© 2005 Nature Publishing Group *Department of Cellular and Molecular Medicine and Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H8M5, Canada.‡Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina 27599, USA.§Department of Biomedical Engineering and Center for BioDynamics, Boston University, 44 Cummington Street, Boston, Massachusetts 02215, USA. Correspondence to M.K.e-mail: [email protected] online 10 May 2005doi:10.1038/nrg1615Stochasticity in gene expression arises from fluctuations in transcription and translation, despite constant envi-ronmental conditions. This phenomenon has attracted interest for many years because of its implications for cellular regulation and non-genetic individuality1–7. Recent advances in techniques for single-cell analysis have provided an impetus for novel experimental and theoretical investigations that, in turn, have led to fundamental new insights in this field. As a result, a coherent picture of stochasticity in prokaryotic and eukaryotic gene expression is beginning to emerge.Here, we discuss the theoretical mechanisms that are thought to cause fluctuations in the expression levels of single genes and the experiments that have been used to validate these ideas. We also describe experimental studies of stochastic effects in gene-regulatory net-works. Special emphasis is given to stochastic mecha-nisms that can lead to the emergence of phenotypically distinct subgroups within ISOGENIC cell populations. We conclude by discussing the possibility that stochasticity in gene expression is an evolvable trait, and the grow-ing evidence for a role of stochasticity in development and disease.Origins and consequences of stochasticityModelling the expression of a single gene. FIGURE 1 illustrates some of the main steps in gene expression. The control of transcription is mediated by factors that bind at upstream promoter elements or influence the binding of other molecules to cis-regulatory elements within or near the promoter. Because such binding events are the result of random encounters between molecules, some of which are present in small num-bers, the biochemical processes that regulate transcrip-tion initiation are inherently stochastic. In addition, the multi-step processes that lead to the synthesis and degradation of mRNA and protein molecules are subject to similar molecular-level noise. The model in FIG. 1 is simple in comparison with the true complexity of gene expression8. However, it has provided a good theoretical framework for understanding the effects of stochasticity on prokaryotic and eukaryotic gene expres-sion9–49, and underlies the theoretical investigations used to design and interpret many of the experiments discussed in this review. The origins and consequences of molecular-level noise on the expression of a single gene can be dem-onstrated by comparing the intracellular protein con-centrations obtained from stochastic and deterministic simulations of the model in FIG. 1. Deterministic simu-lations typically use rate equations BOX 1 — which do not take stochastic processes into account — to describe changes in mRNA and protein abundances. By contrast, stochastic simulations typically consider STOCHASTICITY IN GENE EXPRESSION: FROM THEORIES TO PHENOTYPESMads Kærn*, Timothy C. Elston‡, William J. Blake§ and James J. Collins§Abstract | 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.ISOGENICGenetically identical. Individual cells within an isogenic population are typically the progeny of a single ancestor. NATURE REVIEWS | GENETICS VOLUME 6 | JUNE 2005 | 451REVIEWS© 2005 Nature Publishing Group Repressed promoter (R)konsAsRsPδMδPkoffmRNA (M) Protein (P)Active promoter (A)d[M]dt=kon+koffAV+kon+koffRV−M[]onoffkkssMδd [P]dt= sP[M] ]−P[Pδthe random formation and decay of single molecules and multi-component complexes explicitly. As a result, the deterministic approach cannot capture the poten-tially significant effects of factors that cause stochasticity in gene expression. In certain circumstances, deterministic simula-tions of the model in FIG. 1 predict intracellular protein concentrations that are similar to those predicted by stochastic simulations. The conditions that need to be satisfied for the predictions of the two approaches to be similar are large system size (high numbers of expressed mRNA and proteins, and large cell vol-umes) and fast promoter kinetics (BOX 1; see below). These conditions are met in the example illustrated in FIG. 2a, in which the protein concentration (the overall measure of gene expression) predicted by a stochastic simulation fluctuates with very low amplitude around the average level predicted by a deterministic simula-tion. Correspondingly, the relative deviation from the average, measured by the ratio η of the standard deviation σ to the mean N, is quite small. This ratio η (or, alternatively, η2) is typically referred to as the coefficient of variation, or the noise.When the conditions required for good agreement between deterministic and stochastic simulations are not fulfilled, the effects of molecular-level noise can Figure 1 | A model of the expression of a single gene. Each step represents several biochemical reactions, which are associated with mRNA and protein production, transitions between promoter states and the decay of mRNA and protein. kon, koff, sA, sR, sP, δM and δP are the rate constants associated with these steps, as indicated. These reactions involve binding and dissociation events that occur at random at the molecular level. This is ignored in deterministic models of gene expression, which typically describe the different steps in terms of reaction rates. Stochastic models generally describe each step as a single random event, with a reaction time
View Full Document
Unlocking...