Does replication-induced transcription regulate synthesis of the myriad low copy number proteins of Escherichia coli?

Inflation of the edge of chaos in a simple model of gene interaction networks

We study a set of linearized catalytic reactions to model gene and protein interactions. The model is based on experimentally motivated interaction network topologies and is designed to capture some key properties of gene expression statistics. We impose a nonlinearity to the system by enforcing a boundary condition which guarantees non-negative concentrations of chemical substances. System stability is quantified by maximum Lyapunov exponents. We find that the non-negativity constraint leads to a drastic inflation of those regions in parameter space where the Lyapunov exponent exactly vanishes. Within the model this finding can be fully explained as a result of a symmetry breaking mechanism induced by the positivity constraint. The robustness of this finding with respect to network topologies and the role of intrinsic molecular and external noise is discussed. We argue that systems with inflated "edges of chaos" could be much more easily favored by natural selection than systems where the Lyapunov exponent vanishes only on a parameter set of measure zero.

Single-molecule biophysics: at the interface of biology, physics and chemistry

Single-molecule methods have matured into powerful and popular tools to probe the complex behaviour of biological molecules, due to their unique abilities to probe molecular structure, dynamics and function, unhindered by the averaging inherent in ensemble experiments. This review presents an overview of the burgeoning field of single-molecule biophysics, discussing key highlights and selected examples from its genesis to our projections for its future. Following brief introductions to a few popular single-molecule fluorescence and manipulation methods, we discuss novel insights gained from single-molecule studies in key biological areas ranging from biological folding to experiments performed in vivo.

Noise enhanced persistence in a biochemical regulatory network with feedback control

We find that discrete noise of inhibiting (signal) molecules can greatly delay the extinction of plasmids in a plasmid replication system: a prototypical biochemical regulatory network. We calculate the probability distribution of the metastable state of the plasmids and show in this example that the reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is much shorter than the mean extinction time.

Modelling reaction kinetics inside cells

In the past decade, advances in molecular biology such as the development of non-invasive single molecule imaging techniques have given us a window into the intricate biochemical activities that occur inside cells. In this chapter we review four distinct theoretical and simulation frameworks: (i) non-spatial and deterministic, (ii) spatial and deterministic, (iii) non-spatial and stochastic and (iv) spatial and stochastic. Each framework can be suited to modelling and interpreting intracellular reaction kinetics. By estimating the fundamental length scales, one can roughly determine which models are best suited for the particular reaction pathway under study. We discuss differences in prediction between the four modelling methodologies. In particular we show that taking into account noise and space does not simply add quantitative predictive accuracy but may also lead to qualitatively different physiological predictions, unaccounted for by classical deterministic models.

Discreteness-induced transition in catalytic reaction networks

Drastic change in dynamics and statistics in a chemical reaction system, induced by smallness in the molecule number, is reported. Through stochastic simulations for random catalytic reaction networks, transition to a state is observed with the decrease in the total molecule number N , characterized by (i) large fluctuations in chemical concentrations as a result of intermittent switching over several states with extinction of some molecule species and (ii) strong deviation of time averaged distribution of chemical concentrations from that expected in the continuum limit, i.e., N-->infinity. The origin of transition is explained by the deficiency of the molecule leading to termination of some reactions. The critical number of molecules for the transition is obtained as a function of the number of molecule species M and that of reaction paths K, while total reaction rates, scaled properly, are shown to follow a universal form as a function of NK/M.

Robust protein protein interactions in crowded cellular environments

The capacity of proteins to interact specifically with one another underlies our conceptual understanding of how living systems function. Systems-level study of specificity in protein-protein interactions is complicated by the fact that the cellular environment is crowded and heterogeneous; interaction pairs may exist at low relative concentrations and thus be presented with many more opportunities for promiscuous interactions compared with specific interaction possibilities. Here we address these questions by using a simple computational model that includes specifically designed interacting model proteins immersed in a mixture containing hundreds of different unrelated ones; all of them undergo simulated diffusion and interaction. We find that specific complexes are quite robust to interference from promiscuous interaction partners only in the range of temperatures T(design) > T > T(rand). At T > T(design), specific complexes become unstable, whereas at T < T(rand), formation of specific complexes is suppressed by promiscuous interactions. Specific interactions can form only if T(design) > T(rand). This condition requires an energy gap between binding energy in a specific complex and set of binding energies between randomly associating proteins, providing a general physical constraint on evolutionary selection or design of specific interacting protein interfaces. This work has implications for our understanding of how the protein repertoire functions and evolves within the context of cellular systems.

