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.

Upgrading Bio-Platform Molecules in the Gas-Phase: From Levulinic Acid to Bio-Chemicals

Levulinic acid (LA) is a polyfunctional molecule obtained from biomass. Because of its structure, the United States Department of energy classified LA as one of the top 12 building block chemicals. Typically, it is valorized through chemical reduction to γ-valerolactone (GVL). It is usually done with H2 in batch systems with high H2 pressures and noble metal catalysts, making it expensive and less applicable. Therefore, alternative approaches such as catalytic transfer hydrogenation (CTH) through the Meerwein–Ponndorf–Verley (MPV) reaction over heterogeneous catalysts have been studied. This uses organic molecules (alcohols) which act as a hydride transfer agent (H-donor), to reduce molecules containing carbonyl groups. Given the stability of the intermediate, reports have shown the batch liquid-phase CTH of levulinate esters with secondary alcohols, and remarkable results (GVL yield) have been obtained over ZrO2, given the need of a Lewis acid (LASites) and base pair for CTH. However, there were no reports of the continuous gas-phase CTH of levulinate esters. Therefore, high surface area ZrO2 was tested for gas-phase CTH of methyl levulinate (ML) using ethanol, methanol and isopropanol as H-donors. Under optimized conditions with ethanol (250 ℃), the reaction is selective towards GVL (yield 70%). However, heavy carbonaceous materials over the catalyst surface progressively blocked LASites changing the chemoselectivity. The in situ regeneration of the catalyst permitted a partial recovery of the LASites and an almost total recovery of the initial catalytic behavior, proving the deactivation reversible. Tests with methanol were not promising (ML conversion 35%, GVL yield 4%). As expected, using isopropanol provided complete conversion and a GVL yield of 80%. The reaction was also tested using bioethanol derived from agricultural waste. In addition, a preliminary study was performed for the hydrogenolysis of polyols to produce bioethanol, were Pd-Fe catalyst promoted the ethanol selective (37%) hydrogenolysis of glycerol.

Development of a numerical platform for the modeling and optimal control of liquid metal flows

The main purpose of this work is to develop a numerical platform for the turbulence modeling and optimal control of liquid metal flows. Thanks to their interesting thermal properties, liquid metals are widely studied as coolants for heat transfer applications in the nuclear context. However, due to their low Prandtl numbers, the standard turbulence models commonly used for coolants as air or water are inadequate. Advanced turbulence models able to capture the anisotropy in the flow and heat transfer are then necessary. In this thesis, a new anisotropic four-parameter turbulence model is presented and validated. The proposed model is based on explicit algebraic models and solves four additional transport equations for dynamical and thermal turbulent variables. For the validation of the model, several flow configurations are considered for different Reynolds and Prandtl numbers, namely fully developed flows in a plane channel and cylindrical pipe, and forced and mixed convection in a backward-facing step geometry. Since buoyancy effects cannot be neglected in liquid metals-cooled fast reactors, the second aim of this work is to provide mathematical and numerical tools for the simulation and optimization of liquid metals in mixed and natural convection. Optimal control problems for turbulent buoyant flows are studied and analyzed with the Lagrange multipliers method. Numerical algorithms for optimal control problems are integrated into the numerical platform and several simulations are performed to show the robustness, consistency, and feasibility of the method.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.

Hybrid ground-aerial mesh networks for IoT monitoring applications: network design and software platform development

The Internet of Things (IoT) has grown rapidly in recent years, leading to an increased need for efficient and secure communication between connected devices. Wireless Sensor Networks (WSNs) are composed of small, low-power devices that are capable of sensing and exchanging data, and are often used in IoT applications. In addition, Mesh WSNs involve intermediate nodes forwarding data to ensure more robust communication. The integration of Unmanned Aerial Vehicles (UAVs) in Mesh WSNs has emerged as a promising solution for increasing the effectiveness of data collection, as UAVs can act as mobile relays, providing extended communication range and reducing energy consumption. However, the integration of UAVs and Mesh WSNs still poses new challenges, such as the design of efficient control and communication strategies. This thesis explores the networking capabilities of WSNs and investigates how the integration of UAVs can enhance their performance. The research focuses on three main objectives: (1) Ground Wireless Mesh Sensor Networks, (2) Aerial Wireless Mesh Sensor Networks, and (3) Ground/Aerial WMSN integration. For the first objective, we investigate the use of the Bluetooth Mesh standard for IoT monitoring in different environments. The second objective focuses on deploying aerial nodes to maximize data collection effectiveness and QoS of UAV-to-UAV links while maintaining the aerial mesh connectivity. The third objective investigates hybrid WMSN scenarios with air-to-ground communication links. One of the main contribution of the thesis consists in the design and implementation of a software framework called "Uhura", which enables the creation of Hybrid Wireless Mesh Sensor Networks and abstracts and handles multiple M2M communication stacks on both ground and aerial links. The operations of Uhura have been validated through simulations and small-scale testbeds involving ground and aerial devices.