CiteULike: margaritis's experiment

The transcriptional cycle of HIV-1 in real-time and live cells.

S Boireau — 2007-11-12T12:32:05-00:00

J Cell Biol, Vol. 179, No. 2. (22 October 2007), pp. 291-304.

RNA polymerase II (RNAPII) is a fundamental enzyme, but few studies have analyzed its activity in living cells. Using human immunodeficiency virus (HIV) type 1 reporters, we study real-time messenger RNA (mRNA) biogenesis by photobleaching nascent RNAs and RNAPII at specific transcription sites. Through modeling, the use of mutant polymerases, drugs, and quantitative in situ hybridization, we investigate the kinetics of the HIV-1 transcription cycle. Initiation appears efficient because most polymerases demonstrate stable gene association. We calculate an elongation rate of approximately 1.9 kb/min, and, surprisingly, polymerases remain at transcription sites 2.5 min longer than nascent RNAs. With a total polymerase residency time estimated at 333 s, 114 are assigned to elongation, and 63 are assigned to 3'-end processing and/or transcript release. However, mRNAs were released seconds after polyadenylation onset, and analysis of polymerase density by chromatin immunoprecipitation suggests that they pause or lose processivity after passing the polyA site. The strengths and limitations of this kinetic approach to analyze mRNA biogenesis in living cells are discussed.

Transient heterogeneity in extracellular protease production by Bacillus subtilis

Jan-Willem Veening — 2008-04-16T16:31:01-00:00

Mol Syst Biol, Vol. 4 (15 April 2008)

Stochastic gene expression out-of-steady-state in the cyanobacterial circadian clock

Jeffrey Chabot — 2007-12-19T19:53:09-00:00

Nature, Vol. 450, No. 7173. (20 December 2007), pp. 1249-1252.

Dissecting Timing Variability in Yeast Meiosis

Iftach Nachman — 2007-11-07T17:35:40-00:00

Cell, Vol. 131, No. 3. (2 November 2007), pp. 544-556.

Summary Cell-to-cell variability in the timing of cell-fate changes can be advantageous for a population of single-celled organisms growing in a fluctuating environment. We study timing variability during meiosis in Saccharomyces cerevisiae, initiated upon nutritional starvation. We use time-lapse fluorescence microscopy to measure the timing of meiotic events in single cells and find that the duration of meiosis is highly variable between cells. This variability is concentrated between the beginning of starvation and the onset of early meiosis genes. Cell-cycle variability and nutritional history have little effect on this timing variability. Rather, variation in the production rate of the meiotic master regulator Ime1 and its gradual increase over time govern this variability, and cell size effects are channeled through Ime1. These results tie phenotypic variability with expression dynamics of a transcriptional regulator and provide a general framework for the study of temporal developmental processes.

An RNA polymerase mutant with reduced accuracy of chain elongation.

A Blank — 2007-10-09T13:46:22-00:00

Biochemistry, Vol. 25, No. 20. (7 October 1986), pp. 5920-5928.

A new Escherichia coli RNA polymerase mutant was isolated which exhibited reduced accuracy of chain elongation in vivo and in vitro. The novel isolation procedure consisted of simultaneous selection for rifampicin resistance and screening for increased leakiness of an early, strongly polar nonsense mutation of lacZ, one of a special class of mutations whose leakiness reflects mainly transcriptional rather than translational errors. The spontaneous mutant thus isolated displayed a 3-4-fold increase in the leakiness of two different lacZ mutations of this class. Transduction analysis indicated that a single mutation, mapping in or very near the rpoB gene for the beta subunit of RNA polymerase, conferred both rifampicin resistance and increased nonsense leakiness. In an in vitro fidelity assay, homogeneous RNA polymerases from the mutant and parent strains exhibited error rates of 1/0.90 X 10(5) and 1/2.0 X 10(5), respectively, for the poly[d(A-T)] X poly[d(A-T)]-directed misincorporation of noncomplementary GMP. These error rates were verified by product analyses which further revealed that GMP was misincorporated in place of AMP in the synthesis of poly[r(A-U)]. The error rate of wild-type K12 RNA polymerase from a different source was 1/2.0 X 10(5), while that of a hybrid RNA polymerase, containing mutant core enzyme and wild-type sigma subunit, was 1/0.64 X 10(5). These error rates confirmed the selection of a transcriptional accuracy mutant. The error frequencies observed are much lower than those reported in other in vitro assays. The safeguards used to avoid artifactually enhanced misincorporation, and to thereby quantitate lower error rates, are discussed.

