Stochastic Fluctuations In Gene Expression Research Articles

Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State.

Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4+ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.

A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions

Stochastic fluctuations in gene expression (‘noise’) are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base-excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA-repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean-expression levels. This amplified expression noise originates from shorter duration, higher intensity, transcriptional bursts generated by Apex1-mediated DNA supercoiling.

Understanding the role of phenotypic switching in cancer drug resistance

The emergence of acquired drug resistance in cancer represents a major barrier to treatment success. While research has traditionally focused on genetic sources of resistance, recent findings suggest that cancer cells can acquire transient resistant phenotypes via epigenetic modifications and other non-genetic mechanisms. Although these resistant phenotypes are eventually relinquished by individual cells, they can temporarily ’save’ the tumor from extinction and enable the emergence of more permanent resistance mechanisms. These observations have generated interest in the potential of epigenetic therapies for long-term tumor control or eradication. In this work, we develop a mathematical model to study how phenotypic switching at the single-cell level affects resistance evolution in cancer. We highlight unique features of non-genetic resistance, probe the evolutionary consequences of epigenetic drugs and explore potential therapeutic strategies. We find that even short-term epigenetic modifications and stochastic fluctuations in gene expression can drive long-term drug resistance in the absence of any bona fide resistance mechanisms. We also find that an epigenetic drug that slightly perturbs the average retention of the resistant phenotype can turn guaranteed treatment failure into guaranteed success. Lastly, we find that combining an epigenetic drug with an anti-cancer agent can significantly outperform monotherapy, and that treatment outcome is heavily affected by drug sequencing.

Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise.

Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feedback and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

Gene expression is an inherently noisy process. This noise is generally thought to be deleterious as precise internal regulation of biochemical reactions is essential for cell growth and survival. Self-repression of gene expression, which is the simplest form of a negative feedback loop, is commonly believed to be employed by cellular systems to decrease the stochastic fluctuations in gene expression. When there is some delay in autoregulation, it is also believed that this system can generate oscillations. In eukaryotic cells, mRNAs that are synthesised in the nucleus must be exported to the cytoplasm to function in protein synthesis, whereas proteins must be transported into the nucleus from the cytoplasm to regulate the expression levels of genes. Nuclear transport thus plays a critical role in eukaryotic gene expression and regulation. Some recent studies have suggested that nuclear retention of mRNAs can control noise in mRNA expression. However, the effect of nuclear transport on protein noise and its interplay with negative feedback regulation is not completely understood. In this paper, we systematically compare four different simple models of gene expression. By using simulations and applying the linear noise approximation to the corresponding chemical master equations, we investigate the influence of nuclear import and export on noise in gene expression in a negative autoregulatory feedback loop. We first present results consistent with the literature, i.e., that negative feedback can effectively buffer the variability in protein levels, and nuclear retention can decrease mRNA noise levels. Interestingly we find that when negative feedback is combined with nuclear retention, an amplification in gene expression noise can be observed and is dependant on nuclear translocation rates. Finally, we investigate the effect of nuclear compartmentalisation on the ability of self-repressing genes to exhibit stochastic oscillatory dynamics.

Stochastic fluctuations in gene expression in aging hippocampal neurons could be exacerbated by traumatic brain injury.

Traumatic brain injury (TBI) is a risk factor for age-related dementia and development of neurodegenerative disorders such as Alzheimer's disease that are associated with cognitive decline. The exact mechanism for this risk is unknown but we hypothesized that TBI is exacerbating age-related changes in gene expression. Here, we present evidence in an animal model that experimental TBI increases age-related stochastic gene expression. We compared the variability in expression of several genes associated with cell survival or death, among three groups of laser capture microdissected hippocampal neurons from aging rat brains. TBI increased stochastic fluctuations in gene expression in both dying and surviving neurons compared to the naïve neurons. Increases in random, stochastic fluctuations in prosurvival or prodeath gene expression could potentially alter cell survival or cell death pathways in aging neurons after TBI which may lead to age-related cognitive decline.

Heterogeneities in Nanog Expression Drive Stable Commitment to Pluripotency in the Mouse Blastocyst.

The pluripotent epiblast (EPI) is the founder tissue of almost all somatic cells. EPI and primitive endoderm (PrE) progenitors arise from the inner cell mass (ICM) of the blastocyst-stage embryo. The EPI lineage isdistinctly identified by its expression of pluripotency-associated factors. Many of these factors have been reported to exhibit dynamic fluctuations of expression in embryonic stem cell cultures. Whether these fluctuations correlating with ICM fate choice occur invivo remains an open question. Using single-cell resolution quantitative imaging of a Nanog transcriptional reporter, we noted an irreversible commitment to EPI/PrE lineages invivo. A period of apoptosis occurred concomitantly with ICM cell-fate choice, followed by a burst of EPI-specific cell proliferation. Transitions were occasionally observed from PrE-to-EPI, but not vice versa, suggesting that they might be regulated and not stochastic. We propose that the rapid timescale of early mammalian embryonic development prevents fluctuations in cell fate.