Stochastic cooperativity in non-linear dynamics of genetic regulatory networks

Two major approaches are known in the field of stochastic dynamics of genetic regulatory networks (GRN). The first one, referred here to as the Markov Process Paradigm (MPP), places the focus of attention on the fact that many biochemical constituents vitally important for the network functionality are present only in small quantities within the cell, and therefore the regulatory process is essentially discrete and prone to relatively big fluctuations. The Master Equation of Markov Processes is an appropriate tool for the description of this kind of stochasticity. The second approach, the Non-linear Dynamics Paradigm (NDP), treats the regulatory process as essentially continuous. A natural tool for the description of such processes are deterministic differential equations. According to NDP, stochasticity in such systems occurs due to possible bistability and oscillatory motion within the limit cycles. The goal of this paper is to outline a third scenario of stochasticity in the regulatory process. This scenario is only conceivable in high-dimensional, highly non-linear systems, and thus represents an adequate framework for conceptually modeling the GRN. We refer to this framework as the Stochastic Cooperativity Paradigm (SCP). In this approach, the focus of attention is placed on the fact that in systems with the size and link density of GRN ( approximately 25000 and approximately 100, respectively), the confluence of all the factors which are necessary for gene expression is a comparatively rare event, and only massive redundancy makes such events sufficiently frequent. An immediate consequence of this rareness is 'burstiness' in mRNA and protein concentrations, a well known effect in intracellular dynamics. We demonstrate that a high-dimensional non-linear system, despite the absence of explicit mechanisms for suppressing inherent instability, may nevertheless reside in a state of stationary pseudo-random fluctuations which for all practical purposes may be regarded as a stochastic process. This type of stochastic behavior is an inherent property of such systems and requires neither an external random force as in the Langevin approach, nor the discreteness of the process as in MPP, nor highly specialized conditions of bistability as in NDP, nor bifurcations with transition to chaos as in low-dimensional chaotic maps.

Subdiffusion and weak ergodicity breaking in the presence of a reactive boundary

We derive the boundary condition for a subdiffusive particle interacting with a reactive boundary with a finite reaction rate. Molecular crowding conditions, that are found to cause subdiffusion of larger molecules in biological cells, are shown to effect long-tailed distributions with an identical exponent for both the unbinding times from the boundary to the bulk and the rebinding times from the bulk. This causes a weak ergodicity breaking: typically, an individual particle either stays bound or remains in the bulk for very long times. We discuss why this may be beneficial for in vivo gene regulation by DNA-binding proteins, whose typical concentrations are nanomolar.

Detection of Green Fluorescent Protein in a Single Bacterium by Capillary Electrophoresis with Laser-Induced Fluorescence

Green fluorescence protein (GFP) is a common reporter used to monitor protein expression in single cells. However, autofluorescence from endogenous components can mask the signal from GFP, particularly at low expression levels in prokaryotes. We employ capillary electrophoresis with laser-induced fluorescence for the analysis of the expression of green fluorescent protein in a single bacterium. Capillary electrophoresis separates GFP from native cellular autofluorescent components, reducing the background signal and improving detection limits. Our system provides 100 ymol (60 copies) limits of detection for GFP. To demonstrate the performance of this instrument, we employ a model system of Deinococcus radiodurans that has been engineered to express GFP under the control of the recA promoter. We report resolution and detection of GFP and autofluorescent components in a single D. radiodurans bacterium. This paper presents the first example of expression of GFP in D. radiodurans and the first detection of GFP in a single bacterium by capillary electrophoresis.

Expression levels of Mycobacterium tuberculosis antigen-encoding genes versus production levels of antigen-specific T cells during stationary level lung infection in mice

Mycobacterium tuberculosis lung infection in mice was controlled at an approximately stationary level after 20 days of log linear growth. Onset of stationary level infection was associated with the generation by the host of T helper type 1 (Th1) immunity, as evidenced by the accumulation of CD4 Th1 cells specific for the early secretory antigen (ESAT-6) of M. tuberculosis encoded by esat6, and for a mycolyl transferase (Ag85B) encoded by fbpB. CD4 T cells specific for these antigens were maintained at relatively high numbers throughout the course of infection. The number of CD4 T cells generated against ESAT-6 was larger than the number generated against Ag85B, and this was associated with a higher transcription level of esat6. The total number of transcripts of esat6 increased during the first 15 days of infection, after which it decreased and then approximately stabilized at 10(6.5) per lung. The total number of fbpB transcripts increased for 20 days of infection before decreasing and then approximately stabilizing at 10(4.8) per lung. The number of transcripts of esat6 per colony-forming unit of M. tuberculosis fell from 8.6 to 0.8 after day 15, and of fbpB from 0.3 to less than 0.02 after day 10, suggesting that at any given time during stationary level infection the latter gene was expressed by a very small percentage of bacilli. Expressed at an even lower level was an M. tuberculosis replication gene involved in septum formation (ftsZ), indicating that there was no significant turnover of the M. tuberculosis population during stationary level infection.

Stochastic protein expression in individual cells at the single molecule level

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.

Probing gene expression in live cells, one protein molecule at a time

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.

