Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.
Fluctuations in rates of gene expression can produce highly erratic time patterns of protein production in individual cells and wide diversity in instantaneous protein concentrations across cell populations. When two independently produced regulatory proteins acting at low cellular concentrations competitively control a switch point in a pathway, stochastic variations in their concentrations can produce probabilistic pathway selection, so that an initially homogeneous cell population partitions into distinct phenotypic subpopulations. Many pathogenic organisms, for example, use this mechanism to randomly switch surface features to evade host responses. This coupling between molecular-level fluctuations and macroscopic phenotype selection is analyzed using the phage lambda lysis-lysogeny decision circuit as a model system. The fraction of infected cells selecting the lysogenic pathway at different phage:cell ratios, predicted using a molecular-level stochastic kinetic model of the genetic regulatory circuit, is consistent with experimental observations. The kinetic model of the decision circuit uses the stochastic formulation of chemical kinetics, stochastic mechanisms of gene expression, and a statistical-thermodynamic model of promoter regulation. Conventional deterministic kinetics cannot be used to predict statistics of regulatory systems that produce probabilistic outcomes. Rather, a stochastic kinetic analysis must be used to predict statistics of regulatory outcomes for such stochastically regulated systems.
Department of Developmental Biology, Stanford University, Stanford, California 94305, USA.
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Arkin
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Ross
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McAdams
D010582:Bacteriophage lambda / Q000235:genetics* / Q000472:pathogenicity* / Q000502:physiology; D019281:Dimerization; D004926:Escherichia coli / Q000254:growth & development / Q000821:virology*; D015967:Gene Expression Regulation, Viral; D007700:Kinetics; D008242:Lysogeny / Q000235:genetics; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D013269:Stochastic Processes; D014764:Viral Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism
D014764:Viral Proteins