MicroRNAs in B-cell lymphomas: how a complex biology gets more complex.

MicroRNAs (miRNAs) represent important regulators of gene expression besides transcriptional control. miRNA regulation can be involved in the cell developmental fate decisions, but can also have more subtle roles in buffering stochastic fluctuations in gene expression. They participate in pathways fundamental to B-cell development like B-cell receptor (BCR) signalling, B-cell migration/adhesion, cell-cell interactions in immune niches, and the production and class-switching of immunoglobulins. miRNAs influence B-cell maturation, generation of pre-, marginal zone, follicular, B1, plasma and memory B cells. In this review, we discuss miRNAs with essential functions in malignant B-cell development (such as miR-150, miR-155, miR-21, miR-34a, miR-17-92 and miR-15-16). We also put these miRNAs in the context of normal B-cell differentiation, as this is intimately connected to neoplastic B-cell development. We review miRNAs' role in the most common B-cell malignancies, including chronic lymphocytic leukaemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and mantle cell lymphoma (MCL). We focus on miR-contribution to the regulation of important signalling pathways (such as NF-κB, PI3K/AKT and TGF-β), BCR signalling and its modulators (such as PTEN, SHIP-1, ZAP-70, GAB1 and BTK), anti- and pro-apoptotic proteins (such as BCL2, MCL1, TCL1, BIM, p53 and SIRT1) and transcription factors (such as MYC, MYB, PU.1, FOXP1 and BCL6). We also discuss the association of miRNAs' expression levels with the patients' survival and response to therapy, summarizing their potential use as predictive and prognostic markers. Importantly, the targeting of miRNAs (like use of anti-miR-155 or miR-34a mimic) could provide a novel therapeutic approach as evidenced by tumour regression in xenograft mouse models and initial promising data from clinical trials.

A computational framework for analyzing stochasticity in gene expression.

Stochastic fluctuations in gene expression give rise to distributions of protein levels across cell populations. Despite a mounting number of theoretical models explaining stochasticity in protein expression, we lack a robust, efficient, assumption-free approach for inferring the molecular mechanisms that underlie the shape of protein distributions. Here we propose a method for inferring sets of biochemical rate constants that govern chromatin modification, transcription, translation, and RNA and protein degradation from stochasticity in protein expression. We asked whether the rates of these underlying processes can be estimated accurately from protein expression distributions, in the absence of any limiting assumptions. To do this, we (1) derived analytical solutions for the first four moments of the protein distribution, (2) found that these four moments completely capture the shape of protein distributions, and (3) developed an efficient algorithm for inferring gene expression rate constants from the moments of protein distributions. Using this algorithm we find that most protein distributions are consistent with a large number of different biochemical rate constant sets. Despite this degeneracy, the solution space of rate constants almost always informs on underlying mechanism. For example, we distinguish between regimes where transcriptional bursting occurs from regimes reflecting constitutive transcript production. Our method agrees with the current standard approach, and in the restrictive regime where the standard method operates, also identifies rate constants not previously obtainable. Even without making any assumptions we obtain estimates of individual biochemical rate constants, or meaningful ratios of rate constants, in 91% of tested cases. In some cases our method identified all of the underlying rate constants. The framework developed here will be a powerful tool for deducing the contributions of particular molecular mechanisms to specific patterns of gene expression.

Noise propagation through extracellular signaling leads to fluctuations in gene expression.

BackgroundCell-to-cell variability in mRNA and proteins has been observed in many biological systems, including the human innate immune response to viral infection. Most of these studies have focused on variability that arises from (a) intrinsic stochastic fluctuations in gene expression and (b) extrinsic sources (e.g. fluctuations in transcription factors). The main focus of our study is the effect of extracellular signaling on enhancing intrinsic stochastic fluctuations. As a new source of noise, the communication between cells with fluctuating numbers of components has received little attention. We use agent-based modeling to study this contribution to noise in a system of human dendritic cells responding to viral infection.ResultsOur results, validated by single-cell experiments, show that in the transient state cell-to-cell variability in an interferon-stimulated gene (DDX58) arises from the interplay between the spatial randomness of the cellular sources of the interferon and the temporal stochasticity of its own production. The numerical simulations give insight into the time scales on which autocrine and paracrine signaling act in a heterogeneous population of dendritic cells upon viral infection. We study the effect of different factors that influence the magnitude of the cell-to-cell-variability of the induced gene, including the cell density, multiplicity of infection, and the time scale over which the cellular sources begin producing the cytokine.ConclusionsWe propose a mechanism of noise propagation through extracellular communication and establish conditions under which the mechanism is operative. The cellular stochasticity of gene induction, which we investigate, is not limited to the specific interferon-induced gene we have studied; a broad distribution of copy numbers across cells is to be expected for other interferon-stimulated genes. This can lead to functional consequences for the system-level response to a viral challenge.

