calculation of active kinase concentrations via a mathematical model. Using this technology, we made several experimental observations that had previously been technicallyunfeasible, including stimulus-dependent patterns of c-Jun N-terminal kinase (JNK) and nuclear factor kappa B (NF-κB) activation. We also measured JNK, p38, and ERK activities simultaneously, finding that p38 regulates the peak number, but not the intensity, of ERK fluctuations. Our approach opens the possibility of analyzing a wide range of kinase-mediated processes in individual cells.

We present two modifications of the flux balance analysis (FBA) metabolic modeling framework which relax implicit assumptions of the biomass reaction. Our flexible flux balance analysis (flexFBA) objective removes the fixed proportion between reactants, and can therefore produce a subset of biomass reactants. Our time-linked flux balance analysis (tFBA) simulation removes the fixed proportion between reactants and byproducts, and can therefore describe transitions between metabolic steady states. Used together, flexFBA and tFBA model a time scale shorter than the regulatory and growth steady state encoded by the biomass reaction. This combined short-time FBA method is intended for integrated modeling applications to enable detailed and dynamic depictions of microbial physiology such as whole-cell modeling. For example, when modeling Escherichia coli, it avoids artifacts caused by low-copy-number enzymes in single-cell models with kinetic bounds. Even outside integrated modeling contexts, the detailed predictions of flexFBA and tFBA complement existing FBA techniques. We show detailed metabolite production of in silico knockouts used to identify when correct essentiality predictions are made for the wrong reason.

Whole-cell modeling promises to facilitate scientific inquiry by prioritizing future experiments based on existing datasets. To test this promise, we compared simulated growth rates with new measurements for all viable single-gene disruption strains in Mycoplasma genitalium. The discrepancies between simulations and experiments led to novel model predictions about specific kinetic parameters that we subsequently validated. These findings represent the first application of whole-cell modeling to accelerate biological discovery.

Although we know a great deal about the phenotype and function of haematopoietic stem/progenitor cells, a major challenge has been mapping their dynamic behaviour within living systems. Here we describe a strategy to image cells in vivo with high spatial and temporal resolution, and quantify their interactions using a high-throughput computational approach. Using these tools, and a new Msi2 reporter model, we show that haematopoietic stem/progenitor cells display preferential spatial affinity for contacting the vascular niche, and a temporal affinity for making stable associations with these cells. These preferences are markedly diminished as cells mature, suggesting that programs that control differentiation state are key determinants of spatiotemporal behaviour, and thus dictate the signals a cell receives from specific microenvironmental domains. These collectively demonstrate that high-resolution imaging coupled with computational analysis can provide new biological insight, and may in the long term enable creation of a dynamic atlas of cells within their native microenvironment., It is difficult to image haematopoietic stem cells (HSC) in their niche. Here, the authors present a new high-throughput computational approach to visualise HSCs in vivo at a high spatial and temporal resolution and also use a Msi2-reporter to label endogenous HSCs and progenitors, enabling cell tracking

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems., Author Summary Dynamic live-cell imaging experiments are a powerful tool to interrogate biological systems with single cell resolution. The key barrier to analyzing data generated by these measurements is image segmentation—identifying which parts of an image belong to which individual cells. Here we show that deep learning is a natural technology to solve this problem for these experiments. We show that deep learning is more accurate, requires less time to curate segmentation results, can segment multiple cell types, and can distinguish between different cell lines present in the same image. We highlight specific design rules that enable us to achieve high segmentation accuracy even with a small number of manually annotated images (~100 cells). We expect that our work will enable new experiments that were previously impossible, as well as reduce the computational barrier for new labs to join the live-cell imaging space.

Recent genome-wide screens of host genetic requirements for viral infection have reemphasized the critical role of host metabolism in enabling the production of viral particles. In this review, we highlight the metabolic aspects of viral infection found in these studies, and focus on the opportunities these requirements present for metabolic engineers. In particular, the objectives and approaches that metabolic engineers use are readily comparable to the behaviors exhibited by viruses during infection. As a result, metabolic engineers have a unique perspective that could lead to novel and effective methods to combat viral infection.

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types 1 . Cell-to-cell communicationisprimarilymediatedbysignallingmoleculesthat form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities 2 . Here we use highthroughputmicrofluidiccellculture 3 andfluorescencemicroscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-a, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-kB. We measured NF-kB activity in thousands of live cells under TNF-a doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays 2 , that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses.Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-kB peak intensity, response time and number ofoscillations.Wedevelopedastochasticmathematicalmodelthat reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-a-induced NF-kB signalling in various types of cells. These results highlight the value of highthroughput quantitative measurements with single-cell resolution in understanding how biological systems operate. Most of the information on cell signalling has been obtained from

During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.

One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. In this study, we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of ‘model-driven discovery’: cases in which computational modeling of a signaling network has led to new insights that have been verified experimentally. Copyright © 2009 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website.

Cells must abide by a number of constraints. The environmental constrains of cellular behavior and physicochemical limitations affect cellular processes. To regulate and adapt their functions, cells impose constraints on themselves. Enumerating, understanding, and applying these constraints leads to a constraints-based modeling formalism that has been helpful in converting conceptual models to computational models in biology. The continued success of the constraints-based approach depends upon identification and incorporation of new constraints to more accurately define cellular capabilities. This review considers constraints in terms of environmental, physicochemical, and self-imposed regulatory and evolutionary constraints with the purpose of refining current constraints-based models of cell phenotype.

Constraints-based models have been effectively used to analyse, interpret, and predict the function of reconstructed genome-scale metabolic models. The first generation of these models used "hard" non-adjustable constraints associated with network connectivity, irreversibility of metabolic reactions, and maximal flux capacities. These constraints restrict the allowable behaviors of a network to a convex mathematical solution space whose edges are extreme pathways that can be used to characterize the optimal performance of a network under a stated performance criterion. The development of a second generation of constraints-based models by incorporating constraints associated with regulation of gene expression was described in a companion paper published in this journal, using flux-balance analysis to generate time courses of growth and by-product secretion using a skeleton representation of core metabolism. The imposition of these additional restrictions prevents the use of a subset of the extreme pathways that are derived from the "hard" constraints, thus reducing the solution space and restricting allowable network functions. Here, we examine the reduction of the solution space due to regulatory constraints using extreme pathway analysis. The imposition of environmental conditions and regulatory mechanisms sharply reduces the number of active extreme pathways. This approach is demonstrated for the skeleton system mentioned above, which has 80 extreme pathways. As regulatory constraints are applied to the system, the number of feasible extreme pathways is reduced to between 26 and 2 extreme pathways, a reduction of between 67.5 and 97.5%. The method developed here provides a way to interpret how regulatory mechanisms are used to constrain network functions and produce a small range of physiologically meaningful behaviors from all allowable network functions.