Markus Covert

All Publications

• Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons. Cell systems Sun, G., DeFelice, M. M., Gillies, T. E., Ahn-Horst, T. A., Andrews, C. J., Krummenacker, M., Karp, P. D., Morrison, J. H., Covert, M. W. 2024

  Abstract

  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.

  View details for DOI 10.1016/j.cels.2024.02.002

  View details for PubMedID 38417437

• Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. PLoS computational biology Skalnik, C. J., Cheah, S. Y., Yang, M. Y., Wolff, M. B., Spangler, R. K., Talman, L., Morrison, J. H., Peirce, S. M., Agmon, E., Covert, M. W. 2023; 19 (6): e1011232

  Abstract

  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.

  View details for DOI 10.1371/journal.pcbi.1011232

  View details for PubMedID 37327241

• Whole-cell modeling of E. coli confirms that in vitro tRNA aminoacylation measurements are insufficient to support cell growth and predicts a positive feedback mechanism regulating arginine biosynthesis. Nucleic acids research Choi, H., Covert, M. W. 2023

  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.

  View details for DOI 10.1093/nar/gkad435

  View details for PubMedID 37224536

• A multiplexed epitope barcoding strategy that enables dynamic cellular phenotypic screens. Cell systems Kudo, T., Lane, K., Covert, M. W. 2022

  Abstract

  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.

  View details for DOI 10.1016/j.cels.2022.02.006

  View details for PubMedID 35316656

• Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science (New York, N.Y.) Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., Lane, K., Spangler, R. K., Gillies, T. E., Paull, M. L., Akhter, S., Bray, S. R., Weaver, D. S., Keseler, I. M., Karp, P. D., Morrison, J. H., Covert, M. W. 2020; 369 (6502)

  Abstract

  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.

  View details for DOI 10.1126/science.aav3751

  View details for PubMedID 32703847

• Engineered Fluorescent E. coli Lysogens Allow Live-Cell Imaging of Functional Prophage Induction Triggered inside Macrophages. Cell systems Bodner, K. n., Melkonian, A. L., Barth, A. I., Kudo, T. n., Tanouchi, Y. n., Covert, M. W. 2020

  Abstract

  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.

  View details for DOI 10.1016/j.cels.2020.02.006

  View details for PubMedID 32191875

• Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLoS computational biology Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., Covert, M. W. 2016; 12 (11)

  Abstract

  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.

  View details for DOI 10.1371/journal.pcbi.1005177

  View details for PubMedID 27814364

  View details for PubMedCentralID PMC5096676

• High-sensitivity measurements of multiple kinase activities in live single cells. Cell Regot, S., Hughey, J. J., Bajar, B. T., Carrasco, S., Covert, M. W. 2014; 157 (7): 1724-1734

  Abstract

  Increasing 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.

  View details for DOI 10.1016/j.cell.2014.04.039

  View details for PubMedID 24949979

• A Whole-Cell Computational Model Predicts Phenotype from Genotype CELL Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., Covert, M. W. 2012; 150 (2): 389-401

  Abstract

  Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

  View details for DOI 10.1016/j.cell.2012.05.044

  View details for Web of Science ID 000306595700017

  View details for PubMedID 22817898

  View details for PubMedCentralID PMC3413483

• The EcoCyc Database (2023). EcoSal Plus Karp, P. D., Paley, S., Caspi, R., Kothari, A., Krummenacker, M., Midford, P. E., Moore, L. R., Subhraveti, P., Gama-Castro, S., Tierrafria, V. H., Lara, P., Muñiz-Rascado, L., Bonavides-Martinez, C., Santos-Zavaleta, A., Mackie, A., Sun, G., Ahn-Horst, T. A., Choi, H., Covert, M. W., Collado-Vides, J., Paulsen, I. 2023: eesp00022023

  Abstract

  EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

  View details for PubMedID 37220074

• An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control. NPJ systems biology and applications Ahn-Horst, T. A., Mille, L. S., Sun, G., Morrison, J. H., Covert, M. W. 2022; 8 (1): 30

  Abstract

  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.

  View details for DOI 10.1038/s41540-022-00242-9

  View details for PubMedID 35986058

• Microbial metabolites in the marine carbon cycle. Nature microbiology Moran, M. A., Kujawinski, E. B., Schroer, W. F., Amin, S. A., Bates, N. R., Bertrand, E. M., Braakman, R., Brown, C. T., Covert, M. W., Doney, S. C., Dyhrman, S. T., Edison, A. S., Eren, A. M., Levine, N. M., Li, L., Ross, A. C., Saito, M. A., Santoro, A. E., Segre, D., Shade, A., Sullivan, M. B., Vardi, A. 2022; 7 (4): 508-523

  Abstract

  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.

  View details for DOI 10.1038/s41564-022-01090-3

  View details for PubMedID 35365785

• Vivarium: an interface and engine for integrative multiscale modeling in computational biology. Bioinformatics (Oxford, England) Agmon, E., Spangler, R. K., Skalnik, C. J., Poole, W., Peirce, S. M., Morrison, J. H., Covert, M. W. 2022

  Abstract

  This paper 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.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.The specific models, simulation pipelines, and notebooks developed for this paper 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 PyPI. 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.

  View details for DOI 10.1093/bioinformatics/btac049

  View details for PubMedID 35134830

• The E. coli Whole-Cell Modeling Project. EcoSal Plus Sun, G., Ahn-Horst, T. A., Covert, M. W. 2021: eESP00012020

  Abstract

  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.

