SBMLToolbox: an SBML toolbox for MATLAB users

We present SBMLToolbox, a toolbox that facilitates importing and exporting models represented in the Systems Biology Markup Language (SBML) in and out of the MATLAB environment and provides functionality that enables an experienced user of either SBML or MATLAB to combine the computing power of MATLAB with the portability and exchangeability of an SBML model. SBMLToolbox supports all levels and versions of SBML

Autonomously Calibrating a Quadrupole Mass Spectrometer

A computer program autonomously manages the calibration of a quadrupole ion mass spectrometer intended for use in monitoring concentrations and changes in concentrations of organic chemicals in the cabin air of the International Space Station. The instrument parameters calibrated include the voltage on a channel electron multiplier, a discriminator threshold, and an ionizer current. Calibration is achieved by analyzing the mass spectrum obtained while sweeping the parameter ranges in a heuristic procedure, developed by mass spectrometer experts, that involves detection of changes in signal trends that humans can easily recognize but cannot necessarily be straightforwardly codified in an algorithm. The procedure includes calculation of signal-to-noise ratios, signal-increase rates, and background-noise-increase rates; finding signal peaks; and identifying peak patterns. The software provides for several recovery-from-error scenarios and error-handling schemes. The software detects trace amounts of contaminant gases in the mass spectrometer and notifies associated command- and-data-handling software to schedule a cleaning. Furthermore, the software autonomously analyzes the mass spectrum to determine whether the parameters of a radio-frequency ramp waveform are set properly so that the peaks of the mass spectrum are at expected locations

Mobile Thread Task Manager

The Mobile Thread Task Manager (MTTM) is being applied to parallelizing existing flight software to understand the benefits and to develop new techniques and architectural concepts for adapting software to multicore architectures. It allocates and load-balances tasks for a group of threads that migrate across processors to improve cache performance. In order to balance-load across threads, the MTTM augments a basic map-reduce strategy to draw jobs from a global queue. In a multicore processor, memory may be "homed" to the cache of a specific processor and must be accessed from that processor. The MTTB architecture wraps access to data with thread management to move threads to the home processor for that data so that the computation follows the data in an attempt to avoid L2 cache misses. Cache homing is also handled by a memory manager that translates identifiers to processor IDs where the data will be homed (according to rules defined by the user). The user can also specify the number of threads and processors separately, which is important for tuning performance for different patterns of computation and memory access. MTTM efficiently processes tasks in parallel on a multiprocessor computer. It also provides an interface to make it easier to adapt existing software to a multiprocessor environment

LibSBML: an API library for SBML

LibSBML is an application programming interface library for reading, writing, manipulating and validating content expressed in the Systems Biology Markup Language (SBML) format. It is written in ISO C and C++, provides language bindings for Common Lisp, Java, Python, Perl, MATLAB and Octave, and includes many features that facilitate adoption and use of both SBML and the library. Developers can embed libSBML in their applications, saving themselves the work of implementing their own SBML parsing, manipulation and validation software

Injecting Artificial Memory Errors Into a Running Computer Program

Single-event upsets (SEUs) or bitflips are computer memory errors caused by radiation. BITFLIPS (Basic Instrumentation Tool for Fault Localized Injection of Probabilistic SEUs) is a computer program that deliberately injects SEUs into another computer program, while the latter is running, for the purpose of evaluating the fault tolerance of that program. BITFLIPS was written as a plug-in extension of the open-source Valgrind debugging and profiling software. BITFLIPS can inject SEUs into any program that can be run on the Linux operating system, without needing to modify the program s source code. Further, if access to the original program source code is available, BITFLIPS offers fine-grained control over exactly when and which areas of memory (as specified via program variables) will be subjected to SEUs. The rate of injection of SEUs is controlled by specifying either a fault probability or a fault rate based on memory size and radiation exposure time, in units of SEUs per byte per second. BITFLIPS can also log each SEU that it injects and, if program source code is available, report the magnitude of effect of the SEU on a floating-point value or other program variable

Major Constituents Analysis for the Vehicle Cabin Atmosphere Monitor

Vehicle Cabin Atmosphere Monitor (VCAM) can provide a means for monitoring the air within enclosed environments such as the International Space Station, the Crew Exploration Vehicle (CEV), a Lunar habitat, or another vehicle traveling to Mars. The software processes a sum total spectra (counts vs. mass channel) with the intention of computing abundance ratios for N2, O2, CO2, Ar2, and H2O. A brute-force powerset expansion compares a library of expected mass lines with those found within the data. Least squares error is combined with a penalty term for using small peaks

Histogrammatic Method for Determining Relative Abundance of Input Gas Pulse

To satisfy the Major Constituents Analysis (MCA) requirements for the Vehicle Cabin Atmosphere Monitor (VCAM), this software analyzes the relative abundance ratios for N2, O2, Ar, and CO2 as a function of time and constructs their best-estimate mean. A histogram is first built of all abundance ratios for each of the species vs time. The abundance peaks corresponding to the intended measurement and any obfuscating background are then separated via standard peak-finding techniques in histogram space. A voting scheme is then used to include/exclude this particular time sample in the final average based on its membership to the intended measurement or the background population. This results in a robust and reasonable estimate of the abundance of trace components such as CO2 and Ar even in the presence of obfuscating backgrounds internal to the VCAM device. VCAM can provide a means for monitoring the air within the enclosed environments, such as the ISS (International Space Station), Crew Exploration Vehicle (CEV), a Lunar Habitat, or another vehicle traveling to Mars. Its miniature pre-concentrator, gas chromatograph (GC), and mass spectrometer can provide unbiased detection of a large number of organic species as well as MCA analysis. VCAM s software can identify the concentration of trace chemicals and whether the chemicals are on a targeted list of hazardous compounds. This innovation s performance and reliability on orbit, along with the ground team s assessment of its raw data and analysis results, will validate its technology for future use and development

A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data

Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein–DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein–DNA interaction data to identify G(1) regulatory modules