Physical nature of bacterial cytoplasm

We track the motion of individual fluorescently labeled mRNA molecules inside live E. coli cells. We find that the motion is subdiffusive, with an exponent that is robust to physiological changes, including the disruption of cytoskeletal elements. By modifying the parameters of the RNA molecule and the bacterial cell, we are able to examine the possible mechanisms that can lead to this unique type of motion, especially the effect of macromolecular crowding. We also examine the implications of anomalous diffusion on the kinetics of bacterial gene regulation, in particular, how transcription factors find their DNA targets.

Real-time kinetics of gene activity in individual bacteria

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.

Sex and estrogenic effects on coexpression of mRNAs in single ventromedial hypothalamic neurons

Regulated gene expression in single neurons can be linked to biophysical events and behavior in the case of estrogen-regulated gene expression in neurons in the ventrolateral portion of the ventromedial nucleus (VMN) of the hypothalamus. These cells are essential for lordosis behavior. What genes are coexpressed in neurons that have high levels of mRNAs for estrogen receptors (ERs)? We have been able to isolate and measure certain mRNAs from individual VMN neurons collected from rat hypothalamus. Large numbers of neurons express mRNA for ERalpha, but these neurons are not identical with the population of VMN neurons expressing the likely gene duplication product, ERbeta. An extremely high proportion of neurons expressing either ER also coexpress mRNA for the oxytocin receptor (OTR). This fact matches the known participation of oxytocin binding and signaling in sexual and affiliative behaviors. In view of data that ER and OTR can signal through PKCs, we looked at coexpression of selected PKCs in the same individual neurons. The most discriminating analysis was for triple coexpression of ERs, OTR, and each selected PKC isoform. These patterns of triple coexpression were significantly different for male vs. female VMN neurons. Further, individual neurons expressing ERalpha could distribute their signaling across the various PKC isoforms differently in different cells, whereas the reverse was not true. These findings and this methodology establish the basis for systematic linkage of the brain's hormone-sensitive signaling pathways to biophysical and behavioral mechanisms in a well studied mammalian system.

Dizzy: stochastic simulation of large-scale genetic regulatory networks

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.

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

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.

Physical limits to biochemical signaling

Many crucial biological processes operate with surprisingly small numbers of molecules, and there is renewed interest in analyzing the impact of noise associated with these small numbers. Twenty-five years ago, Berg and Purcell showed that bacterial chemotaxis, where a single-celled organism must respond to small changes in concentration of chemicals outside the cell, is limited directly by molecule counting noise and that aspects of the bacteria's behavioral and computational strategies must be chosen to minimize the effects of this noise. Here, we revisit and generalize their arguments to estimate the physical limits to signaling processes within the cell and argue that recent experiments are consistent with performance approaching these limits.

Efficient attenuation of stochasticity in gene expression through post-transcriptional control

Thermal fluctuations can lead to significant, unpredictable concentration changes in intracellular molecules, potentially disrupting the functioning of cellular networks and challenging cellular efficiency. Biochemical systems might therefore be expected to have evolved network architectures and motifs that limit the effects of stochastic disturbances. During gene expression itself, stochasticity, or "noise", in protein concentrations is believed to be determined mostly by mRNA, rather than protein, levels. Here, we demonstrate in silico, and analytically, how a number of commonly occurring network architectures in bacteria use mRNA to efficiently attenuate fluctuations. Genes coded in operons share mRNA, which we show generates strongly correlated expression despite multiple ribosome binding sites. For autogeneous control, we provide general analytic expressions using Langevin theory, and demonstrate that negative translational feedback has a much greater efficiency at reducing stochasticity than negative transcriptional feedback. Using the ribosomal proteins as an example, we also show that translational, rather than transcriptional, feedback best coordinates gene expression during assembly of macromolecular complexes. Our findings suggest that selection of a gene controlled post-transcriptionally may be for the resulting low stochasticity in its expression. Such low noise genes can be speculated to play a central role in the local gene network.

Simulated Annealing of Microarray Data Reduces Noise and Enables Cross-Experimental Comparisons

Microarrays are a powerful tool for assessing the genome-wide induction of a transcriptional response to internal or external stimuli, but are not considered quantitatively rigorous (i.e., the signal intensity of hybridized probe is normally used to quantify relative transcript abundance). Thus, it is difficult, if not impossible, to accurately compare separate microarray experiments without a reference standard. However, even among replicated microarray experiments, each gene varies significantly in the amount of signal detected, suggesting no single gene would be appropriate as a standard. We propose and test a method to "align" experimental transcription profiles to a set of reference experiments using simulated annealing (SA), essentially using the relative positions of all genes as a reference standard. SA attempts to find a globally optimal adjustment factor for the relative expression level of each experimental gene expression signal, given a previously observed range of gene expression measurements. By defining a relative dynamic range of gene expression under control conditions for all genes, we can more accurately compare transcription profiles between separate experiments and, potentially, between species--enabling comparative transcriptomics. Testing SA on a published dataset, we find that it significantly reduces interexperimental variation, suggesting it holds promise to accomplish this goal.