Creating memories of transcription

Two genetically identical cells grown under the same conditions can exhibit stochastic fluctuations in gene expression. Such cell-to-cell variation in expression of key genes is hypothesized to help drive developmental fate decisions and cell specialization during differentiation. However, to date, most analyses dissecting steps involved in the activation of cell cycle or developmentally regulated promoters have been conducted using cell population-based assays, thereby precluding a detailed appreciation of how transcription factors, cofactors, and chromatin remodelers contribute to the stochastic nature of transcriptional activation in individual cells. In PNAS, Zhang et al. (1) conduct an elegant series of single cell pedigree analyses to explore molecular mechanisms influencing stochastic gene expression from the cell cycle-regulated HO promoter that drives cell fate decisions in Saccharomyces cerevisiae. Through the use of an unstable GFP reporter to visualize transcription indirectly, Zhang et al. demonstrate that different factors have independent modes by which they can regulate the HO promoter. Some factors primarily influence the frequency of transcriptional events or the fraction of cell cycles in which transcription from HO occurs, whereas others affect the amplitude or the level of expression from the HO promoter achieved within a given cell cycle. Assessing HO promoter activation at the single cell level has also permitted the authors to uncover evidence for a role of histone acetylation in conferring short-term cis-based memory of transcriptional states.

Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis

Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Double-negative feedback loops as a common design motif in the transcriptional networks regulating cell fate

The gene networks that regulate the choice of cell fate in living organisms contain a set of ubiquitous design motifs. We discuss the role of one such motif, the double-negative feedback loop, in producing discrete and stable steady states during the development of mammalian cell lineages. Several examples of this motif are described from the literature. Thereafter, we use an ordinary differential equation-based computational model of the double-negative feedback loop motif to illustrate the mechanism by which it generates stable cellular states. Stochastic simulations show how this motif ensures robustness of cell states to stochastic fluctuations in gene expression.

Stochastic Modeling of B Lymphocyte Terminal Differentiation and Its Suppression by Dioxin

BackgroundUpon antigen encounter, naïve B lymphocytes differentiate into antibody-secreting plasma cells. This humoral immune response is suppressed by the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other dioxin-like compounds, which belong to the family of aryl hydrocarbon receptor (AhR) agonists.ResultsTo achieve a better understanding of the immunotoxicity of AhR agonists and their associated health risks, we have used computer simulations to study the behavior of the gene regulatory network underlying B cell terminal differentiation. The core of this network consists of two coupled double-negative feedback loops involving transcriptional repressors Bcl-6, Blimp-1, and Pax5. Bifurcation analysis indicates that the feedback network can constitute a bistable system with two mutually exclusive transcriptional profiles corresponding to naïve B cells and plasma cells. Although individual B cells switch to the plasma cell state in an all-or-none fashion when stimulated by the polyclonal activator lipopolysaccharide (LPS), stochastic fluctuations in gene expression make the switching event probabilistic, leading to heterogeneous differentiation response among individual B cells. Moreover, stochastic gene expression renders the dose-response behavior of a population of B cells substantially graded, a result that is consistent with experimental observations. The steepness of the dose response curve for the number of plasma cells formed vs. LPS dose, as evaluated by the apparent Hill coefficient, is found to be inversely correlated to the noise level in Blimp-1 gene expression. Simulations illustrate how, through AhR-mediated repression of the AP-1 protein, TCDD reduces the probability of LPS-stimulated B cell differentiation. Interestingly, stochastic simulations predict that TCDD may destabilize the plasma cell state, possibly leading to a reversal to the B cell phenotype.ConclusionOur results suggest that stochasticity in gene expression, which renders a graded response at the cell population level, may have been exploited by the immune system to launch humoral immune response of a magnitude appropriately tuned to the antigen dose. In addition to suppressing the initiation of the humoral immune response, dioxin-like compounds may also disrupt the maintenance of the acquired immunity.

