Active Collaborations and Partnerships

6 December, 2023

Introduction

Throughout CompBioMed and until 30th of November, 2023, this document was used as a list of all then current collaborations/partnerships of CompBioMed2 Core Partners, which promoted synergy and thereby fostered our relationships.

 

There is a sister document, our central repository of all HPC e-Infrastructure bodies, which can be found here. This document is designed to enable application development.

 

The aim was to both record all collaborations for input to CompBioMed2 deliverables and reviews, and to share this information between Core Partners to help create new collaborations.

 

Below we differentiate between ‘partnerships’ and ‘collaborations’ between each centre and the external body.

 

  • Partnerships are where staff at your centre are directly funded by the body, perhaps as PIs, WP Leaders, Task Leaders, and/or providing technology support (consultancy, data safe haven management, hardware, etc.).
  • Collaborations are interactions creating synergies, between staff within your centre and staff at the other body, where the collaborative effort may or may not be funded directly by CompBioMed2. 
    • Collaborations can also be simply potential collaborations: synergies with CompBioMed that you think may be beneficial.

 

Each body can be either an EU Project, a CompBioMed User Community, or ‘Other’, e.g., a US Project, or a Country-led Project. 

 

For each of the bodies listed below, we provided information for each centre, describing 

  1. your Centre’s role in that project (PI, WP Leader, Task leader, Advisory Board member, etc.), and/or
  2. any on-going collaborations (either as part of CompBioMed or as part of another of your Centre’s projects), and/or a
  3. potential collaboration you believe CompBioMed can benefit from.

 

High Level Overview

  • EU Projects
    • Exascale Software
      • CoEs: BioExcel, ChEESE, CoEC, E-CAM, EoCoE, ESiWACE, EXCELLERAT, FocusCOE, HiDALGO, MaX, PerMedCoE, POP, T-Rex, SPACE
      • EPEEC, EPiGRAM-HS, Exa2Pro, ExaQUte, Hi-Fi Turb, STriTuVaD, VECMA, VESTEC, EUPEX
    • Exascale Hardware
      • DEEP-EST, EPI, ETP4HPC, ff4EuroHPC, Fortissimo, PRACE, EUPEX
    • AI and ML, Modelling 
      • CoEs: BioExcel, ChEESE, CoEC, E-CAM, EoCoE, ESiWACE, Excellerat, FocusCOE, HiDALGO, MaX, PerMedCoE, POP, T-Rex
      • PRIMAGE, VESTEC
      • SilicoFCM
      • In Silico World
    • Data Management
      • DICE, ELIXIR, EOSC Hub, EUDAT, EXSCALATE4CoV (E4C), LEXIS, VESTEC
  • User Communities
    • End-Users
      • Hospitals
      • VPH Institute
      • Avicenna Alliance (industries)
      • Others
        • HDR-UK, HEALTH-RI, IRB, Mobilise-D, GPAS
    • CompBioMed Advisory Board
      • EEAB
    • Software User Communities
      • CCPBioSim, HPC-Europa3, Palabos
    • Hardware User Communities
      • I4MS ICT
    • AI/ML User Communities
      • IRB, ISARIC4C
    • Data Management User Communities
      • RDA
  • Others
    • Exascale Software
      • CCPBIOSIM, ExCALIBUR, GoutSMART, IMAGE-INE, PICTURES, RAMP, SIRIUS
    • Exascale Hardware
      • Catalyst UK, RIKEN
    • AI/ML
      • HDR-UK, HEALTH-RI, iCAIRD, CRyPTIC
      • OpenMM
      • Groq, Cerebras Systems
    • Data Management
      • Bavarian Genomes, DataLoch, DigiMed Bayern, EMBL-EBI, HDR-UK, HEALTH-RI, SAGE2
    • Digital Europe CSA
      • EDITH

List of EU Projects and User Communities

XSEDE 

Time Frame

January 2018 – December 2021

Description

The Extreme Science and Engineering Discovery Environment (XSEDE) is a single virtual system that scientists can use to interactively share computing resources, data and expertise. People around the world use these resources and services — things like supercomputers, collections of data and new tools — to improve our planet.