  View details for DOI 10.1128/ecosalplus.ESP-0001-2020

  View details for PubMedID 34242084

• A forecast for large-scale, predictive biology: Lessons from meteorology. Cell systems Covert, M. W., Gillies, T. E., Kudo, T., Agmon, E. 2021; 12 (6): 488-496

  Abstract

  Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.

  View details for DOI 10.1016/j.cels.2021.05.014

  View details for PubMedID 34139161

• Building Structural Models of a Whole Mycoplasma Cell. Journal of molecular biology Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J., Goodsell, D. S. 2021: 167351

  Abstract

  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.

  View details for DOI 10.1016/j.jmb.2021.167351

  View details for PubMedID 34774566

• A Protocol to Engineer Bacteriophages for Live-Cell Imaging of Bacterial Prophage Induction Inside Mammalian Cells. STAR protocols Bodner, K., Melkonian, A. L., Covert, M. W. 2020; 1 (2): 100084

  Abstract

  The gut microbiome is dominated by lysogens, bacteria that carry bacterial viruses (phages). Uncovering the function of phages in the microbiome and observing interactions between phages, bacteria, and mammalian cells in real time in specific cell types are limited by the difficulty of engineering fluorescent markers into large, lysogenic phage genomes. Here, we present a method to multiplex the engineering of life-cycle reporters into lysogenic phages and how to infect macrophages with engineered lysogens to study these interactions at the single-cell level. For complete details on the use and execution of this protocol, please refer to Bodner et al. (2020).

  View details for DOI 10.1016/j.xpro.2020.100084

  View details for PubMedID 33111117

  View details for PubMedCentralID PMC7580223

• Stress-mediated exit to quiescence restricted by increasing persistence in CDK4/6 activation. eLife Yang, H. W., Cappell, S. D., Jaimovich, A., Liu, C., Chung, M., Daigh, L. H., Pack, L. R., Fan, Y., Regot, S., Covert, M., Meyer, T. 2020; 9

  Abstract

  Mammalian cells typically start the cell-cycle entry program by activating cyclin-dependent protein kinase 4/6 (CDK4/6). CDK4/6 activity is clinically relevant as mutations, deletions, and amplifications that increase CDK4/6 activity contribute to the progression of many cancers. However, when CDK4/6 is activated relative to CDK2 remained incompletely understood. Here we developed a reporter system to simultaneously monitor CDK4/6 and CDK2 activities in single cells and found that CDK4/6 activity increases rapidly before CDK2 activity gradually increases, and that CDK4/6 activity can be active after mitosis or inactive for variable time periods. Markedly, stress signals in G1 can rapidly inactivate CDK4/6 to return cells to quiescence but with reduced probability as cells approach S phase. Together, our study reveals a regulation of G1 length by temporary inactivation of CDK4/6 activity after mitosis, and a progressively increasing persistence in CDK4/6 activity that restricts cells from returning to quiescence as cells approach S phase.

  View details for DOI 10.7554/eLife.44571

  View details for PubMedID 32255427

• The Enemy of My Enemy: New Insights Regarding Bacteriophage-Mammalian Cell Interactions. Trends in microbiology Bodner, K. n., Melkonian, A. L., Covert, M. W. 2020

  Abstract

  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.

  View details for DOI 10.1016/j.tim.2020.10.014

  View details for PubMedID 33243546

• A Protocol to Engineer Bacteriophages for Live-Cell Imaging of Bacterial Prophage Induction Inside Mammalian Cells STAR Protocols Bodner, K., Melkonian, A. L., Covert, M. W. 2020

  View details for DOI 10.1016/j.xpro.2020.100084

• Deep learning for cellular image analysis. Nature methods Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., Van Valen, D. 2019

  Abstract

  Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

  View details for DOI 10.1038/s41592-019-0403-1

  View details for PubMedID 31133758

• NF-kappaB signaling dynamics is controlled by a dose-sensing autoregulatory loop. Science signaling DeFelice, M. M., Clark, H. R., Hughey, J. J., Maayan, I., Kudo, T., Gutschow, M. V., Covert, M. W., Regot, S. 2019; 12 (579)

  Abstract

  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, autoinhibitory loop that rendered cells refractory to further stimulation. Using inducible gene expression and optogenetics to perturb the network at different levels, we identified IL-1R-associated kinase 1 (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-kappaB. Thus, protein activities that may be "dispensable" from a topological perspective can nevertheless be essential in shaping the dynamic response to the external environment.

  View details for PubMedID 31040261

• Techniques for Studying Decoding of Single Cell Dynamics. Frontiers in immunology Jeknić, S., Kudo, T., Covert, M. W. 2019; 10: 755

  Abstract

  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.

  View details for DOI 10.3389/fimmu.2019.00755

  View details for PubMedID 31031756

  View details for PubMedCentralID PMC6470274

• Escalating Threat Levels of Bacterial Infection Can Be Discriminated by Distinct MAPK and NF-kappaB Signaling Dynamics in Single Host Cells. Cell systems Lane, K., Andres-Terre, M., Kudo, T., Monack, D. M., Covert, M. W. 2019

  Abstract

  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-kappaB signaling, two key pathways downstream of TLRs, and bacterial infection and load. This combined, single-cell approach reveals that NF-kappaB 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.

