Bio

Bio


Our focus is on building computational models of complex biological processes, and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory and signaling networks as well as cell-cell interactions. Current research involves the dynamic behavior of NF-kappaB, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.

Academic Appointments


  • Associate Professor, Bioengineering
  • Associate Professor (By courtesy), Chemical and Systems Biology
  • Member, Bio-X

Honors & Awards


  • Robert Black Postdoctoral Fellow, Damon Runyon Cancer Research Foundation (2004-2006)
  • Ezra Taft Benson Presidential Scholar, Brigham Young University (1991-1997)

Professional Education


  • Ph.D., UCSD, Bioengineering/ Bioinformatics (2003)
  • M.S., UCSD, Bioengineering (2002)
  • B.S., Brigham Young University, Chemical Engineering (1997)

Research & Scholarship

Current Research and Scholarly Interests


Our focus is on building computational models of complex biological processes, and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory and signaling networks as well as cell-cell interactions. Current research involves the dynamic behavior of NF-kappaB, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.

Teaching

2013-14 Courses


Graduate and Fellowship Programs


Publications

Journal Articles


  • 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)

    Abstract

    Lipopolysaccharide (LPS), found in the outer membrane of gram-negative bacteria, elicits a strong response from the transcription factor family Nuclear factor (NF)-?B 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-?B dynamics across multiple sources and concentrations of LPS.Using live-cell fluorescence microscopy, we determined the NF-?B 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-?B 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 Web of Science ID 000313480000040

    View details for PubMedID 23301045

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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-?B 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-?B 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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
  • 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 GENOME RESEARCH Herrgard, M. J., Covert, M. W., Palsson, B. O. 2003; 13 (11): 2423-2434

    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 JOURNAL OF BACTERIOLOGY Schilling, C. H., Covert, M. W., Famili, I., Church, G. M., Edwards, J. S., Palsson, B. O. 2002; 184 (16): 4582-4593

    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

Books and Book Chapters


  • Computational Systems Biology. Integrated regulatory and metabolic models Covert, M. W. Academic Press. 2005
  • 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

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