The specification of SBML Level 1 is freely available from http://www.sbml.org/

BioModels Database (), part of the international initiative BioModels.net, provides access to published, peer-reviewed, quantitative models of biochemical and cellular systems. Each model is carefully curated to verify that it corresponds to the reference publication and gives the proper numerical results. Curators also annotate the components of the models with terms from controlled vocabularies and links to other relevant data resources. This allows the users to search accurately for the models they need. The models can currently be retrieved in the SBML format, and import/export facilities are being developed to extend the spectrum of formats supported by the resource.

LibSBML 3 was released in August 2007. Source code, binaries and documentation are freely available under LGPL open-source terms from http://sbml.org/software/libsbml.

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

SBMLToolbox is freely available from http://sbml.org/software/sbmltoolbox

Limitations on interplanetary communications create operations latencies and slow progress in planetary surface missions, with particular challenges to narrow-field-of-view science instruments requiring precise targeting. The AEGIS (Autonomous Exploration for Gathering Increased Science) autonomous targeting system has been in routine use on NASA's Curiosity Mars rover since May 2016, selecting targets for the ChemCam remote geochemical spectrometer instrument. AEGIS operates in two modes; in autonomous target selection, it identifies geological targets in images from the rover's navigation cameras, choosing for itself targets that match the parameters specified by mission scientists the most, and immediately measures them with ChemCam, without Earth in the loop. In autonomous pointing refinement, the system corrects small pointing errors on the order of a few milliradians in observations targeted by operators on Earth, allowing very small features to be observed reliably on the first attempt. AEGIS consistently recognizes and selects the geological materials requested of it, parsing and interpreting geological scenes in tens to hundreds of seconds with very limited computing resources. Performance in autonomously selecting the most desired target material over the last 2.5 kilometers of driving into previously unexplored terrain exceeds 93% (where ~24% is expected without intelligent targeting), and all observations resulted in a successful geochemical observation. The system has substantially reduced lost time on the mission and markedly increased the pace of data collection with ChemCam. AEGIS autonomy has rapidly been adopted as an exploration tool by the mission scientists and has influenced their strategy for exploring the rover's environment. ChemCam ChemCam (short for "chemistry and camera") is a mast-mounted, targetable, remote-sensing instrument aboard the Curiosity rover (19, 20).

The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

The Onboard Autonomous Science Investigation System has been developed to enable a rover to identify and react to serendipitous science opportunities. Using the FIDO rover in the Mars Yard at JPL, we have successfully demonstrated a fully autonomous opportunistic science system. The closed loop system tests included the rover acquiring image data, finding rocks in the image, analyzing rock properties and identifying rocks that merit further investigation. When the system on the rover alerts the rover to take additional measurements of interesting rocks, the planning and scheduling component determines if there are enough resources to meet this additional science data request. The rover is then instructed to either turn toward the rock, or to actually move closer to the rock to take an additional, close-up image. Prototype dust devil and cloud detection algorithms were delivered to an infusion task which refined the algorithms specifically for Mars Exploration Rovers ͑MER͒. These algorithms have been integrated into the MER flight software and were recently uploaded to the rovers on Mars.