Modelling the Molecular Interactions in the Flower Developmental Network of Arabidopsis thaliana

We present a dynamical model of the gene network controlling flower development in Arabidopsis thaliana. The network is centered at the regulation of the floral organ identity genes (AP1, AP2, AP3, PI and AG) and ends with the transcription factor complexes responsible for differentiation of floral organs. We built and simulated the regulatory interactions that determine organ specificity using an extension of hybrid Petri nets as implemented in Cell Illustrator. The network topology is characterized by two main features: (1) the presence of multiple autoregulatory feedback loops requiring the formation of protein complexes, and (2) the role of spatial regulators determining floral patterning. The resulting network shows biologically coherent expression patterns for the involved genes, and simulated mutants produce experimentally validated changes in organ expression patterns. The requirement of heteromeric higher-order protein complex formation for positive autoregulatory feedback loops attenuates stochastic fluctuations in gene expression, enabling robust organ-specific gene expression patterns. If autoregulation is mediated by monomers or homodimers of proteins, small variations in initial protein levels can lead to biased production of homeotic proteins, ultimately resulting in homeosis. We also suggest regulatory feedback loops involving miRNA loci by which homeotic genes control the activity of their spatial regulators.

Variability in Gene Expression Underlies Incomplete Penetrance in C. Elegans: Using Single Molecules To Study the Development of Single Cells

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical organ-isms in homogenous environments vary, suggesting that random processes may play an important role in generating phenotypic diversity. Few studies, have ex-plored the impact of stochastic fluctuations in gene expression on phenotypic variation and cell fate decisions in multicellular organisms. In order to examine the consequences of gene expression variability in development, we explored intestinal specification in C. elegans, in which wild-type cell fate is invariant and controlled by a small transcriptional network. In contrast, cell fates in embryos with mutant skn-1, the first gene expressed in this network, are variable: while most mutant embryos fail to develop intestinal cells, some embryos nevertheless produce intestinal precursors. By counting transcripts in individual embryos, we show that mutations in skn-1 result in large variability in the expression of the downstream gene end-1, arising partly from misregulation of chromatin remodel-ing. end-1 expression is are subsequently thresholded during a critical time win-dow to produce an ON/OFF expression pattern of elt-2, the master regulator of intestinal differentiation. The loss of skn-1 activity eliminates redundancy in the network, making elt-2 activation particularly sensitive to variability in end-1 ex-pression. Although end-3 can also activate elt-2, deleting end-3 in wild-type ani-mals results in variability in levels and timing of elt-2 expression, suggesting that robust expression of the downstream target requires multiple transcriptional acti-vators and also hinting at subtle differences in the roles of putatively redundant elements in the network. Our results show that mutations in developmental net-works can expose otherwise buffered stochastic variability in gene expression, leading to pronounced phenotypic variation.

Role of cooperative binding on noise expression

The origin of stochastic fluctuations in gene expression has received considerable attention recently. Fluctuations in gene expression are particularly pronounced in cellular systems because of the small copy number of species undergoing transitions between discrete chemical states and the small size of biological compartments. In this paper, we propose a stochastic model for gene expression regulation including several binding sites, considering elementary reactions only. The model is used to investigate the role of cooperativity on the intrinsic fluctuations of gene expression by means of master-equation formalism. We found that the Hill coefficient and the level of noise increase as the interaction energy between activators increases. Additionally, we show that the model allows one to distinguish between two cooperative binding mechanisms.

Epigenetic Silencing of Human Immunodeficiency Virus (HIV) Transcription by Formation of Restrictive Chromatin Structures at the Viral Long Terminal Repeat Drives the Progressive Entry of HIV into Latency

The molecular mechanisms utilized by human immunodeficiency virus (HIV) to enter latency are poorly understood. Following the infection of Jurkat T cells with lentiviral vectors that express Tat in cis, gene expression is progressively silenced. Silencing is greatly enhanced when the lentiviral vectors carry an attenuated Tat gene with the H13L mutation. Individual clones of lentivirus-infected cells showed a wide range of shutdown rates, with the majority showing a 50% silencing frequency between 30 to 80 days. The silenced clones characteristically contained a small fraction (0 to 15%) of activated cells that continued to express d2EGFP. When d2EGFP(+) and d2EGFP(-) cell populations were isolated from the shutdown clones, they quickly reverted to the original distribution of inactive and active cells, suggesting that the d2EGFP(+) cells arise from stochastic fluctuations in gene expression. The detailed analysis of transcription initiation and elongation using chromatin immunoprecipitation (ChIP) assays confirms that Tat levels are restricted in the latently infected cells but gradually rise during proviral reactivation. ChIP assays using clones of latently infected cells demonstrate that the latent proviruses carry high levels of deacetylated histones and trimethylated histones. In contrast, the cellular genes IkappaB alpha and GAPDH had high levels of acetylated histones and no trimethylated histones. The levels of trimethylated histone H3 and HP1-alpha associated with HIV proviruses fell rapidly after tumor necrosis factor alpha activation. The progressive shutdown of HIV transcription following infection suggests that epigenetic mechanisms targeting chromatin structures selectively restrict HIV transcription initiation. This decreases Tat production below the levels that are required to sustain HIV gene expression.

Stochastic Gene Expression in a Lentiviral Positive-Feedback Loop: HIV-1 Tat Fluctuations Drive Phenotypic Diversity

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.