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      Strategies for cellular decision-making

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          Abstract

          Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information-processing strategies cells use in their decision-making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision-making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision-makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decision-making strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision-making network provides a potentially powerful coarse-graining that links systems and evolutionary biology to understand biological design.

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          Most cited references78

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Bacterial persistence as a phenotypic switch.

            A fraction of a genetically homogeneous microbial population may survive exposure to stress such as antibiotic treatment. Unlike resistant mutants, cells regrown from such persistent bacteria remain sensitive to the antibiotic. We investigated the persistence of single cells of Escherichia coli with the use of microfluidic devices. Persistence was linked to preexisting heterogeneity in bacterial populations because phenotypic switching occurred between normally growing cells and persister cells having reduced growth rates. Quantitative measurements led to a simple mathematical description of the persistence switch. Inherent heterogeneity of bacterial populations may be important in adaptation to fluctuating environments and in the persistence of bacterial infections.
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              Phenotypic diversity, population growth, and information in fluctuating environments.

              Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2009
                17 November 2009
                : 5
                : 326
                Affiliations
                [1 ]Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
                [2 ]Centre for Systems Biology at Edinburgh, University of Edinburgh, Edinburgh, UK
                Author notes
                [a ]Centre for Systems Biology at Edinburgh, University of Edinburgh, Mayfield Road, Edinburgh, Scotland EH9 3JD, UK. Tel.: +44 131 650 5451; Fax: +44 131 651 9068; peter.swain@ 123456ed.ac.uk
                Article
                msb200983
                10.1038/msb.2009.83
                2795477
                19920811
                cd43b270-a47a-4c2e-8464-694d45b31dc9
                Copyright © 2009, EMBO and Nature Publishing Group

                This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.

                History
                : 23 June 2009
                : 25 September 2009
                Page count
                Pages: 1
                Categories
                Review Article

                Quantitative & Systems biology
                statistical inference,biochemical networks,social evolution,decision theory,decision-making

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