 

https://www.xsede.org/

Relevance

High-performance computing

Core Partner Involvement

The RADICAL Lab utilises XSEDE machines and services to develop abstractions, design principles and systems engineering concepts for high-performance distributed cyberinfrastructure. The RADICAL Lab also deploys software and services on XSEDE machines. The RADICAL Lab also supports CompBioMed applications on the Frontera Supercomputer at TACC.

DOE Leadership Computing Facilities

Time Frame

January 2018 – December 2021

Description

The U.S. Department of Energy (DOE) provides a portfolio of national high-performance computing facilities housing some of the world’s most advanced supercomputers. These leadership computing facilities enable world-class research for significant advances in science.

 

http://www.doeleadershipcomputing.org

Relevance

Cyberinfrastructure Research and Development

Core Partner Involvement

The RADICAL Lab utilizes DOE computing facilities for high-performance computing research and development activities, as well as supporting CompBioMed applications on DOE facilities.

MolSSI

Time Frame

January 2018 – December 2021

Description

The Molecular Sciences Software Institute (MolSSI) serves as a nexus for science, education, and cooperation serving the worldwide community of computational molecular scientists – a broad field including biomolecular simulation, quantum chemistry, and materials science. The Institute is responsible for significant advances in software infrastructure, education, standards, and best-practices that are needed to enable the molecular science community to open new windows on the next generation of scientific Grand Challenges, ranging from the simulation of intrinsically disordered proteins associated with a range of diseases to the design of new catalysts vital to the global chemical industry and climate change.

 

https://molssi.org/

Relevance

Software engineering and middleware development

Core Partner Involvement

The RADICAL Lab is playing a leading role in the software engineering activities and middleware design and development upon which MolSSI is developing its capabilities.

AIDD

Time Frame

January 2021 – 2024

Description

The dramatic increase in the use of Artificial Intelligence (AI) and machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro biotech, and other chemical companies. However, the application of AI in these fields is not straightforward and requires excellent knowledge of chemistry. Thus, there is a strong need to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry, which is the prime goal of the AIDD proposal. Research WPs include sixteen topics selected to cover the key innovative directions in machine learning in chemistry. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated “One Chemistry” model that can predict outcomes ranging from different properties to molecule generation and synthesis. Training on various modalities allows the model to understand how to intertwine chemistry and biology to develop a new drug making its design robust and explainable. All partners agreed to make their software open source. It will boost the field and will provide the broadest possible dissemination of the results both to the academy and industry, including SMEs. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools. The IP policy and commercial exploitation of the project results have the highest priority supported by intellectual property asset management organisations. Comprehensive public engagement activities will complement the dissemination of results to the scientific community.

 

https://ai-dd.eu/

Relevance

PhD Training in machine learning applied to drug discovery. 

Core Partner Involvement

UPF

Core Partner of the project, UPF offers a joint PhD programme with Bayer on reactivity simulations by combining quantum mechanics and machine learning.

Bavarian Genomes

Time Frame

Sept 2018 – Aug 2021

Description

 

The Bavarian Genomes project connects the medical centres for rare diseases in Bavaria. Its goal is to identify the genomic sequence variants of at least a thousand patients suffering from rare diseases with genetically unclear diagnosis. RNA and DNA sequencing are carried out at a central up-to-date laboratory;. Data storage and analysis is carried out at the Leibniz Supercomputing Centre. Medical scientists and patients at the centres will get a network-based, controlled data access for decentralised analytics and interpretation. The project will improve the patient-centred care for rare diseases and will enable focal points for research on new treatment strategies.

 

https://www.bavarian-genomes.de (to be released soon).

Relevance

Data management and secure IT infrastructure

Core Partner Involvement

LRZ

Collaborating as IT partner in the project, responsible for IT infrastructure and system related topics.

 

Cerebras

Time Frame

Jan 2019 – 

Description

Cerebras builds wafer-scale hardware accelerators for complex artificial intelligence and deep learning applications. The wafer-scale engine behind their systems allows deep learning algorithms to be distributed over the entire chip thus pipelining model training and inference rather than GPU’s smaller core count which requires loading layer by layer. 

Relevance

AI/ML accelerator hardware for scientific HPC. 

