ABACUS. Many technologically and societally important mathematical problems are essentially intractable for traditional, serial computers. Therefore, a significant need exists for parallel computing approaches that would be able to solve such problems faster than current serial computers. This project will develop and benchmark a novel paradigm for future parallel computing approaches, based on biological entities. Specifically, we will encode mathematical problems into networks consisting of nano- or microsized channels and nodes, and will use self-propelled biological agents to explore these networks and find the solution to the encoded mathematical problems. Due to the very large number of agents, the problem is solved in a highly parallel manner. A number of different types of micro- and nanoscale biological agents will be used, including innate objects (protein filaments propelled by molecular motors) and living systems (bacteria and fungi). The novelty of this approach lies in the use of self-propelled agents (avoiding scalability issues associated with the use of external driving forces), as well as in the combination of human intelligence (in the target-oriented design of networks) with the parallelism enabled by large numbers of biological agents. Key aims of the project will be the benchmarking against existing computational approaches, and the identification of application areas where this novel paradigm may lead to transformative applications. Benefits to society will include the ability to solve hitherto intractable problems, and the development of a sustainable and energy-efficient computing approach that is radically different from current ICT technology. More about ABACUS project here.
NASTI. Nanomaterials found innumerable applications in areas as diverse as space technology, aeronautics, transport, construction, and –central to this application- biology and medicine. Bio-related nanomaterials applications, which are both high added-value and societally-important, are very diverse themselves, from large consumer interest, e.g., sun screens, water treatment; to healthcare, e.g., biomaterials, drug delivery. This interest has translated into solid investment in research regarding the safe use of nanomaterials and nanoparticles (NPs). However, despite the large extent of experimental data available in various databases, the complexity of the processes involving interactions at nanometre level have to date resisted an accurate prediction of the deleterious effects of NPs from the physico-chemical properties. The project proposes to build models for predicting NP-induced toxicity that account for the impact of the NP surface physico-chemistry and geometry on key biomolecules involved in biological processes, with an emphasis on their NP-induced conformational change, and with an emphasis on proteins. The project aims to provide a set of universally-applicable measures for the characterisation of nano-objects, either natural, i.e., proteins, membranes, DNA molecules, or artificial – NPs; QSAR model for the prediction of NP-induced toxicity mediated by protein coronas; and of NP-induced toxicity on membranes.
Bionanofactory. Many aspects of the biological processes that occur at the interface between a biological organism and an artificial surface have become well-studied phenomena, due to the availability of techniques from the semiconductor and microdevice industries, which were used to build planar devices interfaced with cells or microorganisms. While a tremendous amount of valuable information has been gleaned from these studies there is a need to develop strategies to expand methods to study the processes that occur in a three-dimensional natural environment. This proposal will develop methods to create Nature-mimicking devices to measure the processes of biological organisms in 3D scaffolds, or structured materials. Learning about how organisms behave on a physico-chemical level in 3D will enable a myriad number of applications ranging from aiding pharmaceutical research & discovery all the way to enabling the development of more efficient algorithms for space searching, competition/cooperation, and traffic congestion.