Bibliography of: Gene Expression Regulation Bacterial

  1. Karp, P.D., Riley, M., Saier, M., Paulsen, I.T., Collado-Vides, J., Paley, S.M., Pellegrini-Toole, A., Bonavides, C., and Gama-Castro, S.. "The EcoCyc Database." Nucleic Acids Res. 30 (1). 2002. pp. 56-8.
    [ .pdf ] [ PubMed ]

    EcoCyc is an organism-specific pathway/genome database that describes the metabolic and signal-transduction pathways of Escherichia coli, its enzymes, its transport proteins and its mechanisms of transcriptional control of gene expression. EcoCyc is queried using the Pathway Tools graphical user interface, which provides a wide variety of query operations and visualization tools. EcoCyc is available at http://ecocyc.org/.

    Keywords: Database Management Systems ; *Databases Genetic ; Escherichia coli_*genetics ; Escherichia coli_*metabolism ; Escherichia coli Proteins_*genetics ; Escherichia coli Proteins_*physiology ; Gene Expression Regulation Bacterial ; *Genome Bacterial ; Information Storage and Retrieval ; Internet ; Protein Transport ; Signal Transduction


  2. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S., and Gilles, E.D.. "Metabolic network structure determines key aspects of functionality and regulation." Nature. 420 (6912). 2002. pp. 190-3.
    [ .pdf ] [ PubMed ]

    The relationship between structure, function and regulation in complex cellular networks is a still largely open question. Systems biology aims to explain this relationship by combining experimental and theoretical approaches. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness or metabolic phenotype, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.

    Keywords: Biomass ; Cell Physiology ; Computational Biology ; Computer Simulation ; *Energy Metabolism ; Escherichia coli_genetics ; Escherichia coli_growth and development ; Escherichia coli_*metabolism ; Gene Expression Regulation Bacterial ; *Models Biological ; Phenotype ; Systems Theory


  3. Srivastava, R., Peterson, M.S., and Bentley, W.E.. "Stochastic kinetic analysis of the Escherichia coli stress circuit using sigma(32)-targeted antisense." Biotechnol Bioeng. 75 (1). 2001. pp. 120-9.
    [ PubMed ]

    A stochastic Petri net model was developed for simulating the sigma(32) stress circuit in E. coli. Transcription factor sigma(32) is the principal regulator of the response of E. coli to heat shock. Stochastic Petri net (SPN) models are well suited for kinetics characterization of fluxes in biochemical pathways. Notably, there exists a one-to-one mapping of model tokens and places to molecules of particular species. Our model was validated against experiments in which ethanol (inducer of heat shock response) and sigma(32)-targeted antisense (downward regulator) were used to perturb the sigma(32) regulatory pathway. The model was also extended to simulate the effects of recombinant protein production. Results show that the stress response depends heavily on the partitioning of sigma(32) within the cell; that is, sigma(32) becomes immediately available to mediate a stress response because it exists primarily in a sequestered, inactive form, complexed with chaperones DnaK, DnaJ, and GrpE. Recombinant proteins, however, also compete for chaperone proteins, particularly when folded improperly. Our simulations indicate that when the expression of recombinant protein has a low requirement for DnaK, DnaJ, and GrpE, the overall sigma(32) levels may drop, but the level of heat shock proteins will increase. Conversely, when the overexpressed recombinant protein has a strong requirement for the chaperones, a severe response is predicted. Interestingly, both cases were observed experimentally.

    Keywords: Antisense Elements (Genetics) ; Computer Simulation ; Escherichia coli_*genetics ; Escherichia coli_*metabolism ; Ethanol ; Gene Expression Regulation Bacterial ; Heat-Shock Proteins_genetics ; Heat-Shock Proteins_metabolism ; *Models Biological ; Recombinant Proteins_genetics ; Sigma Factor_*genetics ; Sigma Factor_*metabolism ; Solvents ; Stochastic Processes