  View details for PubMedID 30904375

• Simultaneous Cross-Evaluation of Heterogeneous E. coli Datasets via Mechanistic Simulation Covert, M. CELL PRESS. 2019: 451A

  View details for DOI 10.1016/j.bpj.2018.11.2429

  View details for Web of Science ID 000460779802265

• Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level. Molecular biology of the cell Gutschow, M. V., Mason, J. C., Lane, K. M., Maayan, I., Hughey, J. J., Bajar, B. T., Amatya, D. N., Valle, S. D., Covert, M. W. 2018: mbcE18070423

  Abstract

  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)-kappaB. 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-kappaB response to combinations of bacterial lipopolysaccharide (LPS) and the stress cytokine Tumor Necrosis Factor (TNF). We found that cells encode the presence of both stimuli via the dynamics of NF-kappaB nuclear translocation in individual cells, suggesting the integration of NF-kappaB 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-kappaB 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.

  View details for PubMedID 30462580

• Live-cell measurements of kinase activity in single cells using translocation reporters NATURE PROTOCOLS Kudo, T., Jeknic, S., Macklin, D. N., Akhter, S., Hughey, J. J., Regot, S., Covert, M. W. 2018; 13 (1): 155–69

  Abstract

  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.

  View details for PubMedID 29266096

• Combining Comprehensive Analysis of Off-Site Lambda Phage Integration with a CRISPR-Based Means of Characterizing Downstream Physiology MBIO Tanouchi, Y., Covert, M. W. 2017; 8 (5)

  Abstract

  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.

  View details for PubMedID 28928209

• Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-?B Activation. Cell systems Lane, K., Van Valen, D., DeFelice, M. M., Macklin, D. N., Kudo, T., Jaimovich, A., Carr, A., Meyer, T., Pe'er, D., Boutet, S. C., Covert, M. W. 2017; 4 (4): 458-469 e5

  Abstract

  Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.

  View details for DOI 10.1016/j.cels.2017.03.010

  View details for PubMedID 28396000

• High-resolution imaging and computational analysis of haematopoietic cell dynamics in vivo. Nature communications Koechlein, C. S., Harris, J. R., Lee, T. K., Weeks, J., Fox, R. G., Zimdahl, B., Ito, T., Blevins, A., Jung, S., Chute, J. P., Chourasia, A., Covert, M. W., Reya, T. 2016; 7: 12169-?

  Abstract

  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.

  View details for DOI 10.1038/ncomms12169

  View details for PubMedID 27425143

  View details for PubMedCentralID PMC4960315

• Why Build Whole-Cell Models? Trends in cell biology Carrera, J., Covert, M. W. 2015; 25 (12): 719-722

  Abstract

  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 forum, we enumerate several areas in which whole-cell modeling can significantly impact research and technology.

  View details for DOI 10.1016/j.tcb.2015.09.004

  View details for PubMedID 26471224

  View details for PubMedCentralID PMC4663153

• NetworkPainter: dynamic intracellular pathway animation in Cytobank BMC BIOINFORMATICS Karr, J. R., Guturu, H., Chen, E. Y., Blair, S. L., Irish, J. M., Kotecha, N., Covert, M. W. 2015; 16

  Abstract

  High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point.NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

  View details for DOI 10.1186/s12859-015-0602-4

  View details for Web of Science ID 000354930700001

  View details for PubMedID 26003204

  View details for PubMedCentralID PMC4491883

• Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS computational biology Karr, J. R., Williams, A. H., Zucker, J. D., Raue, A., Steiert, B., Timmer, J., Kreutz, C., Wilkinson, S., Allgood, B. A., Bot, B. M., Hoff, B. R., Kellen, M. R., Covert, M. W., Stolovitzky, G. A., Meyer, P. 2015; 11 (5)

  Abstract

  Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

  View details for DOI 10.1371/journal.pcbi.1004096

  View details for PubMedID 26020786

  View details for PubMedCentralID PMC4447414

• Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS computational biology Karr, J. R., Williams, A. H., Zucker, J. D., Raue, A., Steiert, B., Timmer, J., Kreutz, C., Wilkinson, S., Allgood, B. A., Bot, B. M., Hoff, B. R., Kellen, M. R., Covert, M. W., Stolovitzky, G. A., Meyer, P. 2015; 11 (5): e1004096

  Abstract

  Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

  View details for DOI 10.1371/journal.pcbi.1004096

  View details for PubMedID 26020786

  View details for PubMedCentralID PMC4447414

• Single-cell variation leads to population invariance in NF-?B signaling dynamics. Molecular biology of the cell Hughey, J. J., Gutschow, M. V., Bajar, B. T., Covert, M. W. 2015; 26 (3): 583-590

  Abstract

  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.

  View details for DOI 10.1091/mbc.E14-08-1267

  View details for PubMedID 25473117

• NetworkPainter: dynamic intracellular pathway animation in Cytobank. BMC bioinformatics Karr, J. R., Guturu, H., Chen, E. Y., Blair, S. L., Irish, J. M., Kotecha, N., Covert, M. W. 2015; 16: 172-?

  Abstract

  High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point.NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

  View details for DOI 10.1186/s12859-015-0602-4

  View details for PubMedID 26003204

  View details for PubMedCentralID PMC4491883

• WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION Karr, J. R., Phillips, N. C., Covert, M. W. 2014

  View details for DOI 10.1093/database/bau095

  View details for Web of Science ID 000342752100001

• Nonlytic viral spread enhanced by autophagy components PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Bird, S. W., Maynard, N. D., Covert, M. W., Kirkegaard, K. 2014; 111 (36): 13081-13086

  Abstract

  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.