In vivo dynamics of RNA polymerase II transcription.

Xavier Darzacq — 2007-08-08T16:29:32-00:00

Nat Struct Mol Biol (5 August 2007)

We imaged transcription in living cells using a locus-specific reporter system, which allowed precise, single-cell kinetic measurements of promoter binding, initiation and elongation. Photobleaching of fluorescent RNA polymerase II revealed several kinetically distinct populations of the enzyme interacting with a specific gene. Photobleaching and photoactivation of fluorescent MS2 proteins used to label nascent messenger RNAs provided sensitive elongation measurements. A mechanistic kinetic model that fits our data was validated using specific inhibitors. Polymerases elongated at 4.3 kilobases min(-1), much faster than previously documented, and entered a paused state for unexpectedly long times. Transcription onset was inefficient, with only 1% of polymerase-gene interactions leading to completion of an mRNA. Our systems approach, quantifying both polymerase and mRNA kinetics on a defined DNA template in vivo with high temporal resolution, opens new avenues for studying regulation of transcriptional processes in vivo.

Combinatorial promoter design for engineering noisy gene expression

Kevin Murphy — 2007-07-25T14:28:44-00:00

PNAS (24 July 2007), 0608451104.

Edited by Nancy J. Kopell, Boston University, Boston, MA, and approved June 15, 2007 (received for review September 26, 2006)Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO2 operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the doseresponse curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimentalcomputational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts. 10.1073/pnas.0608451104

Noise in Gene Expression Determines Cell Fate in Bacillus subtilis.

Hédia Maamar — 2007-06-17T11:23:39-00:00

Science (14 June 2007)

Random cell-to-cell variations in gene expression within an isogenic population can lead to transitions between alternative states of gene expression. Little is known about how these variations (noise) in natural systems affects such transitions. In Bacillus subtilis, noise in ComK, the protein that regulates competence for DNA uptake, is thought to cause cells to transition to the competent state in which genes encoding DNA uptake proteins are expressed. We demonstrate that noise in comK expression selects cells for competence and that experimental reduction of this noise decreases the number of competent cells. We also show that transitions are limited temporally by a reduction in comK transcription. These results show how such stochastic transitions are regulated in a natural system and suggest that noise characteristics are subject to evolutionary forces.

Programmed population control by cell-cell communication and regulated killing.

L You — 2004-11-22T00:17:30-00:00

Nature, Vol. 428, No. 6985. (22 April 2004), pp. 868-871.

De novo engineering of gene circuits inside cells is extremely difficult, and efforts to realize predictable and robust performance must deal with noise in gene expression and variation in phenotypes between cells. Here we demonstrate that by coupling gene expression to cell survival and death using cell-cell communication, we can programme the dynamics of a population despite variability in the behaviour of individual cells. Specifically, we have built and characterized a 'population control' circuit that autonomously regulates the density of an Escherichia coli population. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate. As predicted by a simple mathematical model, the circuit can set a stable steady state in terms of cell density and gene expression that is easily tunable by varying the stability of the cell-cell communication signal. This circuit incorporates a mechanism for programmed death in response to changes in the environment, and allows us to probe the design principles of its more complex natural counterparts.

Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity.