Core Partner Involvement 

Collaborating with Argonne National Laboratory for testing AI accelerator hardware. 

SambaNova

Time Frame

Jan 2019 – 

Description

Established by industry luminaries, hardware and software design experts, and world-class innovators from Sun/Oracle and Stanford University—we aim to help bring AI to everyone, everywhere. SambaNova Systems Reconfigurable Dataflow Architecture is our software-defined hardware approach that powers SambaNova Systems DataScale—from algorithms to silicon. Our innovations are pushing past the limits of today’s solutions to accelerate AI and usher in a new era of computing.SambaNova Systems Reconfigurable Dataflow Unit (RDU) is the industry’s next-generation processor and is at the core of SambaNova DataScale. RDUs are designed to allow the data to flow through the processor in ways in which the model was intended to run, freely and without any bottlenecks. RDUs eliminate constant data caching and excess data movement inherent to today’s core-based architectures. This unlocks significant silicon utilisation to unleash more compute than any other solution available today.

Relevance

AI/ML accelerator hardware for scientific HPC. 

Core Partner Involvement 

Collaborating with Argonne National Laboratory for testing AI accelerator hardware. 

Groq

Time Frame

Jan 2019 – 

Description

Building the computer for the next generation of high performance machine learning. Groq hardware is designed to be both high performance and highly responsive. Groq’s new simplified architecture drives incredible performance at batch size 1. Whether you have one image or a million, Groq hardware responds faster. Every aspect of the Tensor Streaming Processor is designed in pursuit of performance. Instead of creating a small programmable core and replicating it dozens or hundreds of times, the TSP houses a single enormous processor that has hundreds of functional units. This novel architecture greatly reduces instruction-decoding overhead, and handles integer and floating-point data, which makes delivering the best accuracy for inference and training a breeze.

Relevance

AI/ML accelerator hardware for scientific HPC. 

Core Partner Involvement 

Collaborating with Argonne National Laboratory for testing AI accelerator hardware.

 

Catalyst UK

Time Frame

Jan 2018 – Dec 2022

Description

The key focus of the Catalyst UK programme is to investigate and showcase the potential of Arm-based HPC installations. This is one of the current approaches to overcome the limitations of traditional computer architectures and offer a better price-performance ratio for modern workloads and applications. This includes AI, which needs to process large amounts of data and requires extremely high memory bandwidth, and exascale computing, which requires HPC systems to be hundreds of times faster and more efficient than today’s fastest supercomputers. 

 

Not an EU project, nor a user community, but possible source of new HPC resources

 

https://www.gov.uk/government/publications/process-evaluation-of-the-catalyst-programmes

Relevance

co-design

Core Partner Involvement

EPCC

One of three UK university Core Partners

 

SilicoFCM

Time Frame

Jun 2018 – Feb 2022

Description

According to the 2014 European Society of Cardiology Guidelines, cardiomyopathies are defined as structural and functional abnormalities of the ventricular myocardium that are unexplained by flow limiting coronary artery disease or abnormal loading conditions. There are four major classifications of cardiomyopathy: hypertrophic (HCM), dilated (DCM), restrictive (RCM), and arrhythmogenic right ventricular (ARVC).

Familial cardiomyopathies (FCM) are most commonly diagnosed, or progress of the disease is monitored, through in vivo imaging, with either echocardiography or, increasingly, cardiac magnetic resonance imaging (MRI). The treatment of symptoms of FCM by established therapies could only in part improve the outcome, but novel therapies need to be developed to affect the disease process and time course more fundamentally.

SILICOFCM project will develop in silico computational cloud platform which will integrate from stopped-flow molecular kinetic assays to magnetic resonance imaging of the whole heart, bioinformatics and image processing tools with state of the art computer models with the aim to reduce animal and clinical studies for a new drug development and optimised clinical therapy of FCM.

The developed system will be distributed on the cloud platforms in order to achieve efficient data storage and high-performance computing that can offer end users results in a reasonably short time. Academic technical partners IIT, UOI, UL and BSC will be responsible for developing and integration of in silico cloud computational platforms with multi-scale cardiac muscle modelling which include experiments on protein mutation in vitro from UNIKENT, UNIFI and UW. Bioinformatics tools will be integrated by US company SBG. Clinical partners UNEW, ICVDV, UPMC and UHREG will do retrospective and prospective studies. SME partner R-Tech will be in charge of regulatory issues and reports and BIOIRC will do the exploitation of the project.

