OMICS: A Journal of Integrative Biology

We have developed a reduced model representing feedback loops of transcriptional regulation underlying circadian rhythms in Neurospora crassa. The model contains two delay differential equations that describe the dynamics of two core gene products, FRQ and WCC. In a negative feedback loop, FRQ protein represses frq transcription by binding the white-collar complex (WCC), which consists of the WC-1 and WC-2 proteins. In a positive feedback loop, WCC indirectly enhances its own formation. The model simulates circadian oscillations, light entrainment, and a phase-response curve (PRC) similar to experimental PRCs. The Neurospora model is virtually identical to a model describing Drosophila circadian rhythm generation, illustrating that rhythm generation in these divergent organisms shares important mechanistic elements. Significant dynamic differences were found when the parameter spaces of both models were explored to analyze changes in oscillations and bifurcations to steady states. Stochastic fluctuations in molecule numbers were simulated with the Gillespie algorithm. Circadian oscillations and entrainment to light were simulated with <80 molecules of FRQ and WCC present on average. Simulations suggest that in both Neurospora and Drosophila, only the negative feedback loop is essential for circadian oscillations. Similar models may aid understanding of circadian mechanisms in mammals and other organisms.

Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components—written in diverse programming languages and running on different platforms—to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.

One objective of systems biology is to create predictive, quantitative models of the transcriptional regulation networks that govern numerous cellular processes. Gene expression measurements, as provided by microarrays, are commonly used in studies that attempt to infer the regulation underlying these processes. At present, most gene expression models that have been derived from microarray data are based in discrete-time, which have limited applicability to common biological data sets, and may impede the integration of gene expression models with other models of biological processes that are formulated as ordinary differential equations (ODEs). To overcome these difficulties, a continuous-time approach for process identification to identify gene expression models based in ODEs was developed. The approach utilizes the modulating functions method of parameter identification. The method was applied to three simulated systems: (1) a linear gene expression model, (2) an autoregulatory gene expression model, and (3) simulated microarray data from a nonlinear transcriptional network. In general, the approach was well suited for identifying models of gene expression dynamics, capable of accurately identifying parameters for small numbers of data samples in the presence of modest experimental noise. Additionally, numerous insights about gene expression modeling were revealed by the case studies.

Circadian rhythms are endogenous rhythms with a cycle length of approximately 24 h. Rhythmic production of specific proteins within pacemaker structures is the basis for these physiological and behavioral rhythms. Prior work on mathematical modeling of molecular circadian oscillators has focused on the fruit fly, Drosophila melanogaster. Recently, great advances have been made in our understanding of the molecular basis of circadian rhythms in mammals. Mathematical models of the mammalian circadian oscillator are needed to piece together diverse data, predict experimental results, and help us understand the clock as a whole. Our objectives are to develop mathematical models of the mammalian circadian oscillator, generate and test predictions from these models, gather information on the parameters needed for model development, integrate the molecular model with an existing model of the influence of light and rhythmicity on human performance, and make models available in BioSpice so that they can be easily used by the general community. Two new mammalian models have been developed, and experimental data are summarized. These studies have the potential to lead to new strategies for resetting the circadian clock. Manipulations of the circadian clock can be used to optimize performance by promoting alertness and physiological synchronization.

We have used piglets as an animal model for studying the toxic effects of staphylococcal enterotoxins (SEs). Piglets are easy to handle, easy to carry out vital measurements, inexpensive, and more importantly, express remarkably similar pathological symptoms and responses to SE intoxication as humans at comparable doses. Microarray analyses are used to study the effect of many infections on gene expression profile in peripheral blood mononuclear cells. This high throughput application offers detailed depiction of alteration at the molecular levels. When using high throughput gene expression analysis, there is a high possibility of finding genes that vary normally in the tissues under study. It is necessary to verify genes that are normally differentially expressed between piglets. To evaluate the normal physiological variation in gene expression in vivo in piglets, we used cDNA microarray to measure gene expression levels in peripheral blood mononuclear cells from 10 normal Yorkshire piglets. We used analysis of variance to determine genes that showed statistically significant variations across piglets. Out of 1185 genes, 19 (1.6%) genes revealed statistically significant variance between RNA samples. Some of these varying genes are involved in stress response, immune response, and transcription. This study facilitates the characterization of gene expression base line needed for meaningful interpretation of microarray data.

The goal of the BioSPICE program is to create a framework that provides biologists access to the most current computational tools. At the program midpoint, the BioSPICE member community has produced a software system that comprises contributions from approximately 20 participating laboratories integrated under the BioSPICE Dashboard and a methodology for continued software integration. These contributed software modules are the BioSPICE Dashboard, a graphical environment that combines Open Agent Architecture and NetBeans software technologies in a coherent, biologist-friendly user interface. The current Dashboard permits data sources, models, simulation engines, and output displays provided by different investigators and running on different machines to work together across a distributed, heterogeneous network. Among several other features, the Dashboard enables users to create graphical workflows by configuring and connecting available BioSPICE components. Anticipated future enhancements to BioSPICE include a notebook capability that will permit researchers to browse and compile data to support model building, a biological model repository, and tools to support the development, control, and data reduction of wet-lab experiments. In addition to the BioSPICE software products, a project website supports information exchange and community building.