LS Weinberger — 2005-08-23T03:02:15-00:00

Cell, Vol. 122, No. 2. (29 July 2005), pp. 169-182.

HIV-1 Tat transactivation is vital for completion of the viral life cycle and has been implicated in determining proviral latency. We present an extensive experimental/computational study of an HIV-1 model vector (LTR-GFP-IRES-Tat) and show that stochastic fluctuations in Tat influence the viral latency decision. Low GFP/Tat expression was found to generate bifurcating phenotypes with clonal populations derived from single proviral integrations simultaneously exhibiting very high and near zero GFP expression. Although phenotypic bifurcation (PheB) was correlated with distinct genomic integration patterns, neither these patterns nor other extrinsic cellular factors (cell cycle/size, aneuploidy, chromatin silencing, etc.) explained PheB. Stochastic computational modeling successfully accounted for PheB and correctly predicted the dynamics of a Tat mutant that were subsequently confirmed by experiment. Thus, Tat stochastics appear sufficient to generate PheB (and potentially proviral latency), illustrating the importance of stochastic fluctuations in gene expression in a mammalian system.

A Network of Multiple Regulatory Layers Shapes Gene Expression in Fission Yeast

Daniel Lackner — 2007-06-12T13:27:24-00:00

Molecular Cell, Vol. 26, No. 1. (13 April 2007), pp. 145-155.

Summary Gene expression is controlled at multiple layers, and cells may integrate different regulatory steps for coherent production of proper protein levels. We applied various microarray-based approaches to determine key gene-expression intermediates in exponentially growing fission yeast, providing genome-wide data for translational profiles, mRNA steady-state levels, polyadenylation profiles, start-codon sequence context, mRNA half-lives, and RNA polymerase II occupancy. We uncovered widespread and unexpected relationships between distinct aspects of gene expression. Translation and polyadenylation are aligned on a global scale with both the lengths and levels of mRNAs: efficiently translated mRNAs have longer poly(A) tails and are shorter, more stable, and more efficiently transcribed on average. Transcription and translation may be independently but congruently optimized to streamline protein production. These rich data sets, all acquired under a standardized condition, reveal a substantial coordination between regulatory layers and provide a basis for a systems-level understanding of multilayered gene-expression programs.

Phenotypic Consequences of Promoter-Mediated Transcriptional Noise

William Blake — 2007-01-17T04:19:54-00:00

Molecular Cell, Vol. 24, No. 6. (28 December 2006), pp. 853-865.

SummaryA more complete understanding of the causes and effects of cell-cell variability in gene expression is needed to elucidate whether the resulting phenotypes are disadvantageous or confer some adaptive advantage. Here we show that increased variability in gene expression, affected by the sequence of the TATA box, can be beneficial after an acute change in environmental conditions. We rationally introduce mutations within the TATA region of an engineered Saccharomyces cerevisiae GAL1 promoter and measure promoter responses that can be characterized as being either highly variable and rapid or steady and slow. We computationally illustrate how a stable transcription scaffold can result in "bursts" of gene expression, enabling rapid individual cell responses in the transient and increased cell-cell variability at steady state. We experimentally verify computational predictions that the rapid response and increased cell-cell variability enabled by TATA-containing promoters confer a clear benefit in the face of an acute environmental stress.

Probing Gene Expression in Live Cells, One Protein Molecule at a Time

Ji Yu — 2006-03-17T14:18:30-00:00

Science, Vol. 311, No. 5767. (17 March 2006), pp. 1600-1603.

We directly observed real-time production of single protein molecules in individual Escherichia coli cells. A fusion protein of a fast-maturing yellow fluorescent protein (YFP) and a membrane-targeting peptide was expressed under a repressed condition. The membrane-localized YFP can be detected with single-molecule sensitivity. We found that the protein molecules are produced in bursts, with each burst originating from a stochastically transcribed single messenger RNA molecule, and that protein copy numbers in the bursts follow a geometric distribution. The quantitative study of low-level gene expression demonstrates the potential of single-molecule experiments in elucidating the workings of fundamental biological processes in living cells.