Fast evaluation of fluctuations in biochemical networks with the linear noise approximation

Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables.

A mathematical model of single target site location by Brownian movement in subcellular compartments

The location of distinct sites is mandatory for many cellular processes. In the subcompartments of the cell nucleus, only very small numbers of diffusing macromolecules and specific target sites of some types may be present. In this case, we are faced with the Brownian movement of individual macromolecules and their "random search" for single/few specific target sites, rather than bulk-averaged diffusion and multiple sites. In this article, I consider the location of a distant central target site, e.g. a globular protein, by individual macromolecules executing unbiased (i.e. drift-free) random walks in a spherical compartment. For this walk-and-capture model, the closed-form analytic solution of the first passage time probability density function (p.d.f.) has been obtained as well as the first and second moment. In the limit of a large ratio of the radii of the spherical diffusion space and central target, well-known relations for the variance and the first two moments for the exponential p.d.f. were found to hold with high accuracy. These calculations reinforce earlier numerical results and Monte Carlo simulations. A major implication derivable from the model is that non-directed random movement is an effective means for locating single sites in submicron-sized compartments, even when the diffusion coefficients are comparatively small and the diffusing species are present in one copy only. These theoretical conclusions are underscored numerically for effective diffusion constants ranging from 0.5 to 10.0 microm(2) s(-1), which have been reported for a couple of nuclear proteins in their physiological environment. Spherical compartments of submicron size are, for example, the Cajal bodies (size: 0.1-1.0 microm), which are present in 1-5 copies in the cell nucleus. Within a small Cajal body of radius 0.1 microm a single diffusing protein molecule (with D=0.5 microm(2) s(-1)) would encounter a medium-sized protein of radius 2.5 nm within 1 s with a probability near certainty (p=0.98).

Near-critical phenomena in intracellular metabolite pools

The supply and consumption of metabolites in living cells are catalyzed by enzymes. Here we consider two of the simplest schemes where one substrate is eliminated through Michaelis-Menten kinetics, and where two types of substrates are joined together by an enzyme. It is demonstrated how steady-state substrate concentrations can change ultrasensitively in response to changes in their supply rates and how this is coupled to slow relaxation back to steady state after a perturbation. In the one-substrate system, such near-critical behavior occurs when the supply rate approaches the maximal elimination rate, and in the two-substrate system it occurs when the rates of substrate supply are almost balanced. As systems that operate near criticality tend to display large random fluctuations, we also carried out a stochastic analysis using analytical approximations of master equations and compared the results with molecular-level Monte Carlo simulations. It was found that the significance of random fluctuations was directly coupled to the steady-state sensitivity and that the two substrates can fluctuate greatly because they are anticorrelated in such a way that the product formation rate displays only small variation. Basic relations are highlighted and biological implications are discussed.

Intrinsic and extrinsic contributions to stochasticity in gene expression

Gene expression is a stochastic, or "noisy," process. This noise comes about in two ways. The inherent stochasticity of biochemical processes such as transcription and translation generates "intrinsic" noise. In addition, fluctuations in the amounts or states of other cellular components lead indirectly to variation in the expression of a particular gene and thus represent "extrinsic" noise. Here, we show how the total variation in the level of expression of a given gene can be decomposed into its intrinsic and extrinsic components. We demonstrate theoretically that simultaneous measurement of two identical genes per cell enables discrimination of these two types of noise. Analytic expressions for intrinsic noise are given for a model that involves all the major steps in transcription and translation. These expressions give the sensitivity to various parameters, quantify the deviation from Poisson statistics, and provide a way of fitting experiment. Transcription dominates the intrinsic noise when the average number of proteins made per mRNA transcript is greater than approximately 2. Below this number, translational effects also become important. Gene replication and cell division, included in the model, cause protein numbers to tend to a limit cycle. We calculate a general form for the extrinsic noise and illustrate it with the particular case of a single fluctuating extrinsic variable-a repressor protein, which acts on the gene of interest. All results are confirmed by stochastic simulation using plausible parameters for Escherichia coli.

Stochastic gene expression in a single cell

Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.

