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The Cassini Plasma Spectrometer (CAPS) will make comprehensive three-dimensional mass-resolved measurements of the full variety of plasma phenomena found in Saturn's magnetosphere. Our fundamental scientific goals are to understand the nature of saturnian plasmas primarily their sources of ionization, and the means by which they are accelerated, transported, and lost. In so doing the CAPS investigation will contribute to understanding Saturn's magnetosphere and its complex interactions with Titan, the icy satellites and rings, Saturn's ionosphere and aurora, and the solar wind. Our design approach meets these goals by emphasizing two complementary types of measurements: high-time resolution velocity distributions of electrons and all major ion species; and lower-time resolution, high-mass resolution spectra of all ion species. The CAPS instrument is made up of three sensors: the Electron Spectrometer (ELS), the Ion Beam Spectrometer (IBS), and the Ion Mass Spectrometer (IMS). The ELS measures the velocity distribution of electrons from 0.6 eV to 28,250 keV, a range that permits coverage of thermal electrons found at Titan and near the ring plane as well as more energetic trapped electrons and auroral particles. The IBS measures ion velocity distributions with very high angular and energy resolution from 1 eV to 49,800 keV. It is specially designed

MADE is implemented in Matlab and requires a mixed-integer linear program solver. Source code is freely available at http://www.bme.virginia.edu/csbl/downloads/.

BackgroundSeveral methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.ResultsWe present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae.ConclusionThe TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.