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Genetics, Vol. 149, 1633-1648, August 1998, Copyright © 1998

Stochastic Kinetic Analysis of Developmental Pathway Bifurcation in Phage {lambda}-Infected Escherichia coli Cells

Adam Arkina, John Rossb, and Harley H. McAdamsa
a Department of Developmental Biology, Stanford University, Stanford, California 94305
b Department of Chemistry, Stanford University, Stanford, California 94305

Corresponding author: Harley H. McAdams, Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305., mcadams{at}cmgm.stanford.edu (E-mail).

Communicating editor: R. S. HAWLEY

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.




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