Self-organisation and orderly processes by individual protein complexes in the bacterial cell

In the bacterial cell, individual multimeric proteins and multiprotein assemblies perform and control orderly processes. Individual motor enzyme complexes accomplish highly complex functions, such as nucleic acid and protein syntheses, with impressive efficiency and fidelity. Lac operon repression by the lac repressor is effectively controlled via a single molecular switch. There are only few copies of, for example, DNA polymerase holoenzyme and lac repressor and few specific target molecules/sites, with which these protein complexes interact, present in a single E. coli cell. These interactive processes take place in submicron-sized spaces characterised by extreme crowding (volume exclusion) of macromolecules and small molecules, heterogeneity and non-ideality. Recent evidence reinforces the fundamental difference of the cytoplasmic as compared with in vitro ("test tube") reaction conditions. This is reflected in the breakdown of the applicability of "bulk phase" thermodynamic, macroscopic chemical kinetic and diffusion laws to interactions of individual macromolecules and target sites in a single cell. Stochastic kinetic models and stochastic simulations enable the statistical description and analysis of biochemical reactions and binding processes which involve small numbers of reactants. New unifying concepts and models are required for the quantitative understanding of the microscopic self-organisation of multi-protein complexes and the dynamic order at the single-protein assembly and single-switch level in the living cell.

Fluctuations in repressor control: thermodynamic constraints on stochastic focusing

The influence of fluctuations in molecule numbers on genetic control circuits has received considerable attention. The consensus has been that such fluctuations will make regulation less precise. In contrast, it has more recently been shown that signal fluctuations can sharpen the response in a regulated process by the principle of stochastic focusing (SF) (, Proc. Natl. Acad. Sci. USA. 97:7148-7153). In many cases, the larger the fluctuations are, the sharper is the response. Here we investigate how fluctuations in repressor or corepressor numbers can improve the control of gene expression. Because SF is found to be constrained by detailed balance, this requires that the control loops contain driven processes out of equilibrium. Some simple and realistic out-of-equilibrium steps that will break detailed balance and make room for SF in such systems are discussed. We conclude that when the active repressors are controlled by corepressor molecules that display large ("coherent") number fluctuations or when corepressors can be irreversibly removed directly from promoter-bound repressors, the response in gene activity can become significantly sharper than without intrinsic noise. A simple experimental design to establish the possibility of SF for repressor control is suggested.

Strategy for qualitative and quantitative analysis in proteomics based on signature peptides

This paper describes a new analytical strategy for identifying proteins in concentration flux based on isotopic labeling peptides in tryptic digests. Primary amino groups in peptides from control and experimental samples were derivatized with acetate and trideuteroacetate, respectively. After mixing samples thus labeled from these two sources, the relative concentration of peptides was determined by isotope ratio analysis with MALDI and ESI mass spectrometry. More than a 100-fold difference in relative concentration could be detected. Simplification of complex tryptic digests prior to mass spectral analysis was achieved by selection of histidine-containing peptides with immobilized metal affinity sorbents or of glycopeptides by lectin columns. Because most of these peptides have sequences that are unique to a single protein, they are a signature of the protein from which they were derived; providing a facile route to protein analysis.

Stochastic focusing: fluctuation-enhanced sensitivity of intracellular regulation

Many regulatory molecules are present in low copy numbers per cell so that significant random fluctuations emerge spontaneously. Because cell viability depends on precise regulation of key events, such signal noise has been thought to impose a threat that cells must carefully eliminate. However, the precision of control is also greatly affected by the regulatory mechanisms' capacity for sensitivity amplification. Here we show that even if signal noise reduces the capacity for sensitivity amplification of threshold mechanisms, the effect on realistic regulatory kinetics can be the opposite: stochastic focusing (SF). SF particularly exploits tails of probability distributions and can be formulated as conventional multistep sensitivity amplification where signal noise provides the degrees of freedom. When signal fluctuations are sufficiently rapid, effects of time correlations in signal-dependent rates are negligible and SF works just like conventional sensitivity amplification. This means that, quite counterintuitively, signal noise can reduce the uncertainty in regulated processes. SF is exemplified by standard hyperbolic inhibition, and all probability distributions for signal noise are first derived from underlying chemical master equations. The negative binomial is suggested as a paradigmatic distribution for intracellular kinetics, applicable to stochastic gene expression as well as simple systems with Michaelis-Menten degradation or positive feedback. SF resembles stochastic resonance in that noise facilitates signal detection in nonlinear systems, but stochastic resonance is related to how noise in threshold systems allows for detection of subthreshold signals and SF describes how fluctuations can make a gradual response mechanism work more like a threshold mechanism.

Replication-induced transcription of an autorepressed gene: the replication initiator gene of plasmid P1

The replication origin of plasmid P1 contains an array of five repeats (iterons) that bind the plasmid-encoded initiator RepA. Within the array lies the repA promoter, which becomes largely repressed on RepA binding (autorepression). One might expect that extra iterons produced on plasmid replication would titrate RepA and release the repression. The promoter, however, is induced poorly by extra iterons. The P1 copy number is reduced by extra iterons in the presence of the autorepressed repA gene but not when additional RepA is provided from constitutive sources. It has been proposed that the iteron-bound RepA couples with the promoter-bound RepA and thereby maintains repression. Although not the product of replication, we find that the act of replication itself can renew RepA synthesis. Replication apparently cleans the promoter of bound RepA and provides a window of opportunity for repA transcription. We propose that replication-induced transcription is required to ensure initiator availability in a system that is induced poorly when challenged with additional initiator binding sites.