  View details for DOI 10.1073/pnas.1401437111

  View details for Web of Science ID 000341625600035

  View details for PubMedCentralID PMC4246951

• Nonlytic viral spread enhanced by autophagy components. Proceedings of the National Academy of Sciences of the United States of America Bird, S. W., Maynard, N. D., Covert, M. W., Kirkegaard, K. 2014; 111 (36): 13081-13086

  Abstract

  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.

  View details for DOI 10.1073/pnas.1401437111

  View details for PubMedID 25157142

  View details for PubMedCentralID PMC4246951

• The future of whole-cell modeling CURRENT OPINION IN BIOTECHNOLOGY Macklin, D. N., Ruggero, N. A., Covert, M. W. 2014; 28: 111-115

  Abstract

  Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

  View details for DOI 10.1016/j.copbio.2014.01.012

  View details for Web of Science ID 000340326400018

  View details for PubMedID 24556244

  View details for PubMedCentralID PMC4111988

• Incorporation of flexible objectives and time-linked simulation with flux balance analysis. Journal of theoretical biology Birch, E. W., Udell, M., Covert, M. W. 2014; 345: 12-21

  Abstract

  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.

  View details for DOI 10.1016/j.jtbi.2013.12.009

  View details for PubMedID 24361328

  View details for PubMedCentralID PMC3933926

• WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database : the journal of biological databases and curation Karr, J. R., Phillips, N. C., Covert, M. W. 2014; 2014

  Abstract

  Mechanistic 'whole-cell' models are needed to develop a complete understanding of cell physiology. However, extracting biological insights from whole-cell models requires running and analyzing large numbers of simulations. We developed WholeCellSimDB, a database for organizing whole-cell simulations. WholeCellSimDB was designed to enable researchers to search simulation metadata to identify simulations for further analysis, and quickly slice and aggregate simulation results data. In addition, WholeCellSimDB enables users to share simulations with the broader research community. The database uses a hybrid relational/hierarchical data format architecture to efficiently store and retrieve both simulation setup metadata and results data. WholeCellSimDB provides a graphical Web-based interface to search, browse, plot and export simulations; a JavaScript Object Notation (JSON) Web service to retrieve data for Web-based visualizations; a command-line interface to deposit simulations; and a Python API to retrieve data for advanced analysis. Overall, we believe WholeCellSimDB will help researchers use whole-cell models to advance basic biological science and bioengineering.http://www.wholecellsimdb.org SOURCE CODE REPOSITORY: URL: http://github.com/CovertLab/WholeCellSimDB.

  View details for DOI 10.1093/database/bau095

  View details for PubMedID 25231498

  View details for PubMedCentralID PMC4165886

• Accelerated discovery via a whole-cell model. Nature methods Sanghvi, J. C., Regot, S., Carrasco, S., Karr, J. R., Gutschow, M. V., Bolival, B., Covert, M. W. 2013; 10 (12): 1192-1195

  Abstract

  To test the promise of whole-cell modeling to facilitate scientific inquiry, we compared growth rates simulated in a whole-cell model with experimental measurements for all viable single-gene disruption Mycoplasma genitalium strains. Discrepancies between simulations and experiments led to predictions about kinetic parameters of specific enzymes that we subsequently validated. These findings represent, to our knowledge, the first application of whole-cell modeling to accelerate biological discovery.

  View details for DOI 10.1038/nmeth.2724

  View details for PubMedID 24185838

  View details for PubMedCentralID PMC3856890

• Accelerated discovery via a whole-cell model NATURE METHODS Sanghvi, J. C., Regot, S., Carrasco, S., Karr, J. R., Gutschow, M. V., Bolival, B., Covert, M. W. 2013; 10 (12): 1192-?

  View details for DOI 10.1038/NMETH.2724

  View details for Web of Science ID 000327698100018

  View details for PubMedID 24185838

• Towards a whole-cell modeling approach for synthetic biology CHAOS Purcell, O., Jain, B., Karr, J. R., Covert, M. W., Lu, T. K. 2013; 23 (2)

  Abstract

  Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

  View details for DOI 10.1063/1.4811182

  View details for Web of Science ID 000321146500045

  View details for PubMedID 23822510

• Single-Cell and Population NF-kappa B Dynamic Responses Depend on Lipopolysaccharide Preparation PLOS ONE Gutschow, M. V., Hughey, J. J., Ruggero, N. A., Bajar, B. T., Valle, S. D., Covert, M. W. 2013; 8 (1)

  View details for DOI 10.1371/journal.pone.0053222

  View details for Web of Science ID 000313480000040

  View details for PubMedID 23301045

• WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic acids research Karr, J. R., Sanghvi, J. C., Macklin, D. N., Arora, A., Covert, M. W. 2013; 41 (Database issue): D787-92

  Abstract

  Whole-cell models promise to greatly facilitate the analysis of complex biological behaviors. Whole-cell model development requires comprehensive model organism databases. WholeCellKB (http://wholecellkb.stanford.edu) is an open-source web-based software program for constructing model organism databases. WholeCellKB provides an extensive and fully customizable data model that fully describes individual species including the structure and function of each gene, protein, reaction and pathway. We used WholeCellKB to create WholeCellKB-MG, a comprehensive database of the Gram-positive bacterium Mycoplasma genitalium using over 900 sources. WholeCellKB-MG is extensively cross-referenced to existing resources including BioCyc, KEGG and UniProt. WholeCellKB-MG is freely accessible through a web-based user interface as well as through a RESTful web service.

