James M. McCollum

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.

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.

Hardware acceleration of pseudo-random number generation for simulation applications

In modeling and simulation tools, random numbers from a variety of probability distribution functions are generated to simulate the behavior of random events. Inefficient generation of these numbers can be a significant bottleneck for simulation applications. Generating these random numbers imprecisely can skew results. An efficient and scalable fixed-point method for generating random numbers for any probability distribution function in a Field Programmable Gate Array (FPGA) is developed. A Pi estimator, a Monte Carlo integrator, and a stochastic simulator for chemical species are developed in software. Estimates are made regarding their potential to be accelerated using the designed FPGA. Results are presented which examine trade-offs between the number of gates used by the FPGA and the accuracy of the random numbers generated. The work shows that generating random numbers using the designed hardware can significantly increase the performance of simulation applications that require many random numbers.

A constraint satisfaction algorithm for microcontroller selection and pin assignment

In prototyping and design of embedded systems, selection of the smallest possible microcontroller necessary to meet system requirements while minimizing power consumption and design size is desirable. With the myriad of microcontrollers available on the market, selecting the ideal microcontroller is a non-trivial task. In this work, we address this problem through the implementation of a constraint satisfaction algorithm that will assist in microcontroller selection by matching design requirements to the capabilities of individual microcontroller pins.

Using the iPhone and iPod Touch for remote sensor control and data acquisition

Apples iPod Touch1 is a powerful pocket computing platform, allowing users to play graphics-intensive games, listen to music, browse the World Wide Web, and communicate using email through a Wi-Fi internet connection. Apples iPhone extends the iPod Touch by providing telephone service and high-speed 3G internet support when a Wi-Fi connection is unavailable. In this work, we investigate utilizing the iPhone and iPod Touch platforms for remote sensor control and data collection. Our example design interfaces a set of switches, a set of light emitting diodes, and a temperature sensor to the iPod Touch through a micro-controller and allows these devices to be controlled and monitored by a remote computer. Through this work, we show that the iPod Touch and iPhone can provide a low-cost, high-performance, and lightweight platform for remote data collection and control.

Accelerating Gene Regulatory Network Modeling Using Grid-Based Simulation

Modeling gene regulatory networks has, in some cases, enabled biologists to predict cellular behavior long before such behavior can be experimentally validated. Unfortunately, the extent to which biologists can take advantage of these modeling techniques is limited by the computational complexity of gene regulatory network simulation algorithms. This study presents a new platform-independent, grid-based distributed computing environment that accelerates biological model simulation and, ultimately, development. Applying this environment to gene regulatory network simulation shows a significant reduction in execution time versus running simulation jobs locally. To analyze this improvement, a performance model of the distributed computing environment is built. Although this grid-based system was specifically developed for biological simulation, the techniques discussed are applicable to a variety of simulation performance problems.

Snapshot capture from live high definition video stream for transmission over low-bandwidth data link

This paper will describe a method for providing a useful intelligence down a low-bandwidth output stream from a high-bandwidth input stream. Many intelligence systems take advantage of streaming video and utilize high-bandwidth data links to supply information. The issue is that high-bandwidth systems typically require larger equipment, better line-of-sight, and more complex gear to keep a reliable link. The intended platform for the system described in this paper is an unmanned aerial vehicle, so the limitations of high-bandwidth equipment become more significant in a tactical scenario. By providing snapshots from the high-definition (HD) video stream, the system is able to send data to users that may not have the capability, due to a myriad of reasons, to acquire a full HD video stream. The system was developed using a Gumstix Verdex 600Mhz computer and the frame acquisition algorithm is written in C. Testing was done using HD video files streamed by VideoLan Client (VLC). The system is capable of outputting JPEG images at a maximum interval of 30 seconds. The capture system provides a method of reaching out to more users and providing functional data.

Gene network shaping of inherent noise spectra

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