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Chris D Cox

Publications and source records attributed to Chris D Cox.

8 recordsLinked to original sources
PubMed2006

Direct quantification of N-(3-oxo-hexanoyl)-L-homoserine lactone in culture supernatant using a whole-cell bioreporter.

The autoinducer N-(3-oxo-hexanoyl)-L-homoserine lactone (3-oxo-C6-HSL) plays a significant role in the quorum-sensing system of the marine bacterium Vibrio fischeri. Upon forming a transcriptional activation complex with LuxR, 3-oxo-C6-HSL induces transcription of the luxICDABEG operon, leading to the increased production of both the 3-oxo-C6-HSL synthase (LuxI) and the bioluminescent proteins. In order to quantitatively analyze this regulatory mechanism, a novel approach was developed to measure 3-oxo-C6-HSL concentrations in V. fischeri cell culture supernatant. A bioluminescent strain of Escherichia coli that responds to 3-oxo-C6-HSL was used as a bioreporter. Although a linear response of the bioreporter to exogenously added synthetic 3-oxo-C6-HSL was found over several orders of magnitude, we show that bioreporter performance was dramatically impacted by variations in the supernatants using samples from a V. fischeri LuxI- strain. However, when maintained in the same supernatant background, the normalized peak bioluminescence maintained a linear response to 3-oxo-C6-HSL concentrations. Therefore, a standard additions technique was developed in which a known concentration of 3-oxo-C6-HSL was added to supernatant samples from wild-type V. fischeri cultures, and the incremental increase of the normalized peak bioluminescence relative to the untreated sample was determined. The concentration of 3-oxo-C6-HSL in the supernatant of the unknown sample was then quantified from the slope of the response between the normalized bioluminescent peaks with and without the addition of 3-oxo-C6-HSL. Advantages of this method are that it is rapid, does not require concentration or extraction, uses a small sample volume (ca. 2 ml), and accounts for effects caused by the composition of the supernatant. Furthermore, the findings can be broadly applicable to other bioreporter systems involving variable background conditions.

4-Butyrolactone↗
PubMed2006

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. Gillespie's 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.

Algorithms↗
PubMed2006

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.

Animals↗
PubMed2004

Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation.

We present a frequency domain Langevin approach for stochastic analysis that remains valid for many important gene circuit elements even as molecular populations approach zero. We begin by considering the case of low-rate transcription and show that the previously reported shot noise representation is exact at all mRNA population levels for a constant transcription rate. Next, we consider transcriptional control through protein-DNA interactions at an operator site within the gene promoter region. This analysis results in expressions for the dynamics and noise behavior of this important gene sub-circuit, including the spectral density of the intrinsic operator noise and the processing of extrinsic noise by this transcriptional regulation system. This analysis shows that mRNA synthesis noise is composed of wideband shot noise and band-limited operator binding generated noise components. We find that the bandwidth of operator noise and its ultimate effect on total mRNA and protein noise is controlled by operator binding and unbinding dynamics. The most substantial impact of the operator noise is seen at transcription rates just above basal expression. This analysis captures the full behavior of this transcriptional regulation system, and points to potentially serious flaws in simplified mathematical relationships often used to model transcriptional regulation.

Gene Expression Regulation↗
PubMed2003

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.

Biophysical Phenomena↗
PubMed2003

Analysis of noise in quorum sensing.

Noise may play a pivotal role in gene circuit functionality, as demonstrated for the genetic switch in the bacterial phage lambda. Like the lambda switch, bacterial quorum sensing (QS) systems operate within a population and contain a bistable switching element, making it likely that noise plays a functional role in QS circuit operation. Therefore, a detailed analysis of the noise behavior of QS systems is needed. We have developed a set of tools generally applicable to the analysis of gene circuits, with an emphasis on investigations in the frequency domain (FD), that we apply here to the QS system in the marine bacterium Vibrio fischeri. We demonstrate that a tight coupling between exact stochastic simulation and FD analysis provides insights into the structure/function relationships in the QS circuit. Furthermore, we argue that a noise analysis is incomplete without consideration of the power spectral densities (PSDs) of the important molecular output signals. As an example we consider reversible reactions in the QS circuit, and show through analysis and exact stochastic simulation that these circuits make significant and dynamic modifications to the noise spectra. In particular, we demonstrate a "whitening" effect, which occurs as the noise is processed through these reversible reactions.

Calibration↗
PubMedDate not supplied

Statistical distributions of uncertainty and variability in activated sludge model parameters.

All models used in activated sludge design and analysis use parameters to characterize process performance. The values of these parameters are often assumed based on default values recommended in the literature, but to date, no quantitative estimates of the parameter uncertainties have been published. Similarly, little attention has been given to quantifying site-specific parameter variability, even though its occurrence has been observed several times in the literature. In this paper, universal uncertainty distributions of the model parameters from Activated Sludge Model No. 1 are developed from a database of parameter values reported in the literature using Bayesian statistics. Site-specific distributions of parameter variability were developed using the same techniques. All parameter distributions developed demonstrated that significant uncertainty and variability exist, which could lead to overdesign or plant failure if not considered during the design process.

Bayes Theorem↗