  View details for DOI 10.1093/nar/gks1108

  View details for PubMedID 23175606

  View details for PubMedCentralID PMC3531061

• Accelerated discovery via a whole-cell model Nature Methods. Sanghvi, J. C., Regot, S., Carrasco, G. S., Karr, J. R., Gutschow, M. V., Bolival, B., Covert, M. 2013

  View details for DOI 10.1038/nmeth.2724

• WholeCellViz: data visualization for whole-cell models. BMC bioinformatics Lee, R., Karr, J. R., Covert, M. W. 2013; 14 (1): 253-?

  Abstract

  Whole-cell models promise to accelerate biomedical science and engineering. However, discovering new biology from whole-cell models and other high-throughput technologies requires novel tools for exploring and analyzing complex, high-dimensional data.We developed WholeCellViz, a web-based software program for visually exploring and analyzing whole-cell simulations. WholeCellViz provides 14 animated visualizations, including metabolic and chromosome maps. These visualizations help researchers analyze model predictions by displaying predictions in their biological context. Furthermore, WholeCellViz enables researchers to compare predictions within and across simulations by allowing users to simultaneously display multiple visualizations.WholeCellViz was designed to facilitate exploration, analysis, and communication of whole-cell model data. Taken together, WholeCellViz helps researchers use whole-cell model simulations to drive advances in biology and bioengineering.

  View details for DOI 10.1186/1471-2105-14-253

  View details for PubMedID 23964998

  View details for PubMedCentralID PMC3765349

• WholeCellKB: model organism databases for comprehensive whole-cell models NUCLEIC ACIDS RESEARCH Karr, J. R., Sanghvi, J. C., Macklin, D. N., Arora, A., Covert, M. W. 2013; 41 (D1): D787-D792

  View details for DOI 10.1093/nar/gks1108

  View details for Web of Science ID 000312893300111

• Single-cell and population NF-?B dynamic responses depend on lipopolysaccharide preparation. PloS one Gutschow, M. V., Hughey, J. J., Ruggero, N. A., Bajar, B. T., Valle, S. D., Covert, M. W. 2013; 8 (1)

  Abstract

  Lipopolysaccharide (LPS), found in the outer membrane of gram-negative bacteria, elicits a strong response from the transcription factor family Nuclear factor (NF)-κB via Toll-like receptor (TLR) 4. The cellular response to lipopolysaccharide varies depending on the source and preparation of the ligand, however. Our goal was to compare single-cell NF-κB dynamics across multiple sources and concentrations of LPS.Using live-cell fluorescence microscopy, we determined the NF-κB activation dynamics of hundreds of single cells expressing a p65-dsRed fusion protein. We used computational image analysis to measure the nuclear localization of the fusion protein in the cells over time. The concentration range spanned up to nine orders of magnitude for three E. coli LPS preparations. We find that the LPS preparations induce markedly different responses, even accounting for potency differences. We also find that the ability of soluble TNF receptor to affect NF-κB dynamics varies strikingly across the three preparations.Our work strongly suggests that the cellular response to LPS is highly sensitive to the source and preparation of the ligand. We therefore caution that conclusions drawn from experiments using one preparation may not be applicable to LPS in general.

  View details for DOI 10.1371/journal.pone.0053222

  View details for PubMedID 23301045

  View details for PubMedCentralID PMC3536753

• Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation PLOS COMPUTATIONAL BIOLOGY Birch, E. W., Ruggero, N. A., Covert, M. W. 2012; 8 (10)

  Abstract

  Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

  View details for DOI 10.1371/journal.pcbi.1002746

  View details for Web of Science ID 000310568800041

  View details for PubMedID 23093930

  View details for PubMedCentralID PMC3475664

• Neuron-to-neuron transmission of alpha-synuclein fibrils through axonal transport ANNALS OF NEUROLOGY Freundt, E. C., Maynard, N., Clancy, E. K., Roy, S., Bousset, L., Sourigues, Y., Covert, M., Melki, R., Kirkegaard, K., Brahic, M. 2012; 72 (4): 517-524

  Abstract

  The lesions of Parkinson disease spread through the brain in a characteristic pattern that corresponds to axonal projections. Previous observations suggest that misfolded α-synuclein could behave as a prion, moving from neuron to neuron and causing endogenous α-synuclein to misfold. Here, we characterized and quantified the axonal transport of α-synuclein fibrils and showed that fibrils could be transferred from axons to second-order neurons following anterograde transport.We grew primary cortical mouse neurons in microfluidic devices to separate somata from axonal projections in fluidically isolated microenvironments. We used live-cell imaging and immunofluorescence to characterize the transport of fluorescent α-synuclein fibrils and their transfer to second-order neurons.Fibrillar α-synuclein was internalized by primary neurons and transported in axons with kinetics consistent with slow component-b of axonal transport (fast axonal transport with saltatory movement). Fibrillar α-synuclein was readily observed in the cell bodies of second-order neurons following anterograde axonal transport. Axon-to-soma transfer appeared not to require synaptic contacts.These results support the hypothesis that the progression of Parkinson disease can be caused by neuron-to-neuron spread of α-synuclein aggregates and that the anatomical pattern of progression of lesions between axonally connected areas results from the axonal transport of such aggregates. That the transfer did not appear to be trans-synaptic gives hope that α-synuclein fibrils could be intercepted by drugs during the extracellular phase of their journey.

