ERC FUNDED PROJECTS

ACHIEVE focuses on the application of Excluded Volume Effect in cell culture systems in order to enhance Extracellular Matrix (ECM) deposition. It represents a new horizon in in vitro cell culture which will address major challenges in medical advancement and food security. ACHIEVE will elucidate extracellular processes which occur during tissue generation, identifying favourable conditions for optimum tissue cultivation in vitro. These results will be applied in the diverse fields of regenerative medicine, drug discovery and cellular agriculture which all require advancements in in vitro tissue engineering to overcome current bottlenecks. Effective in vitro tissue culture is currently limited by lengthy culture periods. An inability to maintain physiologic (in vivo) conditions during this lengthy in vitro culture leads to cellular phenotype drift, ultimately resulting in generation of an undesired tissue. Enhanced tissue generation in vitro will greatly reduce culture times and costs, effecting improved in vitro tissue substitutes which remain true to their original phenotype. The research will be addressed under four work-packages. WP1 will investigate biochemical, biophysical and biological responses to varying culture conditions; WP 2, 3 and 4 will apply results in the fields of Tissue Engineering, Drug Discovery and Cellular Agriculture respectively. Research will involve extensive characterisation of derived- and stem-cell cultures in varying conditions of expansion and relevant health and safety and preclinical testing. The five year programme will be undertaken at the National University of Ireland, Galway, a centre of excellence in tissue engineering research, at a cost of € 2,439,270.

2 076 770 €

Start date: 2020-09-01, End date: 2025-08-31

1.2 billion people worldwide lack access to safe drinking water. Drinking water quality is threatened by newly emerging organic micro-pollutants (pesticides, pharmaceuticals, industrial chemicals) in source waters. Nanofiltration is a technology that is expected to play a key role in future water treatment processes due to its effectiveness in removal of micropollutants. However, the loss of membrane flux due to fouling is one of the main impediments in the development of membrane processes for use in drinking water treatment. Currently there is a wholly inadequate mechanistic understanding of the role of biofilm on the fouling of nanofiltration membranes. Applying techniques including confocal microscopy, force spectroscopy, and infrared spectroscopy using an experimental programme informed by a technique known as scale-down together with mathematical modelling, it is confidently expected that significant advances will be gained in the mechanistic understanding of nanofiltration biofouling. The specific objectives are 1. How is the rate of formation and extent of such biofilms influenced by the biological response to the local microenvironment? 2 Elucidate the effect of extracellular polysaccharide substances on physical properties, composition and structure of these biofilms. 3: Investigate mechanisms to enhance biofilm removal by a physical detachment process complemented by techniques that alter biofilm material properties. A more fundamental insight into the mechanisms of nanofiltration operation will help in further development of this treatment method in future water treatment processes.

1 468 987 €

Start date: 2011-10-01, End date: 2016-09-30

As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.

1 499 786 €

Start date: 2021-01-01, End date: 2025-12-31

Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases. This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics. Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context. Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.

1 411 258 €

Start date: 2018-02-01, End date: 2024-01-31