Random signal fluctuations can reduce random fluctuations in regulated components of chemical regulatory networks

Many intracellular components are present in low copy numbers per cell and subject to feedback control. We use chemical master equations to analyze a negative feedback system where species X and S regulate each other's synthesis with standard intracellular kinetics. For a given number of X-molecules, S-variation can be significant. We show that this signal noise does not necessarily increase X-variation as previously thought but, surprisingly, can be necessary to reduce it below a Poissonian limit. The principle resembles Stochastic Resonance in that signal noise improves signal detection.

Interference of the simian virus 40 origin of replication by the cytomegalovirus immediate early gene enhancer: evidence for competition of active regulatory chromatin conformation in a single domain

Replication origins are often found closely associated with transcription regulatory elements in both prokaryotic and eukaryotic cells. To examine the relationship between these two elements, we studied the effect of a strong promoter-enhancer on simian virus 40 (SV40) DNA replication. The human cytomegalovirus (CMV) immediate early gene enhancer-promoter was found to exert a strong inhibitory effect on SV40 origin-based plasmid replication in Cos-1 cells in a position- and dose-dependent manner. Deletion analysis indicated that the effect was exerted by sequences located in the enhancer portion of the CMV sequence, thus excluding the mechanism of origin occlusion by transcription. Insertion of extra copies of the SV40 origin only partially alleviated the inhibition. Analysis of nuclease-sensitive cleavage sites of chromatin containing the transfected plasmids indicate that the chromatin was cleaved at one of the regulatory sites in the plasmids containing more than one regulatory site, suggesting that only one nuclease-hypersensitive site existed per chromatin. A positive correlation was found between the degree of inhibition of DNA replication and the decrease of P1 cleavage frequency at the SV40 origin. The CMV enhancer was also found to exhibit an inhibitory effect on the CMV enhancer-promoter driving chloramphenicol acetyltransferase expression in a dose-dependent manner. Together these results suggest that inhibition of SV40 origin-based DNA replication by the CMV enhancer is due to intramolecular competition for the formation of active chromatin structure.

Transcription regulation by repressosome and by RNA polymerase contact

The original model of repression of transcription initiation is steric interference of RNA polymerase binding to a promoter by its repressor protein bound to a DNA site that overlaps the promoter. From the results described here, we propose two other mechanisms of repressor action, both of which involve formation of higher-order DNA-multiprotein complexes. These models also explain the problem of RNA polymerase gaining access to a promoter in the condensed nucleoid in response to an inducing signal to initiate transcription.

Replication-induced protein synthesis and its importance to proteomics

Replication-induced protein synthesis (RIPS) can occur following the passage of the replisome due to transcription initiated by RNA polymerase in association with: (i) negative supercoiling trailing the replisome / replication fork, (ii) hemimethylation prior to the action of dam methylase, (iii) transient derepression following passage of the replisome/replication fork and prior to renewed synthesis of the repressor gene-product, and (iv) 'sliding clamp' accessory DNA-binding proteins binding to the lagging strand DNA duplex to retard rotational upstream propagation of supercoils. The latter include subunits of DNA polymerase III in Escherichia coli and gp45 in T4 bacteriophage. By far the most convincing evidence for the existence of RIPS comes from the pulse of protein synthesis which follows the passage of the replisome in late T4 bacteriophage, the dynamics of replication in Escherichia coli, recent results from cDNA high-density expression arrays in yeast and the workings of the lac-operon. More circumstantial evidence is provided by 'leaky' or 'aberrant' protein expression in genetic systems where attempts have been made to turn off protein synthesis by molecular means. In higher vertebrates, RIPS may have a potentially important role in explaining the mechanisms by which thymic and peripheral immune self-tolerance is established, either directly through antigen presentation on dendritic cells or through the presentation of peptides derived from T-cells. The latter model is preferred, as young T-cells will have recently divided and will be dying in large numbers near the antigen-presenting dendritic cells in the thymus. The functional utility of RIPS would appear to be linked to both facilitating cellular metabolism and an improved survival during stress. RIPS, as a potentially universal molecular phenomenon, presents proteomics with numerous challenges and opportunities, both technical and commercial.

It's a noisy business! Genetic regulation at the nanomolar scale

Many molecules that control genetic regulatory circuits act at extremely low intracellular concentrations. Resultant fluctuations (noise) in reaction rates cause large random variation in rates of development, morphology and the instantaneous concentration of each molecular species in each cell. To achieve regulatory reliability in spite of this noise, cells use redundancy in genes as well as redundancy and extensive feedback in regulatory pathways. However, some regulatory mechanisms exploit this noise to randomize outcomes where variability is advantageous.

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.