  View details for DOI 10.1002/ana.23747

  View details for Web of Science ID 000310544900009

  View details for PubMedID 23109146

  View details for PubMedCentralID PMC3490229

• Competing pathways control host resistance to virus via tRNA modification and programmed ribosomal frameshifting MOLECULAR SYSTEMS BIOLOGY Maynard, N. D., Macklin, D. N., Kirkegaard, K., Covert, M. W. 2012; 8

  Abstract

  Viral infection depends on a complex interplay between host and viral factors. Here, we link host susceptibility to viral infection to a network encompassing sulfur metabolism, tRNA modification, competitive binding, and programmed ribosomal frameshifting (PRF). We first demonstrate that the iron-sulfur cluster biosynthesis pathway in Escherichia coli exerts a protective effect during lambda phage infection, while a tRNA thiolation pathway enhances viral infection. We show that tRNA(Lys) uridine 34 modification inhibits PRF to influence the ratio of lambda phage proteins gpG and gpGT. Computational modeling and experiments suggest that the role of the iron-sulfur cluster biosynthesis pathway in infection is indirect, via competitive binding of the shared sulfur donor IscS. Based on the universality of many key components of this network, in both the host and the virus, we anticipate that these findings may have broad relevance to understanding other infections, including viral infection of humans.

  View details for DOI 10.1038/msb.2011.101

  View details for Web of Science ID 000299892400001

  View details for PubMedID 22294093

  View details for PubMedCentralID PMC3296357

• High-throughput, single-cell NF-kappa B dynamics CURRENT OPINION IN GENETICS & DEVELOPMENT Lee, T. K., Covert, M. W. 2010; 20 (6): 677-683

  Abstract

  Single cells in a population often respond differently to perturbations in the environment. Live-cell microscopy has enabled scientists to observe these differences at the single-cell level. Some advantages of live-cell imaging over population-based methods include better time resolution, higher sensitivity, automation, and richer datasets. One specific area where live-cell microscopy has made a significant impact is the field of NF-κB signaling dynamics, and recent efforts have focused on making live-cell imaging of these dynamics more high-throughput. We highlight the major aspects of increasing throughput and describe a current system that can monitor, image and analyze the NF-κB activation of thousands of single cells in parallel.

  View details for DOI 10.1016/j.gde.2010.08.005

  View details for Web of Science ID 000285229000016

  View details for PubMedID 20846851

  View details for PubMedCentralID PMC2982878

• Single-cell NF-kappa B dynamics reveal digital activation and analogue information processing NATURE Tay, S., Hughey, J. J., Lee, T. K., Lipniacki, T., Quake, S. R., Covert, M. W. 2010; 466 (7303): 267-U149

  Abstract

  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. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, 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)-alpha, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-kappaB. We measured NF-kappaB activity in thousands of live cells under TNF-alpha doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, 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-kappaB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that 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-alpha-induced NF-kappaB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

  View details for DOI 10.1038/nature09145

  View details for Web of Science ID 000279580800043

  View details for PubMedID 20581820

  View details for PubMedCentralID PMC3105528

• A Forward-Genetic Screen and Dynamic Analysis of Lambda Phage Host-Dependencies Reveals an Extensive Interaction Network and a New Anti-Viral Strategy PLOS GENETICS Maynard, N. D., Birch, E. W., Sanghvi, J. C., Chen, L., Gutschow, M. V., Covert, M. W. 2010; 6 (7)

  Abstract

  Latently infecting viruses are an important class of virus that plays a key role in viral evolution and human health. Here we report a genome-scale forward-genetics screen for host-dependencies of the latently-infecting bacteriophage lambda. This screen identified 57 Escherichia coli (E. coli) genes--over half of which have not been previously associated with infection--that when knocked out inhibited lambda phage's ability to replicate. Our results demonstrate a highly integrated network between lambda and its host, in striking contrast to the results from a similar screen using the lytic-only infecting T7 virus. We then measured the growth of E. coli under normal and infected conditions, using wild-type and knockout strains deficient in one of the identified host genes, and found that genes from the same pathway often exhibited similar growth dynamics. This observation, combined with further computational and experimental analysis, led us to identify a previously unannotated gene, yneJ, as a novel regulator of lamB gene expression. A surprising result of this work was the identification of two highly conserved pathways involved in tRNA thiolation-one pathway is required for efficient lambda replication, while the other has anti-viral properties inhibiting lambda replication. Based on our data, it appears that 2-thiouridine modification of tRNAGlu, tRNAGln, and tRNALys is particularly important for the efficient production of infectious lambda phage particles.

  View details for DOI 10.1371/journal.pgen.1001017

  View details for Web of Science ID 000280512700013

  View details for PubMedID 20628568

  View details for PubMedCentralID PMC2900299

• The virus as metabolic engineer BIOTECHNOLOGY JOURNAL Maynard, N. D., Gutschow, M. V., Birch, E. W., Covert, M. W. 2010; 5 (7): 686-694

  Abstract

  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.

  View details for DOI 10.1002/biot.201000080

  View details for Web of Science ID 000280622500005

  View details for PubMedID 20665642

  View details for PubMedCentralID PMC3004434

• Computational modeling of mammalian signaling networks WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE Hughey, J. J., Lee, T. K., Covert, M. W. 2010; 2 (2): 194-209

  Abstract

  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.

  View details for DOI 10.1002/wsbm.52

  View details for Web of Science ID 000283711700007

  View details for PubMedID 20836022

  View details for PubMedCentralID PMC3105527

• Genome-scale metabolic networks WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE Terzer, M., Maynard, N. D., Covert, M. W., Stelling, J. 2009; 1 (3): 285-297

  Abstract

  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.