 

https://silicofcm.eu/

Relevance

Modelling and simulation in cardiology

Core Partner Involvement

BSC

Usage of Alya as a computational cardiac modelling tool for supercomputers.

 

CCPBIOSIM

Time Frame

Jan 2018 – Dec 2022

Description

CCPBioSim (Collaborative Computational Project for Biomolecular Simulation) is an inclusive and wide ranging project, bringing together chemists, physicists and chemical engineers as well as researchers from all branches of “molecule-oriented” biochemistry and biology. Our aim is to involve experimentalists and computational specialists, sharing the belief that the best science can be done when theory and experiment are closely integrated. CCPBioSim engages with early career researchers and the non-expert through the provision of tutorials and workshops enabling them to become proficient and productive users of biomolecular simulation techniques. We are also actively engaged in developing new advanced methods, which in future will be used by our community to deliver new and exciting science.

 

http://www.ccpbiosim.ac.uk/

Relevance

This project is not dissimilar to CompBioMed.

Core Partner Involvement

UCL

Prof Francesco Gervasio, UCL, part of Management Group

 

CoE BioExcel-2

Time Frame

Jan 2018 – Dec 2022

Description

BioExcel is the leading European Centre of Excellence for Computational Biomolecular Research. Established in 2015, the centre has grown into a major research and innovation hub for scientific computing. BioExcel develops some of the most popular applications for modelling and simulations of biomolecular systems. A broad range of additional pre-/post-processing tools are integrated with the core applications within user-friendly workflows and container solutions. The software stack comes with great performance and scalability capabilities for extreme-scale utilization of the worlds largest high-performance computing (HPC) and high-throughput computing (HTC) compute resources. BioExcel has developed an extensive training program to address competence gaps in extreme-scale scientific computing for beginners, advanced users and HPC/HTC system maintainers. The centre maintains an extensive and growing network of industrial researchers in the pharmaceutical, chemical and food industries, and offers tailored products and consultancy services, while code development is done in close collaborations with hardware and software vendors to ensure compatibility and support for cutting-edge features. BioExcel works closely with various governmental, non-profit, educational and policy projects and initiatives.

 

https://bioexcel.eu/

Relevance

This project is not dissimilar to CompBioMed.

Core Partner Involvement

EPCC

Core Partner, WP3 Lead of Use Cases and Community Support, members of WP1-6, bar WP2.

BSC

delete this subsection if ‘none’

CoE ChEESE

Time Frame

Jan 2018 – Dec 2022

Description

The main objective of ChEESE is to establish a new Center of Excellence (CoE) in the domain of Solid Earth (SE) targeting the preparation of 10 Community flagship European codes for the upcoming pre-Exascale (2020) and Exascale (2022) supercomputers.

 

https://cheese-coe.eu/

Core Partner Involvement

EPCC

Collaborating as Excellerat member in workshop including co-design

 

CoE CoEC

Time Frame

Jan 2018 – Dec 2022

Description

Center of Excellence in Combustion

Core Partner Involvement

UvA

delete this subsection if ‘none’

BSC

delete this subsection if ‘none’

 

CoE E-CAM

Time Frame

Oct 2015 – ?

Description

E-CAM is an e-infrastructure for software development, training, and industrial discussion in simulation and modelling. It has a 60 month duration (starting from October 2015) and involves 48 staff years of effort. For a complete list of our partners see here.

 

At E-CAM we focus on four scientific areas of interest to computational scientists:

  • Classical Molecular Dynamics
  • Electronic Structure
  • Quantum Dynamics
  • Meso- and Multi-Scale Modelling

 

https://www.e-cam2020.eu/

CoE EoCoE-II

Time Frame

Jan 2018 – Dec 2022

Description

At the crossroads of the energy and digital revolutions, EoCoE develops and applies cutting-edge computational methods in its mission to accelerate the transition to the production, storage and management of clean, decarbonized energy.

