Modeling and designing a proteomics application on PROTEUS

Objectives: Biomedical applications, such as analysis and management of mass spectrometry proteomics experiments, involve heterogeneous platforms and knowledge, massive data sets, and complex algorithms. Main requirements of such applications are semantic modeling of the experiments and data analysis, as well as high performance computational platforms. In this paper we propose a software platform allowing to model and execute biomedical applications on the Grid. Methods: Computational Grids offer the required computational power, whereas ontologies and workflow help to face the heterogeneity of biomedical applications. In this paper we propose the use of domain ontologies and workflow techniques for modeling biomedical applications, whereas Grid middleware is responsible for high performance execution. As a case study, the modeling of a proteomics experiment is discussed. Results: The main result is the design and first use of PROTEUS, a Grid-based problem-solving environment for biomedical and bioinformatics applications. Conclusion: To manage the complexity of biomedical experiments, ontologies help to model applications and to identify appropriate data and algorithms, workflow techniques allow to combine the elements of such applications in a systematic way. Finally, translation of workflow into execution plans allows the exploitation of the computational power of Grids. Along this direction, in this paper we present PROTEUS discussing a real case study in the proteomics domain.

Objectives. Biomedical applications, such as analysis and management of moss spectrometry proteomics experiments, involve heterogeneous platforms and knowledge, massive data sets, and complex algorithms. Main requirements of such applications are semantic modeling of the experiments and data analysis, as well as high performance computational platforms. In this paper we propose a software platform allowing to model and execute biomedical applications on the Grid. Methods: Computational Grids offer the required computational power, whereas ontologies and workflow help to face the heterogeneity of biomedical applications. In this paper we propose the use of domain ontologies and workflow techniques for modeling biomedical applications, whereas Grid middleware is responsible for high performance execution. As a case study, the modeling of a proteomics experiment is discussed. Results: The main result is the design and first use of PROTEUS, a Grid-based problem-solving environment for biomedical and bioinformatics applications. Conclusion: To manage the complexity of biomedical experiments, ontologies help to model applications and to identify appropriate data and algorithms, workflow techniques allow to combine the elements of such applications in a systematic way. Finally, translation of workflow into execution plans allows the exploitation of the computational power of Grids. Along this direction, in this paper we present PROTEUS discussing a real case study in the proteomics domain.