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      Promoter activity dynamics in the lag phase of Escherichia coli

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          Abstract

          Background

          Lag phase is a period of time with no growth that occurs when stationary phase bacteria are transferred to a fresh medium. Bacteria in lag phase seem inert: their biomass does not increase. The low number of cells and low metabolic activity make it difficult to study this phase. As a consequence, it has not been studied as thoroughly as other bacterial growth phases. However, lag phase has important implications for bacterial infections and food safety. We asked which, if any, genes are expressed in the lag phase of Escherichia coli, and what is their dynamic expression pattern.

          Results

          We developed an assay based on imaging flow cytometry of fluorescent reporter cells that overcomes the challenges inherent in studying lag phase. We distinguish between lag1 phase- in which there is no biomass growth, and lag2 phase- in which there is biomass growth but no cell division. We find that in lag1 phase, most promoters are not active, except for the enzymes that utilize the specific carbon source in the medium. These genes show promoter activities that increase exponentially with time, despite the fact that the cells do not measurably increase in size. An oxidative stress promoter, katG, is also active. When cells enter lag2 and begin to grow in size, they switch to a full growth program of promoter activity including ribosomal and metabolic genes.

          Conclusions

          The observed exponential increase in enzymes for the specific carbon source followed by an abrupt switch to production of general growth genes is a solution of an optimal control model, known as bang-bang control. The present approach contributes to the understanding of lag phase, the least studied of bacterial growth phases.

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          Most cited references60

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          Stochasticity in gene expression: from theories to phenotypes.

          Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
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            A dynamic approach to predicting bacterial growth in food.

            A new member of the family of growth models described by Baranyi et al. (1993a) is introduced in which the physiological state of the cells is represented by a single variable. The duration of lag is determined by the value of that variable at inoculation and by the post-inoculation environment. When the subculturing procedure is standardized, as occurs in laboratory experiments leading to models, the physiological state of the inoculum is relatively constant and independent of subsequent growth conditions. It is shown that, with cells with the same pre-inoculation history, the product of the lag parameter and the maximum specific growth rate is a simple transformation of the initial physiological state. An important consequence is that it is sufficient to estimate this constant product and to determine how the environmental factors define the specific growth rate without modelling the environment dependence of the lag separately. Assuming that the specific growth rate follows the environmental changes instantaneously, the new model can also describe the bacterial growth in an environment where the factors, such as temperature, pH and aw, change with time.
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              FACS-optimized mutants of the green fluorescent protein (GFP).

              We have constructed a library in Escherichia coli of mutant gfp genes (encoding green fluorescent protein, GFP) expressed from a tightly regulated inducible promoter. We introduced random amino acid (aa) substitutions in the twenty aa flanking the chromophore Ser-Tyr-Gly sequence at aa 65-67. We then used fluorescence-activated cell sorting (FACS) to select variants of GFP that fluoresce between 20-and 35-fold more intensely than wild type (wt), when excited at 488 nm. Sequence analysis reveals three classes of aa substitutions in GFP. All three classes of mutant proteins have highly shifted excitation maxima. In addition, when produced in E. coli, the folding of the mutant proteins is more efficient than folding of wt GFP. These two properties contribute to a greatly increased (100-fold) fluorescence intensity, making the mutants useful for a number of applications.
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                Author and article information

                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2013
                30 December 2013
                : 7
                : 136
                Affiliations
                [1 ]Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
                [2 ]Biological Services Unit, Weizmann Institute of Science, Rehovot 76100, Israel
                Article
                1752-0509-7-136
                10.1186/1752-0509-7-136
                3918108
                24378036
                45a7f00d-c26a-433e-89c5-62a4eaddc9ac
                Copyright © 2013 Madar et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 May 2013
                : 21 November 2013
                Categories
                Research Article

                Quantitative & Systems biology
                stringent response,e. coli,lag phase,bang-bang control,optimal control theory,resource allocation,transcriptional program,pontryagin maximum principle

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