Chris D. Cox

Transcriptional burst frequency and burst size are equally modulated across the human genome

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.

Frequency domain analysis of noise in autoregulated gene circuits

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.

The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior

A key to advancing the understanding of molecular biology in the post-genomic age is the development of accurate predictive models for genetic regulation, protein interaction, metabolism, and other biochemical processes. To facilitate model development, simulation algorithms must provide an accurate representation of the system, while performing the simulation in a reasonable amount of time. Gillespies stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous models with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, we examine the performance of different versions of the SSA when applied to several biochemical models. Through our analysis, we discover that transient changes in reaction execution frequencies, which are typical of biochemical models with gene induction and repression, can dramatically affect simulator performance. To account for these shifts, we propose a new algorithm called the sorting direct method that maintains a loosely sorted order of the reactions as the simulation executes. Our measurements show that the sorting direct method performs favorably when compared to other well-known exact stochastic simulation algorithms.

Micellar solubilization of polynuclear aromatic hydrocarbons in coal tar-contaminated soils

Solubilization of PAHs from a coal tar-contaminated soil obtained from a manufactured gas plant (MGP) site was evaluated using nonionic polyoxyethylene surfactants at dosages greater than cmc. Up to 25% of Soxhlet-extractable PAHs could be solubilized at surfactant loadings of 0.3 g/g of oil in 16 days in completely stirred batch reactors. Longer periods were required to reach equilibrium at higher surfactant dosages. Raoult`s law satisfactorily described the partitioning of constituent PAHs between the weathered coal tar and the micellar solution. An equilibrium model was developed to predict the solubilization of PAHs from coal tar-contaminated soils for given properties of the soil, surfactant, and component PAHs. The model predicted solubilization of constituent PAHs reasonably well at low surfactant dosages. At extremely high surfactant dosages, the model failed to reliably predict solubilization. Presumably, mass transfer mass transfer limitations prevented the attainment of equilibrium during the duration (380h) of solubilization experiments. 25 refs., 6 figs., 4 tabs.

Transcriptional Bursting from the HIV-1 Promoter is a Significant Source of Stochastic Noise in HIV-1 Gene Expression

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-1s 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.

Surface complexation of methylated arsenates by hydrous oxides

Abstract The adsorption of methyl arsenate and dimethyl arsenate on hydrous ferric oxide and activated alumina was measured as a function of pH, ionic strength and sorbate-sorbent ratio by the bottle-point method and the characteristics of the two oxide surfaces were determined by surface titration. Adsorption decreased with increasing pH while it was only weakly dependent on ionic strength. The results were interpreted in terms of the triple-layer surface complexation model by assuming that arsenicals formed inner-sphere complexes with the oxide surface. A satisfactory interpretation of results could be made by modifying the triple-layer model to include surface sites of two different adsorption energies. Attempts to model adsorption considering a homogeneous surface were unsatisfactory.

Using noise to probe and characterize gene circuits

Stochastic fluctuations (or “noise”) in the single-cell populations of molecular species are shaped by the structure and biokinetic rates of the underlying gene circuit. The structure of the noise is summarized by its autocorrelation function. In this article, we introduce the noise regulatory vector as a generalized framework for making inferences concerning the structure and biokinetic rates of a gene circuit from its noise autocorrelation function. Although most previous studies have focused primarily on the magnitude component of the noise (given by the zero-lag autocorrelation function), our approach also considers the correlation component, which encodes additional information concerning the circuit. Theoretical analyses and simulations of various gene circuits show that the noise regulatory vector is characteristic of the composition of the circuit. Although a particular noise regulatory vector does not map uniquely to a single underlying circuit, it does suggest possible candidate circuits, while excluding others, thereby demonstrating the probative value of noise in gene circuit analysis.

Frequency domain analysis of noise in simple gene circuits

Recent advances in single cell methods have spurred progress in quantifying and analyzing stochastic fluctuations, or noise, in genetic networks. Many of these studies have focused on identifying the sources of noise and quantifying its magnitude, and at the same time, paying less attention to the frequency content of the noise. We have developed a frequency domain approach to extract the information contained in the frequency content of the noise. In this article we review our work in this area and extend it to explicitly consider sources of extrinsic and intrinsic noise. First we review applications of the frequency domain approach to several simple circuits, including a constitutively expressed gene, a gene regulated by transitions in its operator state, and a negatively autoregulated gene. We then review our recent experimental study, in which time-lapse microscopy was used to measure noise in the expression of green fluorescent protein in individual cells. The results demonstrate how changes in rate constants within the gene circuit are reflected in the spectral content of the noise in a manner consistent with the predictions derived through frequency domain analysis. The experimental results confirm our earlier theoretical prediction that negative autoregulation not only reduces the magnitude of the noise but shifts its content out to higher frequency. Finally, we develop a frequency domain model of gene expression that explicitly accounts for extrinsic noise at the transcriptional and translational levels. We apply the model to interpret a shift in the autocorrelation function of green fluorescent protein induced by perturbations of the translational process as a shift in the frequency spectrum of extrinsic noise and a decrease in its weighting relative to intrinsic noise.

Noise in biological circuits

Noise biology focuses on the sources, processing, and biological consequences of the inherent stochastic fluctuations in molecular transitions or interactions that control cellular behavior. These fluctuations are especially pronounced in small systems where the magnitudes of the fluctuations approach or exceed the mean value of the molecular population. Noise biology is an essential component of nanomedicine where the communication of information is across a boundary that separates small synthetic and biological systems that are bound by their size to reside in environments of large fluctuations. Here we review the fundamentals of the computational, analytical, and experimental approaches to noise biology. We review results that show that the competition between the benefits of low noise and those of low population has resulted in the evolution of genetic system architectures that produce an uneven distribution of stochasticity across the molecular components of cells and, in some cases, use noise to drive biological function. We review the exact and approximate approaches to gene circuit noise analysis and simulation, and review many of the key experimental results obtained using flow cytometry and time-lapse fluorescent microscopy. In addition, we consider the probative value of noise with a discussion of using measured noise properties to elucidate the structure and function of the underlying gene circuit. We conclude with a discussion of the frontiers of and significant future challenges for noise biology.

The measurement of toluene dioxygenase activity in biofilm culture of Pseudomonas putida F1

Toluene dioxygenase (Tod) enzyme activity can be measured by the conversion of indole to indigo. Indigo is measured spectrophotometrically at 600 nm. However, this method is inadequate to measure the whole-cell enzyme activity when interference by suspended biomass is present. Indoxyl is a highly fluorescent intermediate in the conversion of indole to indigo by Tod. A fluorescence-based assay was developed and applied to monitor Tod activity in whole cells of Pseudomonas putida F1 biofilm from a continuously operated biofilter. Suspended growth studies with pure cultures indicated that indoxyl, as measured by fluorescence, correlated with indigo production (r(2)=0.89) as measured by spectrophotometry. Whole-cell enzyme activity was followed during growth on a minimal medium containing toluene. The maximum normalized whole cell enzyme activity of 19+/-1.5x10(-4) mg indigo (mg protein)(-1) min(-1) was reached during early stationary phase. P. putida F1 cells from a biofilm grown on vapor phase toluene had a normalized whole-cell enzyme activity of 5.0+/-0.2x10(-4) mg indigo (mg protein)(-1) min(-1). The half-life of whole-cell enzyme activity was estimated to be between 5.5 and 8 h in both suspended and biofilm growth conditions.