  View details for DOI 10.1002/wsbm.37

  View details for Web of Science ID 000283710600002

  View details for PubMedID 20835998

• A Noisy Paracrine Signal Determines the Cellular NF-kappa B Response to Lipopolysaccharide SCIENCE SIGNALING Lee, T. K., Denny, E. M., Sanghvi, J. C., Gaston, J. E., Maynard, N. D., Hughey, J. J., Covert, M. W. 2009; 2 (93)

  Abstract

  Nearly identical cells can exhibit substantially different responses to the same stimulus. We monitored the nuclear localization dynamics of nuclear factor kappaB (NF-kappaB) in single cells stimulated with tumor necrosis factor-alpha (TNF-alpha) and lipopolysaccharide (LPS). Cells stimulated with TNF-alpha have quantitative differences in NF-kappaB nuclear localization, whereas LPS-stimulated cells can be clustered into transient or persistent responders, representing two qualitatively different groups based on the NF-kappaB response. These distinct behaviors can be linked to a secondary paracrine signal secreted at low concentrations, such that not all cells undergo a second round of NF-kappaB activation. From our single-cell data, we built a computational model that captures cell variability, as well as population behaviors. Our findings show that mammalian cells can create "noisy" environments to produce diversified responses to stimuli.

  View details for DOI 10.1126/scisignal.2000599

  View details for Web of Science ID 000275604000003

  View details for PubMedID 19843957

  View details for PubMedCentralID PMC2778577

• A dynamic network of transcription in LPS-treated human subjects BMC SYSTEMS BIOLOGY Seok, J., Xiao, W., Moldawer, L. L., Davis, R. W., Covert, M. W. 2009; 3

  Abstract

  Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.Using NCA, we were able to build a network that accounted for between 8-11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.

  View details for DOI 10.1186/1752-0509-3-78

  View details for Web of Science ID 000269747200001

  View details for PubMedID 19638230

  View details for PubMedCentralID PMC2729748

• Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli BIOINFORMATICS Covert, M. W., Xiao, N., Chen, T. J., Karr, J. R. 2008; 24 (18): 2044-2050

  Abstract

  The effort to build a whole-cell model requires the development of new modeling approaches, and in particular, the integration of models for different types of processes, each of which may be best described using different representation. Flux-balance analysis (FBA) has been useful for large-scale analysis of metabolic networks, and methods have been developed to incorporate transcriptional regulation (regulatory FBA, or rFBA). Of current interest is the integration of these approaches with detailed models based on ordinary differential equations (ODEs).We developed an approach to modeling the dynamic behavior of metabolic, regulatory and signaling networks by combining FBA with regulatory Boolean logic, and ordinary differential equations. We use this approach (called integrated FBA, or iFBA) to create an integrated model of Escherichia coli which combines a flux-balance-based, central carbon metabolic and transcriptional regulatory model with an ODE-based, detailed model of carbohydrate uptake control. We compare the predicted Escherichia coli wild-type and single gene perturbation phenotypes for diauxic growth on glucose/lactose and glucose/glucose-6-phosphate with that of the individual models. We find that iFBA encapsulates the dynamics of three internal metabolites and three transporters inadequately predicted by rFBA. Furthermore, we find that iFBA predicts different and more accurate phenotypes than the ODE model for 85 of 334 single gene perturbation simulations, as well for the wild-type simulations. We conclude that iFBA is a significant improvement over the individual rFBA and ODE modeling paradigms.All MATLAB files used in this study are available at http://www.simtk.org/home/ifba/.Supplementary data are available at Bioinformatics online.

  View details for DOI 10.1093/bioinformatics/btn352

  View details for Web of Science ID 000258959600011

  View details for PubMedID 18621757

• Integrated Flux Balance Analysis Model of Escherichia coli Bioinformatics. Covert, M. W., Xiao, N., Chen, T. J., Karr, J. R. 2008; 18 (24): 2044-2050
• Achieving stability of lipopolysaccharide-induced NF-kappa B activation SCIENCE Covert, M. W., Leung, T. H., Gaston, J. E., Baltimore, D. 2005; 309 (5742): 1854-1857

  Abstract

  The activation dynamics of the transcription factor NF-kappaB exhibit damped oscillatory behavior when cells are stimulated by tumor necrosis factor-alpha (TNFalpha) but stable behavior when stimulated by lipopolysaccharide (LPS). LPS binding to Toll-like receptor 4 (TLR4) causes activation of NF-kappaB that requires two downstream pathways, each of which when isolated exhibits damped oscillatory behavior. Computational modeling of the two TLR4-dependent signaling pathways suggests that one pathway requires a time delay to establish early anti-phase activation of NF-kappaB by the two pathways. The MyD88-independent pathway required Inferon regulatory factor 3-dependent expression of TNFalpha to activate NF-kappaB, and the time required for TNFalpha synthesis established the delay.

  View details for DOI 10.1126/science.1112304

  View details for Web of Science ID 000231989500049

  View details for PubMedID 16166516

• Integrated regulatory and metabolic models Computational Systems Biology, Academic Press, New York Covert, M.W. 2005
• Computational Systems Biology. Integrated regulatory and metabolic models Covert, M. W. Academic Press. 2005
• Integrating high-throughput and computational data elucidates bacterial networks NATURE Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., Palsson, B. O. 2004; 429 (6987): 92-96

  Abstract

  The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.

