|
|
|
||
|
||||
|
|
|||
Biology is moving from studying small molecular systems in isolation towards the study of molecular systems in complex environments, potentially as large as a complete cell. The amount of information across scales that needs to be processed and integrated in order to understand or simulate such complexity will require a leap in the level of sophistication of the software tools that we develop and use. New environments are needed for combining accurate methods such as quantum-level calculations with multi-resolution, multi-scale methods for the simulation of very large and complex systems.
One of the challenges in designing the next generation of scientific software lies in the demand for easy-to-use tools that enable scientists to create novel visualizations and/or processing pipelines for their data without forcing them to become expert programmers. Over the past four years we have investigated the idea of developing software components that can be used to dynamically extend a high-level, interpreted language that always remains at the heart of any application built from these components. This new paradigm enables the rapid and seamless integration of a wide variety of computational methods while always retaining a full-fledge general-purpose scripting language available to interactively operate on the application itself as well as the data being analyzed or processed. This architecture offers new exciting possibilities for the design of the next generation of simulation software.
Figure 1: Vision network used to compute a molecular surface of Acetylcholine Esterase and map the electrostatic potential on the surface.
The NetworkEditor and Vision software components (Fig. 1) that we have developed under that architecture support the visual programming paradigm enabling the interactive creation of visual tools and the seamless and intuitive integration of data, computational techniques, and computational resources. Visual programming has been around for over two decades, however our implementation provides a number of important innovations including unprecedented levels of flexibility. Vision's unique features and its application-domain independence enables scientists to efficiently and effectively design and build discovery environments that are tailored for the growing computational needs, and that will integrate into network based computational and collaborative environments. This work is supported by the NCRR grant RR08605.
Please note that Vision is based on our former Visual Programming Environment 'ViPEr' which underwent many significant changes and finally became Vision.
You can get Vision here.
References:
Sanner M., Stoffler D., and Olson, A.J. (2002). ViPEr, a Visual Programming Environment for Python. In Proceedings of the Tenth International Python Conference 2002. ViPEr was awarded the first prize for the best project at this conference.
Stoffler D., Coon S.I., Huey R., Olson, A.J., and Sanner, M.F. (2003). Integrating biomolecular analysis and visual programming: flexibility and interactivity in the design of bioinformatics tools. In Proceedings of HICSS-36, Hawaii International conference on system sciences, 2003, Hawaii.
Homepage Daniel Stoffler: Home | Personal
| Projects | Publications
| Links