Core Technologies for Education and Innovation in Life 

Sciences



Project description: Within innovative research institutions, core facilities are important research infrastructures, providing access to advanced instrumentation and technologies operated by experts. Importantly, core facilities provide opportunities to be hubs of innovation as they enable: i) sharing of ideas and expertise on a specific cutting-edge technology across several research topics; ii) convergence of academic research and specific industrial demands as core facilities provide their services not only to principal internal investigators but also to external company users; iii) training diverse stakeholders (including MSc and Ph.D. students, post-docs and principal investigators) on advanced technologies.

However, the core facilities’ tremendous potential in innovation has not been fully deployed yet. Therefore, there is a timely need to develop innovative partnerships to design training that will provide core technologies expertise together with an entrepreneurial mindset and activities that will enable the creation of a robust network between core facilities and companies as well as Sharp practices for knowledge transfer.

In order to address these needs, we have created a strategic partnership, named InnoCore, which mix together research and higher education institutions together with experts on innovation, knowledge transfer and acceleration of business in the biotech industry. The consortium is well integrated by associated partners representing EU-wide networks and biomedical industry that will ensure engagement of companies in the implementation of project activities.




NEMHESYS

  • Principal investigator: prof. RNDr. Šárka Pospíšilová, Ph.D.
  • Acronym: NEMHESYS
  • Project partners: Universidad de Salamanca (Coordinator); University of Helsinki, Charité University Medicine Berlin, Queen´s University Belfast, Artificial Intelligence Techniques S.L., Mnémotix, IDimás Gestión S.L.
  • Investor: European Union, EACEA
  • National Agency of the Coordinator: 
  • Project type: ERASMUS+ KA2 – Knowledge Alliances
  • Implementation period: 01. 01. 2020 – 31. 12. 2022
  • Budget: 829 290 EUR (77 970 EUR for CEITEC MU)

Project description: 

Personalized medicine constitutes the ability to tailor healthcare decisions based on an individual’s unique characteristics (genetics, demographic information, healthcare experience, environment, and social factors) to more accurately diagnose the individual’s disease, predict its outcomes, and select treatments that increase the chances of a successful outcome and reduce possible adverse reactions. Moreover, it is the ability to predict an individual’s susceptibility to diseases with the goal of taking measures to prevent or mitigate the extent to which an individual will experience disease. A key component in personalized medicine is the emergence of whole-genome-scale sequencing as a platform to identify gene variants. Next-generation sequencing (NGS) allows for the fast generation of thousands to millions of base pairs of DNA sequence of an individual patient. The relatively fast emergence and the great success of these technologies in research herald a new era in genetic diagnostics. Since introduced into the research community in 2008, broad use of NGS has been greatly enabled by development of sophisticated informatics and analytic tools. However, the new technologies bring new challenges, both at the technical level and in terms of data management, as well as for the interpretation of the results and for counseling. The challenges to moving next-generation sequencing (NGS) into the clinical setting are both technical and regulatory. While such tools have reduced many of the barriers to clinical application of NGS, the investment needed to bring NGS into medical practice remains significant with the scale of knowledge required being unprecedented at most hospitals. In this context, hospitals are, to some extent, at a position similar to research institutes at the time when NGS first emerged. Some of the knowledge barriers to clinical use of NGS have been bypassed by the expertise in NGS data management developed at genomic centers and across the research community. However, even this wealth of experience does not fully address the roadblocks inherent to integrating NGS information into existing health workflows, and the added challenges posed by the regulatory controls in place across the health system.

These data scientist professionals will be required to work with specific technological tools and will be valued especially for their knowledge of statistics and programming, as well as for their ability to build data models and ask the right questions. They will learn these skills within the Master Class provided by University hospitals of the HEI. The Master Class will include workshops lead by private companies to improve skills and competences on big data, technological developments, ​​