 

https://www.eocoe.eu/

Core Partner Involvement

BSC

Alya is a main code

 

CoE ESiWACE2

Time Frame

Jan 2018 – Dec 2022

Description

for future exascale weather and climate simulations

 

https://www.esiwace.eu/

 

CoE Protein-Ligand Residence Time

Time Frame

Jan 2018 – Dec 2022

Description

Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. UCL (group of Prof Andrea Townsend-Nicholson) and EVO (Alexander Heifetz) developed a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. 

Core Partner Involvement

UCL and EVO

UCL (group of Prof Andrea Townsend-Nicholson) and EVO (Alexander Heifetz) collaborate in developing HPC based tools for drug design 

 

CoE Developing new FMO-based applications for exploration of molecular interactions 

Time Frame

Jan 2018 – Sep 2023

Description

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of potency and selectivity. The efficiency and cost-effectiveness of the drug-discovery process can be accelerated by the availability of structural data regarding the target protein, and by the reliability of the computational tools for data explorations. However, even with the crystal structure in hand, “visual inspection” and force field-based molecular mechanics (MM) calculations often used for the rationalization of ligand-protein potency cannot always explain the full complexity of the molecular interactions.  The use of quantum mechanical (QM) methods has traditionally been employed to improve the reliability of the exploration of protein-ligand interactions.  However, in spite of their many advantages classical QM methods have not been feasible until recently for large biological molecules such as kinases, due to their high computational cost. The fragment molecular orbital (FMO) method offers substantial computational time saving over traditional QM methods. This is achieved by dividing the system into smaller pieces called fragments. FMO takes as an input for example a protein-ligand complex and provides a list of interactions and their chemical nature ((i.e. its strength in kcal/mol and chemical nature: hydrophobic, electrostatic, etc). 

UCL (group of Prof Andrea Townsend-Nicholson), EVO (Alexander Heifetz) and FMODD (FMO Drug Design Consortium) collaborate in developing FMO based tools for drug design FMODD is a non-profit organisation from Japan promoting application of the FMO method for drug design. In this collaboration industry and academia are working together using High Performance Computing Infrastructure (HPCI), a computer, to advance the technology based on FMO and spread FMO as a fundamental method for in silico drug design. 

The FMO applications developed had been effectively applied to a large number of structure-based drug design (SBDD) programs, most recently for COVID-19.

Core Partner Involvement

UCL and EVO

UCL (group of Prof Andrea Townsend-Nicholson) and EVO (Alexander Heifetz) collaborate in developing HPC-FMO based tools for drug design 

 

CoE Excellerat

 

Time Frame

Jan 2018 – Dec 2022

Description

The EXCELLERAT project is a single point of access for expertise on how data management, data analytics, visualisation, simulation-driven design and Co-design with high-performance computing (HPC) can benefit engineering.

 

https://www.excellerat.eu/

Core Partner Involvement

EPCC

Board member, WP2 Ref Apps Leader, Reference Application (TPLS) owner, Co-Design, Applications Working Group Leader, Meshing Task Leader

BSC

WP3 Leader, Reference Application (Alya) owner, AMR introduced into Alya

BULL

delete this subsection if ‘none’

CoE FocusCOE

Time Frame

Jan 2018 – Dec 2022

Description

FocusCoE contributes to the success of the EU HPC Ecosystem and the EuroHPC Initiative by supporting the EU HPC CoEs to more effectively fulfil their role within the ecosystem and initiative: ensuring that extreme scale applications result in tangible benefits for addressing scientific, industrial or societal challenges.

 

https://www.focus-coe.eu/

Relevance

Coordination and Support Action to HPC CoEs including CBM2

Core Partner Involvement

UCL

Collaborating as CBM2 rep in general meetings and the “business working group”

Task Leader

USHEFF

Collaborating as CBM2 rep in “business working group?

CBK

Collaborating as CBM2 rep at meetings and engagement with industry actions

BSC

CoE HiDALGO

Time Frame

Jan 2018 – Dec 2022

Description

We develop novel methods, algorithms and software for HPC and HPDA to accurately model and simulate the complex processes, which arise in connection with major global challenges.