Simulation of prokaryotic genetic circuits

Biochemical and genetic approaches have identified the molecular mechanisms of many genetic reactions, particularly in bacteria. Now a comparably detailed understanding is needed of how groupings of genes and related protein reactions interact to orchestrate cellular functions over the cell cycle, to implement preprogrammed cellular development, or to dynamically change a cell's processes and structures in response to environmental signals. Simulations using realistic, molecular-level models of genetic mechanisms and of signal transduction networks are needed to analyze dynamic behavior of multigene systems, to predict behavior of mutant circuits, and to identify the design principles applicable to design of genetic regulatory circuits. When the underlying design rules for regulatory circuits are understood, it will be far easier to recognize common circuit motifs, to identify functions of individual proteins in regulation, and to redesign circuits for altered functions.

'Proteomic contigs' of Mycobacterium tuberculosis and Mycobacterium bovis (BCG) using novel immobilised pH gradients

Tuberculosis remains a major health problem throughout the world and the failure of the existing bacille Calmette-Guérin (BCG) vaccine in recent trials has prompted a search for potential replacements. Recent advances in molecular and cell biology have cast doubts on the ability of genetic analysis alone to predict polygenic human diseases and other complex phenotypes and have therefore redirected our attention to proteome studies to complement information obtained from DNA sequencing initiatives. Novel acidic (pH 2.3-5) and basic (pH 6-11) IPG gel gradients were employed in conjunction with commercially available pH 4-7 gradients to significantly increase (fourfold) the number of protein spots previously resolved on two-dimensional (2-D) gels of Mycobacterium species. A total of 772 and 638 protein spots were observed for M. bovis BCG and M. tuberculosis H37Rv, respectively, the latter corresponding to only the pH regions 4-7 and 6-11. Of interest was the bimodal distribution observed for proteins separated from M. bovis BCG across both M(r) and pH ranges. Some differences in protein expression were observed between these two organisms, contrary to what may have been expected considering the high degree of conservation in gene order and sequence similarity between homologous genes. Further work will be directed towards a more detailed analysis of these differences, so as to allow more accurate diagnosis between vaccination and active tuberculosis. The latter is of major importance to epidemiological studies and for patient management.

Proteomic 'contigs' of Ochrobactrum anthropi, application of extensive pH gradients

The most extensive linear pH gradients yet employed in combination with two-dimensional gel electrophoresis are described, along with their application in proteome analysis. A significant proportion of the protein compliment of bacterial species is believed to be accessible using an extended linear pH gradient of 2.3 to 11.0. Protein standards with predicted isoelectric points (pI) ranging from 3.24 to 9.56 were used to confirm focusing positions with respect to the immobilised pH gradients (IPG) prior to mapping studies of Ochrobactrum anthropi. Multiple gel images were used to construct contiguous windows of protein expression ('proteomic contigs') within 18 cm pH gradients 2.3-5, 4-7, and 6-11 in conjunction with 15% T and 7.5% T acrylamide gels, the latter being used to resolve higher molecular weight (M(r)) proteins. Each IPG had a 5 cm region of similar pH gradient overlap at pH 4-5 and pH 6-7 that was used to construct an image of protein expression characteristic of whole cell lysates. This is reminiscent of genomic sequencing initiatives whereby portions are combined to form a contiguous picture of the whole. The protein maps obtained demonstrated a means of resolving the many tens of thousands of cellular proteins likely to occur in eukaryotic systems, but also highlighted the need to further optimise protein extraction, equilibration buffers, and separation conditions of higher M(r) proteins occurring at extreme pI. Theoretical 2-D protein maps were constructed for five organisms for which the total DNA sequence is now available. In all cases, higher M(r) acidic and basic proteins were shown to be common.

Proteome research: complementarity and limitations with respect to the RNA and DNA worlds

