Gene expression occurs either as an episodic process, characterized by pulsatile bursts, or as a constitutive process, characterized by a Poisson-like accumulation of gene products. It is not clear which mode of gene expression (constitutive versus bursty) predominates across a genome or how transcriptional dynamics are influenced by genomic position and promoter sequence. Here, we use time-lapse fluorescence microscopy to analyze 8,000 individual human genomic loci and find that at virtually all loci, episodic bursting—as opposed to constitutive expression—is the predominant mode of expression. Quantitative analysis of the expression dynamics at these 8,000 loci indicates that both the frequency and size of the transcriptional bursts varies equally across the human genome, independent of promoter sequence. Strikingly, weaker expression loci modulate burst frequency to increase activity, whereas stronger expression loci modulate burst size to increase activity. Transcriptional activators such as trichostatin A (TSA) and tumor necrosis factor α (TNF) only modulate burst size and frequency along a constrained trend line governed by the promoter. In summary, transcriptional bursting dominates across the human genome, both burst frequency and burst size vary by chromosomal location, and transcriptional activators alter burst frequency and burst size, depending on the expression level of the locus.

Analysis of noise in gene expression has proven a powerful approach for analyzing gene regulatory architecture. To probe the regulatory mechanisms controlling expression of HIV-1, we analyze noise in gene-expression from HIV-1's long terminal repeat (LTR) promoter at different HIV-1 integration sites across the human genome. Flow cytometry analysis of GFP expression from the HIV-1 LTR shows high variability (noise) at each integration site. Notably, the measured noise levels are inconsistent with constitutive gene expression models. Instead, quantification of expression noise indicates that HIV-1 gene expression occurs through randomly timed bursts of activity from the LTR and that each burst generates an average of 2-10 mRNA transcripts before the promoter returns to an inactive state. These data indicate that transcriptional bursting can generate high variability in HIV-1 early gene products, which may critically influence the viral fate-decision between active replication and proviral latency.

Recent work demonstrates that stochastic fluctuations in molecular populations have consequences for gene regulation. Previous experiments focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, in theoretical analysis, we showed that noise frequency content is determined by the underlying gene circuits, leading to a mapping between gene circuit structure and the noise frequency range. An intriguing prediction from our previous studies was that negative autoregulation shifts noise to higher frequencies where it is more easily filtered out by gene networks--a property that may contribute to the prevalence of autoregulation motifs (for example, found in the regulation of approximately 40% of Escherichia coli genes). Here we measure noise frequency content in growing cultures of E. coli, and verify the link between gene circuit structure and noise spectra by demonstrating the negative autoregulation-mediated spectral shift. We further demonstrate that noise spectral measurements provide mechanistic insights into gene regulation, as perturbations of gene circuit parameters are discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate the discovery of novel regulatory relationships.

We describe a frequency domain technique for the analysis of intrinsic noise within negatively autoregulated gene circuits. This approach is based on the transfer function around the feedback loop (loop transmission) and the equivalent noise bandwidth of the system. The loop transmission, T, is shown to be a determining factor of the dynamics and the noise behavior of autoregulated gene circuits, and this T-based technique provides a simple and flexible method for the analysis of noise arising from any source within the gene circuit. We show that negative feedback not only reduces the variance of the noise in the protein concentration, but also shifts this noise to higher frequencies where it may have a negligible effect on the noise behavior of following gene circuits within a cascade. This predicted effect is demonstrated through the exact stochastic simulation of a two-gene cascade. The analysis elucidates important aspects of gene circuit structure that control functionality, and may provide some insights into selective pressures leading to this structure. The resulting analytical relationships have a simple form, making them especially useful as synthetic gene circuit design equations. With the exception of the linearization of Hill kinetics, this technique is general and may be applied to the analysis or design of networks of higher complexity. This utility is demonstrated through the exact stochastic simulation of an autoregulated two-gene cascade operating near instability.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Recent work demonstrates that stochastic fluctuations in molecular populations have gene regulation consequences. Previous experiments focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, in theoretical analysis we showed that noise frequency content is determined by the underlying gene circuits, leading to a mapping between gene circuit structure and the noise frequency range. An intriguing prediction was that negative autoregulation shifts noise to higher frequencies where it is more easily filtered out by gene networks, a property that may contribute to the prevalence (e.g. found in regulation of ~ 40% of E. coli genes) of autoregulation motifs. Here we measure noise frequency content in growing cultures of E. coli and verify the link between gene circuit structure and noise spectra by demonstrating the negative autoregulation-mediated spectral shift. We further demonstrate that noise spectral measurements provide mechanistic insights into gene regulation as perturbations of gene circuit parameters are discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate the discovery of novel regulatory relationships.

The effect of nonionic polyoxyethylene (POE) surfactants on the solubilization rate of individual polycyclic aromatic hydrocarbons (PAHs) from a weathered, coal tar-contaminated soil obtained from a manufactured gas plant (MGP) site was studied. The release of PAHs upon surfactant washing were measured with time. Time-varying adsorption of PAHs to Tenax resin was also studied. Because of its large sorption coefficients for organic solutes, Tenax maintained near-zero concentrations of aqueous PAHs, thereby maximizing transfer of PAHs at the soil-water interface. The release of PAHs from the MGP soil exhibited a nonequilibrium behavior, typical of such soils. Surfactants significantly enhanced the rate of PAH solubilization, the amount solubilized being dependent upon the structure of the surfactant. It is postulated that the enhancement of PAH release by surfactants occurs by two mechanisms: (1) micellar solubilization by increased concentration gradient at the soil/tar-water interface and (2) sorption and penetration of surfactant molecules, causing intrasorbent swelling of the soil-tar matrix and increased matrix diffusivity of PAHs. A modified radial diffusion model was used to evaluate the relative importance of these mechanisms in surfactant washing of contaminated soils. It was concluded that surfactants enhanced PAH release from the test soil mainly by increasing matrix diffusivities, while increase in solubility by partitioning of PAHs into the micellar pseudophase played a secondary role.

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