Origins of extrinsic variability in eukaryotic gene expression

Dmitri Volfson — 2005-12-22T22:43:36-00:00

Nature (21 December 2005)

Variable gene expression within a clonal population of cells has been implicated in a number of important processes including mutation and evolution, determination of cell fates and the development of genetic disease. Recent studies have demonstrated that a significant component of expression variability arises from extrinsic factors thought to influence multiple genes simultaneously, yet the biological origins of this extrinsic variability have received little attention. Here we combine computational modelling with fluorescence data generated from multiple promoter–gene inserts in Saccharomyces cerevisiae to identify two major sources of extrinsic variability. One unavoidable source arising from the coupling of gene expression with population dynamics leads to a ubiquitous lower limit for expression variability. A second source, which is modelled as originating from a common upstream transcription factor, exemplifies how regulatory networks can convert noise in upstream regulator expression into extrinsic noise at the output of a target gene. Our results highlight the importance of the interplay of gene regulatory networks with population heterogeneity for understanding the origins of cellular diversity.

Gene regulation at the single-cell level.

N Rosenfeld — 2005-03-26T19:48:31-00:00

Science, Vol. 307, No. 5717. (25 March 2005), pp. 1962-1965.

The quantitative relation between transcription factor concentrations and the rate of protein production from downstream genes is central to the function of genetic networks. Here we show that this relation, which we call the gene regulation function (GRF), fluctuates dynamically in individual living cells, thereby limiting the accuracy with which transcriptional genetic circuits can transfer signals. Using fluorescent reporter genes and fusion proteins, we characterized the bacteriophage lambda promoter P(R) in Escherichia coli. A novel technique based on binomial errors in protein partitioning enabled calibration of in vivo biochemical parameters in molecular units. We found that protein production rates fluctuate over a time scale of about one cell cycle, while intrinsic noise decays rapidly. Thus, biochemical parameters, noise, and slowly varying cellular states together determine the effective single-cell GRF. These results can form a basis for quantitative modeling of natural gene circuits and for design of synthetic ones.

Contributions of low molecule number and chromosomal positioning to stochastic gene expression

Attila Becskei — 2005-08-31T04:19:48-00:00

Nature Genetics, Vol. 37, No. 9. (07 August 2005), pp. 937-944.

The presence of low-copy-number regulators and switch-like signal propagation in regulatory networks are expected to increase noise in cellular processes. We developed a noise amplifier that detects fluctuations in the level of low-abundance mRNAs in yeast. The observed fluctuations are not due to the low number of molecules expressed from a gene per se but originate in the random, rare events of gene activation. The frequency of these events and the correlation between stochastic expressions of genes in a single cell depend on the positioning of the genes along the chromosomes. Transcriptional regulators produced by such random expression propagate noise to their target genes.

Regulation of noise in the expression of a single gene.

EM Ozbudak — 2005-05-05T19:23:09-00:00

Nat Genet, Vol. 31, No. 1. (May 2002), pp. 69-73.

Stochastic mechanisms are ubiquitous in biological systems. Biochemical reactions that involve small numbers of molecules are intrinsically noisy, being dominated by large concentration fluctuations. This intrinsic noise has been implicated in the random lysis/lysogeny decision of bacteriophage-lambda, in the loss of synchrony of circadian clocks and in the decrease of precision of cell signals. We sought to quantitatively investigate the extent to which the occurrence of molecular fluctuations within single cells (biochemical noise) could explain the variation of gene expression levels between cells in a genetically identical population (phenotypic noise). We have isolated the biochemical contribution to phenotypic noise from that of other noise sources by carrying out a series of differential measurements. We varied independently the rates of transcription and translation of a single fluorescent reporter gene in the chromosome of Bacillus subtilis, and we quantitatively measured the resulting changes in the phenotypic noise characteristics. We report that of these two parameters, increased translational efficiency is the predominant source of increased phenotypic noise. This effect is consistent with a stochastic model of gene expression in which proteins are produced in random and sharp bursts. Our results thus provide the first direct experimental evidence of the biochemical origin of phenotypic noise, demonstrating that the level of phenotypic variation in an isogenic population can be regulated by genetic parameters.