A methodological overview of proteome analysis is provided along with details of efforts to achieve high-throughput screening (HTS) of protein samples derived from two-dimensional electrophoresis gels. For both previously sequenced organisms and those lacking significant DNA sequence information, mass spectrometry has a key role to play in achieving HTS. Prototype robotics designed to conduct appropriate chemistries and deliver 700-1000 protein (genes) per day to batteries of mass spectrometers or liquid chromatography (LC)-based analyses are well advanced, as are efforts to produce high density gridded arrays containing > 1000 proteins on a single matrix assisted laser desorption ionisation/time-of-flight (MALDI-TOF) sample stage. High sensitivity HTS of proteins is proposed by employing principally mass spectrometry in an hierarchical manner: (i) MALDI-TOF-mass spectrometry (MS) on at least 1000 proteins per day; (ii) electrospray ionisation (ESI)/MS/MS for analysis of peptides with respect to predicted fragmentation patterns or by sequence tagging; and (iii) ESI/MS/MS for peptide sequencing. Genomic sequences when complemented with information derived from hybridisation assays and proteome analysis may herald in a new era of holistic cellular biology. The current preoccupation with the absolute quantity of gene-product (RNA and/or protein) should move backstage with respect to more molecularly relevant parameters, such as: molecular half-life; synthesis rate; functional competence (presence or absence of mutations); reaction kinetics; the influence of individual gene-products on biochemical flux; the influence of the environment, cell-cycle, stress and disease on gene-products; and the collective roles of multigenic and epigenetic phenomena governing cellular processes. Proteome analysis is demonstrated as being capable of proceeding independently of DNA sequence information and aiding in genomic annotation. Its ability to confirm the existence of gene-products predicted from DNA sequence is a major contribution to genomic science. The workings of software engines necessary to achieve large-scale proteome analysis are outlined, along with trends towards miniaturisation, analyte concentration and protein detection independent of staining technologies. A challenge for proteome analysis into the future will be to reduce its dependence on two-dimensional (2-D) gel electrophoresis as the preferred method of separating complex mixtures of cellular proteins. Nonetheless, proteome analysis already represents a means of efficiently complementing differential display, high density expression arrays, expressed sequence tags, direct or subtractive hybridisation, chromosomal linkage studies and nucleic acid sequencing as a problem solving tool in molecular biology.

Proteome analysis of Spiroplasma melliferum (A56) and protein characterisation across species boundaries

Spiroplasma melliferum (Class: Mollicutes) is a wall-less, helical bacterium with a genome of approximately 1460 kbp encoding 800-1000 gene-products. A two-dimensional electrophoresis gel reference map of S. melliferum was produced by Phoretix 2-D gel software analysis of eight high quality gels. The reference map showed 456 silver-stained and replicated protein spots. 156 proteins (34% of visible protein spots) from S. melliferum were further characterised by one, or a combination, of the following: amino acid analysis, peptide-mass fingerprinting via matrix assisted laser desorption ionisation-time of flight (MALDI-TOF) mass spectrometry, and N-terminal protein microsequencing. Proteins with close relationship to those previously determined from other species were identified across species barriers. Thus, this study represents the first larger-scale analysis of a proteome based upon the attribution of predominantly 'unique numerical parameters' for protein characterisation across species boundaries, as opposed to a sequence-based approach. This approach allowed all database entries to be screened for homology, as is currently the case for studies based on nucleic acid or protein sequence information. Several proteins studied from this organism were identified as hypothetical, or having no close homolog already present in the databases. Gene-products from major families such as glycolysis, translation, transcription, cellular processes, energy metabolism and protein synthesis were identified. Several gene-products characterised in S. melliferum were not previously found in studies of the entire Mycoplasma genitalium and Mycoplasma pneumoniae (both closely related Mollicutes) genomes. The presence of such gene-products in S. melliferum is discussed in terms of genome size as compared with the smallest known free-living organisms. Finally, the levels of expression of S. melliferum gene-products were determined with respect to total optical intensity associated with all visible proteins expressed in exponentially grown cells.

Stochastic mechanisms in gene expression

In cellular regulatory networks, genetic activity is controlled by molecular signals that determine when and how often a given gene is transcribed. In genetically controlled pathways, the protein product encoded by one gene often regulates expression of other genes. The time delay, after activation of the first promoter, to reach an effective level to control the next promoter depends on the rate of protein accumulation. We have analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links. Simulation of the processes of gene expression shows that proteins are produced from an activated promoter in short bursts of variable numbers of proteins that occur at random time intervals. As a result, there can be large differences in the time between successive events in regulatory cascades across a cell population. In addition, the random pattern of expression of competitive effectors can produce probabilistic outcomes in switching mechanisms that select between alternative regulatory paths. The result can be a partitioning of the cell population into different phenotypes as the cells follow different paths. There are numerous unexplained examples of phenotypic variations in isogenic populations of both prokaryotic and eukaryotic cells that may be the result of these stochastic gene expression mechanisms.

Cooperative relaxation of supercoils and periodic transcriptional initiation within polymerase batteries

Transcription and DNA supercoiling are known to be linked by a cause-effect relationship that operates in both directions. It is proposed here that this two-way relationship may be exploited by the E. coli genome to facilitate constitutive transcription of supercoil-sensitive genes by polymerase batteries made up of uniformly spaces RNA polymerase elongation complexes. Specifically, it is argued that (1) polymerases transcribing DNA in tandem cooperate to relax each other's transcription-driven positive supercoils; and (2) negative supercoils driven upstream by elongation complexes tend to be 'harnessed' and used to cooperatively (and periodically) initiate fresh transcription from promoters. Harnessing of transcription-driven negative supercoils is thought to be achieved through the erection of protein barriers to the rotational upstream propagation of supercoils from transcription events. The possible relevance of such cooperation amongst polymerases to the activation of transcription by DNA-binding protein factors is emphasized. Some testable predictions are made and implications are discussed.