A Centre of Excellence in Computational Biomedicine

Project Information

CompBioMed2

Grant agreement ID: 823712

DOI

10.3030/823712

Project closed

EC signature date 13 May 2019

Start date 1 October 2019

End date 31 March 2024

Funded under

• EXCELLENT SCIENCE - Research Infrastructures

Total cost

€ 8 345 472,50

EU contribution

€ 7 992 822,50

Coordinated by

UNIVERSITY COLLEGE LONDON
United Kingdom

Periodic Reporting for period 3 - CompBioMed2 (A Centre of Excellence in Computational Biomedicine)

Reporting period: 2022-10-01 to 2024-03-31

Summary of the context and overall objectives of the project

Computational methods, based on human biology, are now reaching maturity in the biomedical domain, rendering predictive models of health and disease increasingly relevant to clinical practice by providing a personalised aspect to treatment. Computer based modelling and simulation is well established in the physical sciences and engineering, where the use of high performance computing (HPC) is now routine.

The major purpose of our Centre of Excellence is to promote and foster the use of HPC as a fundamental cornerstone of computationally assisted biomedical research and help translate this into medical and clinical practice. We have, therefore, invested substantially in community building to spread knowledge, tools and best practice to students, researchers, and decision makers across the domain and to future and present clinicians. HPC has the potential to enhance industries in the healthcare sector including pharmaceuticals and medical device manufacturers, and underpinning a range of emerging sectors, such as those concerned with e-health and personalised medicine. The innovative modelling and simulation techniques we have developed within this Centre have proved to be of great interest and relevance to industrial researchers (including medical device manufacturers), HPC manufacturers and independent software vendors as well as with clinical practitioners.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have deployed and scaled up several application codes across High Performance Computing (HPC) centres, made possible through compute allocations awarded to CompBioMed. Selected codes from our partners have been tested on the largest supercomputers in the world and worked with the computer centres as part of co-design projects which are destined to produce well attuned exascale machines in the future (e.g. MEEP). Cardiovascular applications model the full arterial tree and have been working towards whole-human scale. We have investigated the effect on blood flow when plaques are formed and how the insertion of a stent or conducting a bypass graft can affect a patient’s prognosis. Heart models have been used at various scales from single cell to medical device trials and been key in investigating potential drug candidates for COVID-19. In the Molecularly based Medicine Exemplar, collaborations have investigated drug candidates against COVID-19 and developed new workflows using machine learning and molecular dynamics applications. The Neuro-musculoskeletal Exemplar has used bone modelling software developed within the project in a collaboration with Sheffield Teaching Hospital to investigate how the angle of a fall can affect bone fractures. This has also been extended to look at the risk of fracture over a 10-year period. Our partner in Bologna (UNIBO) used this new model to investigate Bone Strength and conducted in silico trials.

We followed up our successful IMAX film from CompBioMed1, 'Virtual Humans', with another film, 'The Next Pandemic', in CompBioMed2, both of which were widely disseminated. We have repeated our conference series with CompBioMed Conference 2021 and 2023. We have published over 227 scientific papers during CompBioMed2 and our partners have participated in and organised various major conferences and workshops. Our media and social media activity has been prolific, reaching collective audience sizes in excess of 180M people.

We have established a set of metrics for monitoring and reporting computational patterns that will be used on the future exascale machines. With monolithic, coupled, and ensemble patterns used in CompBioMed.

We have assessed the Data and Analysis requirements of the consortium and beyond, especially with respect to running large computational jobs on a machine and being able to access the data produced. This includes communication of this data between centres, storage and analysis. To aid with this, we have established and strengthened collaborations and joint projects with other European initiatives such as LEXIS, EOSC’s DICE, EUDAT and MEEP. This has also spread to our service provisions, and our access mechanisms for these services has been optimised. We have established our scalability so that users can find out about scaling possibilities for their codes. We have also federated the data management systems of the HPC centres LRZ and SURF.

Our applications have demonstrated deployment over 300,000 cores. 7 of our codes support GPU execution, including large-scale GPU deployments for two Patterns. We have prepared and reported a detailed VVUQ plan. The application scaling and porting service has supported four applications. We have redesigned the project Software Hub.

CD/CI pipelines are now in place for three codes. We have reviewed and improved the documentation for all codes, with code deployment and GUIs improved for several. Cloud deployment had been in production for 3 codes.

We have delivered a project-long e-seminar series, including an SME e-seminars series. We have organised an evangelisation event for exascale in computational biomedicine. We have continued delivering training at different levels to medical students and biomedical engineering researchers. Our CoE has further developed and refined the long-term project sustainability strategy within EU and UK, profiling each project applications and including training and its digitalisation as part of the sustainability plan.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have run applications on some of the largest supercomputers in the world, coming very close to a full partition of the machine for one application. By doing this we learned the bottlenecks and issues that needed to be addressed in our own codes and within the supercomputing network itself, to ensure good use of exascale computers.

We have published over 227 scientific publications in internationally leading journals. We greatly developed these applications, where a combination of capabilities through translational medicine boosted uptake in coordination with our External Expert Advisory Board and Associate Partner base to broaden the scope of users.

In education and training, we focused on the core principles of biomedical research, reaching out not only to computational scientists but targeting a new generation of clinicians. We have integrated a course into the medical training at UCL, UPF, and USFD, which has also evolved to allow distance or online learning to be used and will include additional institutes throughout Europe in the coming years. This enabled us to establish students in the teacher role, showing the need and appetite for this work within the medical student cohort. We have worked to identify a biomedically-qualified academic within all participating medical schools. We delivered training content outside of the taught curriculum, as a more effective way of disseminating at scale, and are now in a position to expand our extracurricular delivery to include medical schools in EU13 and HPC-poor countries through our links with EuroCC and other programmes.

CompBioMed extended GPU porting to more applications and increased testing on multiple architecture. We collaborated with vendors and system aggregators for co-design hardware and support.

We prepared a report and analysis of avenues for sustainability of the CoE beyond the lifetime of the project.

Long version of the CompBioMed logo