Enabling computationally intensive bioinformatics applications on the Grid platform

Bioinformatics is a recent and emerging discipline which aims at studying biological problems through computational approaches. Most branches of bioinformatics such as Genomics, Proteomics and Molecular Dynamics are particularly computationally intensive, requiring huge amount of computational resources for running algorithms of everincreasing complexity over data of everincreasing size. In the search for computational power, the EGEE Grid platform, world's largest community of interconnected clusters load balanced as a whole, seems particularly promising and is considered the new hope for satisfying the everincreasing computational requirements of bioinformatics, as well as physics and other computational sciences. The EGEE platform, however, is rather new and not yet free of problems. In addition, specific requirements of bioinformatics need to be addressed in order to use this new platform effectively for bioinformatics tasks. In my three years' Ph.D. work I addressed numerous aspects of this Grid platform, with particular attention to those needed by the bioinformatics domain. I hence created three major frameworks, Vnas, GridDBManager and SETest, plus an additional smaller standalone solution, to enhance the support for bioinformatics applications in the Grid environment and to reduce the effort needed to create new applications, additionally addressing numerous existing Grid issues and performing a series of optimizations. The Vnas framework is an advanced system for the submission and monitoring of Grid jobs that provides an abstraction with reliability over the Grid platform. In addition, Vnas greatly simplifies the development of new Grid applications by providing a callback system to simplify the creation of arbitrarily complex multistage computational pipelines and provides an abstracted virtual sandbox which bypasses Grid limitations. Vnas also reduces the usage of Grid bandwidth and storage resources by transparently detecting equality of virtual sandbox files based on content, across different submissions, even when performed by different users. BGBlast, evolution of the earlier project GridBlast, now provides a Grid Database Manager (GridDBManager) component for managing and automatically updating biological flatfile databases in the Grid environment. GridDBManager sports very novel features such as an adaptive replication algorithm that constantly optimizes the number of replicas of the managed databases in the Grid environment, balancing between response times (performances) and storage costs according to a programmed cost formula. GridDBManager also provides a very optimized automated management for older versions of the databases based on reverse delta files, which reduces the storage costs required to keep such older versions available in the Grid environment by two orders of magnitude. The SETest framework provides a way to the user to test and regressiontest Python applications completely scattered with side effects (this is a common case with Grid computational pipelines), which could not easily be tested using the more standard methods of unit testing or test cases. The technique is based on a new concept of datasets containing invocations and results of filtered calls. The framework hence significantly accelerates the development of new applications and computational pipelines for the Grid environment, and the efforts required for maintenance. An analysis of the impact of these solutions will be provided in this thesis. This Ph.D. work originated various publications in journals and conference proceedings as reported in the Appendix. Also, I orally presented my work at numerous international conferences related to Grid and bioinformatics.

Silk Fibroin: a biopolymer platform for innovative pharmaceutical formulation and biomedical devices.

The protein silk fibroin (SF) from the silkworm Bombyx mori is a FDA-approved biomaterial used over centuries as sutures wire. Importantly, several evidences highlighted the potential of silk biomaterials obtained by using so-called regenerated silk fibroin (RSF) in biomedicine, tissue engineering and drug delivery. Indeed, by a water-based protocol, it is possible to obtain protein water-solution, by extraction and purification of fibroin from silk fibres. Notably, RSF can be processed in a variety of biomaterials forms used in biomedical and technological fields, displaying remarkable properties such as biocompatibility, controllable biodegradability, optical transparency, mechanical robustness. Moreover, RSF biomaterials can be doped and/or chemical functionalized with drugs, optically active molecules, growth factors and/or chemicals In this view, activities of my PhD research program were focused to standardize the process of extraction and purification of protein to get the best physical and chemical characteristics. The analysis of the chemo-physical properties of the fibroin involved both the RSF water-solution and the protein processed in film. Chemo-physical properties have been studied through: vibrational (FT-IR and Raman-FT) and optical (absorption and emission UV-VIS) spectroscopy, nuclear magnetic resonance (1H and 13C NMR), thermal analysis and thermo-gravimetric scan (DSC and TGA). In the last year of my PhD, activities were focused to study and define innovative methods of functionalization of the silk fibroin solution and films. Indeed, research program was the application of different methods of manufacturing approaches of the films of fibroin without the use of harsh treatments and organic solvents. New approaches to doping and chemical functionalization of the silk fibroin were studied. Two different methods have been identified: 1) biodoping that consists in the doping of fibroin with optically active molecules through the addition of fluorescent molecules in the standard diet used for the breeding of silkworms; 2) chemical functionalization via silylation.