Intrinsic noise in gene regulatory networks.

M Thattai — 2005-11-18T20:11:36-00:00

Proc Natl Acad Sci U S A, Vol. 98, No. 15. (17 July 2001), pp. 8614-8619.

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

Noise Propagation in Gene Networks

Juan Pedraza — 2005-03-29T06:08:56-00:00

Science, Vol. 307, No. 5717. (25 March 2005), pp. 1965-1969.

Accurately predicting noise propagation in gene networks is crucial for understanding signal fidelity in natural networks and designing noise-tolerant gene circuits. To quantify how noise propagates through gene networks, we measured expression correlations between genes in single cells. We found that noise in a gene was determined by its intrinsic fluctuations, transmitted noise from upstream genes, and global noise affecting all genes. A model was developed that explains the complex behavior exhibited by the correlations and reveals the dominant noise sources. The model successfully predicts the correlations as the network is systematically perturbed. This approach provides a step toward understanding and manipulating noise propagation in more complex gene networks.

Monitoring dynamics of single-cell gene expression over multiple cell cycles

Scott Cookson — 2005-11-23T02:38:38-00:00

Molecular Systems Biology, Vol. 1, No. 1. (22 November 2005), pp. msb4100032-E1-msb4100032-E6.

Recent progress in reconstructing gene regulatory networks has established a framework for a quantitative description of the dynamics of many important cellular processes. Such a description will require novel experimental techniques that enable the generation of time-series data for the governing regulatory proteins in a large number of individual living cells. Here, we utilize microfabrication to construct a Tesla microchemostat that permits single-cell fluorescence imaging of gene expression over many cellular generations. The device is used to capture and constrain asymmetrically dividing or motile cells within a trapping region and to deliver nutrients and regulate the cellular population within this region. We illustrate the operation of the microchemostat with Saccharomyces cerevisiae and explore the evolution of single-cell gene expression and cycle time as a function of generation. Our findings highlight the importance of novel assays for quantifying the dynamics of gene expression and cellular growth, and establish a methodology for exploring the effects of gene expression on long-term processes such as cellular aging.

A synthetic oscillatory network of transcriptional regulators.

MB Elowitz — 2004-11-22T00:17:30-00:00

Nature, Vol. 403, No. 6767. (20 January 2000), pp. 335-338.

Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems. Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function. We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli. The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells. The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation. This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components. Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks.

Stochastic protein expression in individual cells at the single molecule level

Long Cai — 2006-03-15T23:36:34-00:00

Nature, Vol. 440, No. 7082., pp. 358-362.

In a living cell, gene expression—the transcription of DNA to messenger RNA followed by translation to protein—occurs stochastically, as a consequence of the low copy number of DNA and mRNA molecules involved. These stochastic events of protein production are difficult to observe directly with measurements on large ensembles of cells owing to lack of synchronization among cells. Measurements so far on single cells lack the sensitivity to resolve individual events of protein production. Here we demonstrate a microfluidic-based assay that allows real-time observation of the expression of beta-galactosidase in living Escherichia coli cells with single molecule sensitivity. We observe that protein production occurs in bursts, with the number of molecules per burst following an exponential distribution. We show that the two key parameters of protein expression—the burst size and frequency—can be either determined directly from real-time monitoring of protein production or extracted from a measurement of the steady-state copy number distribution in a population of cells. Application of this assay to probe gene expression in individual budding yeast and mouse embryonic stem cells demonstrates its generality. Many important proteins are expressed at low levels, and are thus inaccessible by current genomic and proteomic techniques. This microfluidic single cell assay opens up possibilities for system-wide characterization of the expression of these low copy number proteins.