  View details for Web of Science ID 000221222100051

  View details for PubMedID 15129285

• Reconstruction of microbial transcriptional regulatory networks CURRENT OPINION IN BIOTECHNOLOGY Herrgard, M. J., Covert, M. W., Palsson, B. O. 2004; 15 (1): 70-77

  Abstract

  Although metabolic networks can be readily reconstructed through comparative genomics, the reconstruction of regulatory networks has been hindered by the relatively low level of evolutionary conservation of their molecular components. Recent developments in experimental techniques have allowed the generation of vast amounts of data related to regulatory networks. This data together with literature-derived knowledge has opened the way for genome-scale reconstruction of transcriptional regulatory networks. Large-scale regulatory network reconstructions can be converted to in silico models that allow systematic analysis of network behavior in response to changes in environmental conditions. These models can further be combined with genome-scale metabolic models to build integrated models of cellular function including both metabolism and its regulation.

  View details for DOI 10.1016/j.copbio.2003.11.002

  View details for Web of Science ID 000189358300013

  View details for PubMedID 15102470

• Identifying constraints that govern cell behavior: A key to converting conceptual to computational models in biology? BIOTECHNOLOGY AND BIOENGINEERING Covert, M. W., Famili, I., Palsson, B. O. 2003; 84 (7): 763-772

  Abstract

  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.

  View details for DOI 10.1002/bit.10849

  View details for Web of Science ID 000187634500006

  View details for PubMedID 14708117

• Reconciling gene expression data with known genome-scale regulatory network structures 3rd International Conference on Systems Biology 2002 Herrgard, M. J., Covert, M. W., Palsson, B. O. COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT. 2003: 2423–34

  Abstract

  The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.

  View details for DOI 10.1101/gr.1330003

  View details for Web of Science ID 000186357000008

  View details for PubMedID 14559784

• Constraints-based models: Regulation of gene expression reduces the steady-state solution space JOURNAL OF THEORETICAL BIOLOGY Covert, M. W., Palsson, B. O. 2003; 221 (3): 309-325

  Abstract

  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.

  View details for DOI 10.1006/jtbi.2003.3071

  View details for Web of Science ID 000181779300001

  View details for PubMedID 12642111

• Transcriptional regulation in constraints-based metabolic models of Escherichia coli JOURNAL OF BIOLOGICAL CHEMISTRY Covert, M. W., Palsson, B. O. 2002; 277 (31): 28058-28064

  Abstract

  Full genome sequences enable the construction of genome-scale in silico models of complex cellular functions. Genome-scale constraints-based models of Escherichia coli metabolism have been constructed and used to successfully interpret and predict cellular behavior under a range of conditions. These previous models do not account for regulation of gene transcription and thus cannot accurately predict some organism functions. Here we present an in silico model of the central E. coli metabolism that accounts for regulation of gene expression. This model accounts for 149 genes, the products of which include 16 regulatory proteins and 73 enzymes. These enzymes catalyze 113 reactions, 45 of which are controlled by transcriptional regulation. The combined metabolic/regulatory model can predict the ability of mutant E. coli strains to grow on defined media as well as time courses of cell growth, substrate uptake, metabolic by-product secretion, and qualitative gene expression under various conditions, as indicated by comparison with experimental data under a variety of environmental conditions. The in silico model may also be used to interpret dynamic behaviors observed in cell cultures. This combined metabolic/regulatory model is thus an important step toward the goal of synthesizing genome-scale models that accurately represent E. coli behavior.

  View details for DOI 10.1074/jbc.M201691200

  View details for Web of Science ID 000177189800061

  View details for PubMedID 12006566

• Genome-scale metabolic model of Helicobacter pylori 26695 9th International Conference on Microbial Genomes Schilling, C. H., Covert, M. W., Famili, I., Church, G. M., Edwards, J. S., Palsson, B. O. AMER SOC MICROBIOLOGY. 2002: 4582–93

  Abstract

  A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.

  View details for DOI 10.1128/JB.184.16.4582-4593.2002

  View details for Web of Science ID 000177059500028

  View details for PubMedID 12142428

• Metabolic modelling of microbes: the flux-balance approach Environ Microbiol. Edwards, J. S., Covert, M. W., Palsson, B. Ø. 2002; 3 (4): 133-40
• Regulation of gene expression in flux balance models of metabolism JOURNAL OF THEORETICAL BIOLOGY Covert, M. W., Schilling, C. H., Palsson, B. 2001; 213 (1): 73-88

  Abstract

  Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with flux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, effects of mutation and knock-outs, and gene expression profiles. However, FBA leads to incorrect predictions in situations where regulatory effects are a dominant influence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simplified metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic effects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the effects of transcriptional regulation on cellular metabolism at the systemic level.

  View details for DOI 10.1006/jtbi.2001.2405

  View details for Web of Science ID 000172196000006

  View details for PubMedID 11708855

• Metabolic modeling of microbial strains in silico TRENDS IN BIOCHEMICAL SCIENCES Covert, M. W., Schilling, C. H., Famili, I., Edwards, J. S., Goryanin, I. I., Selkov, E., Palsson, B. O. 2001; 26 (3): 179-186

  Abstract

  The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

  View details for Web of Science ID 000168719800019

  View details for PubMedID 11246024

• Encyclopedia of Microbiology. Genomic Engineering of Bacterial Metabolism Edwards, J. S., Schilling, C. H., Covert, M. W., Smith, S. J., Palsson, B. Ø. Academic Press. 2000