Noise in eukaryotic gene expression.

WJ Blake — 2005-05-18T04:21:42-00:00

Nature, Vol. 422, No. 6932. (10 April 2003), pp. 633-637.

Transcription in eukaryotic cells has been described as quantal, with pulses of messenger RNA produced in a probabilistic manner. This description reflects the inherently stochastic nature of gene expression, known to be a major factor in the heterogeneous response of individual cells within a clonal population to an inducing stimulus. Here we show in Saccharomyces cerevisiae that stochasticity (noise) arising from transcription contributes significantly to the level of heterogeneity within a eukaryotic clonal population, in contrast to observations in prokaryotes, and that such noise can be modulated at the translational level. We use a stochastic model of transcription initiation specific to eukaryotes to show that pulsatile mRNA production, through reinitiation, is crucial for the dependence of noise on transcriptional efficiency, highlighting a key difference between eukaryotic and prokaryotic sources of noise. Furthermore, we explore the propagation of noise in a gene cascade network and demonstrate experimentally that increased noise in the transcription of a regulatory protein leads to increased cell-cell variability in the target gene output, resulting in prolonged bistable expression states. This result has implications for the role of noise in phenotypic variation and cellular differentiation.

Dynamics of protein distributions in cell populations

Naama Brenner — 2006-09-08T16:11:26-00:00

Phys. Biol., Vol. 3, No. 3. (September 2006), 172.

Real-time kinetics of gene activity in individual bacteria.

I Golding — 2006-02-14T22:25:58-00:00

Cell, Vol. 123, No. 6. (16 December 2005), pp. 1025-1036.

Protein levels have been shown to vary substantially between individual cells in clonal populations. In prokaryotes, the contribution to such fluctuations from the inherent randomness of gene expression has largely been attributed to having just a few transcripts of the corresponding mRNAs. By contrast, eukaryotic studies tend to emphasize chromatin remodeling and burst-like transcription. Here, we study single-cell transcription in Escherichia coli by measuring mRNA levels in individual living cells. The results directly demonstrate transcriptional bursting, similar to that indirectly inferred for eukaryotes. We also measure mRNA partitioning at cell division and correlate mRNA and protein levels in single cells. Partitioning is approximately binomial, and mRNA-protein correlations are weaker earlier in the cell cycle, where cell division has recently randomized the relative concentrations. Our methods further extend protein-based approaches by counting the integer-valued number of transcript with single-molecule resolution. This greatly facilitates kinetic interpretations in terms of the integer-valued random processes that produce the fluctuations.

Variability and memory of protein levels in human cells

Alex Sigal — 2006-11-19T21:34:19-00:00

Nature (19 November 2006)

Protein expression is a stochastic process that leads to phenotypic variation among cells. The cell-cell distribution of protein levels in microorganisms has been well characterized but little is known about such variability in human cells. Here, we studied the variability of protein levels in human cells, as well as the temporal dynamics of this variability, and addressed whether cells with higher than average protein levels eventually have lower than average levels, and if so, over what timescale does this mixing occur. We measured fluctuations over time in the levels of 20 endogenous proteins in living human cells, tagged by the gene for yellow fluorescent protein at their chromosomal loci. We found variability with a standard deviation that ranged, for different proteins, from about 15% to 30% of the mean. Mixing between high and low levels occurred for all proteins, but the mixing time was longer than two cell generations (more than 40 h) for many proteins. We also tagged pairs of proteins with two colours, and found that the levels of proteins in the same biological pathway were far more correlated than those of proteins in different pathways. The persistent memory for protein levels that we found might underlie individuality in cell behaviour and could set a timescale needed for signals to affect fully every member of a cell population.