 

https://hidalgo-project.eu/

 

Core Partner Involvement

EPCC

Collaborating as Excellerat member, joint workshop, where EPCC is leading co-design session

 

CoE MaX

Time Frame

Jan 2018 – Dec 2022

Description

The mission of MaX is to develop the technologies and make them available for large and growing base of researchers in the materials domain.

 

http://www.max-centre.eu/

 

Core Partner Involvement

EPCC

Delivered talks, including parallel meshing routines, at ITCP/MaX HPC Summer School, Mexico, 2018.

 

CoE PerMedCoE

Time Frame

1 October 2020 – 30 September 2023

Description

Personalised Medicine (PerMed) opens unexplored frontiers to treat diseases at the individual level combining clinical and omics information. However, the performances of the current simulation software are still insufficient to tackle medical problems such as tumour evolution or patient-specific treatments. The challenge is to develop a sustainable roadmap to scale-up the essential software for the cell-level simulation to the new European HPC/Exascale systems. Simulation of cellular mechanistic models are essential for the translation of omic data to medical relevant actions and these should be accessible to the end-users in the appropriate environment of the PerMed-specific big confidential data.

 

The goal of the HPC/Exascale Centre of Excellence in Personalised Medicine (PerMedCoE) is to provide an efficient and sustainable entry point to the HPC/Exascale-upgraded methodology to translate omics analyses into actionable models of cellular functions of medical relevance. It will accomplish so by 1) optimising four core applications for cell-level simulations to the new pre-exascale platforms; 2) integrating PerMed into the new European HPC/Exascale ecosystem, by offering access to HPC/Exascale-adapted and optimised software; 3) running a comprehensive set of PerMed use cases; & 4) building the basis for the sustainability of the PerMedCoE by coordinating PerMed and HPC communities, and reaching out to industrial and academic end-users, with use cases, training, expertise, and best practices.

 

The PerMedCoE cell-level simulations will fill the gap between the molecular- and organ-level simulations from the CompBioMed and BioExcel CoEs with which this proposal is aligned at different levels. It will connect methods’ developers with HPC, HTC and HPDA experts (at POP and HiDALGO CoEs). Finally, the PerMedCoE will work with biomedical consortia (i.e. ELIXIR, LifeTime initiative) and pre-exascale infrastructures (BSC and CSC), including a substantial co-design effort.

 

New H2020 funded project, announced March 2020: Personalised Medicine CoE, PI based at BSC: https://www.bsc.es/valencia-alfonso

 

This is the description of this EU Project/User Community, and the relevance to CompBioMed

 

https://cordis.europa.eu/project/id/951773

Relevance

This project is also a CoE and is also for personalised medicine

Core Partner Involvement

BSC

BSC is the PI, but with CBM2 non-related staff. However, the BSC spinoff company ELEM Biotech is core partner, with CBM2 related staff involved in the project.

 

PATC: Short course on HPC-based Computational Bio-Medicine, 16-19 Feb 2021, BSC+UCL+UvA+SURF+Atos+,

https://www.bsc.es/education/training/patc-courses/online-patc-short-course-hpc-based-computational-bio-medicine

PerMedCoE as a collaborating institution. 

Impact on HPC-poor european countries.

CoE POP2

Time Frame

Jan 2018 – Dec 2022

Description

 

The Performance Optimisation and Productivity Centre of Excellence in HPC provides performance optimisation and productivity services for (your?) academic AND industrial code(s) in all domains!

 

https://pop-coe.eu/

 

Core Partner Involvement

EPCC

Established collaboration as CBM2 T4.5 Lead re sharing their Application Form, Data Policy and T&Cs

SURF

Collaborating regard for CompBioMed T&C definition 

Collaborating on code analysis for Dutch HPC users.

BSC

PI, WP Leaders, etc.?  Only non-CBM2 staff?

LRZ

Using their Data Policy as basis for ours

 

CoE T-Rex

Time Frame

Jan 2018 – Dec 2022

Description

Targeting Real chemical accuracy at the EXascale

 

CoE SPACE

Time Frame

Jan 2023 – Dec 2026

Description

Scalable Parallel Astrophysical Codes for Exascale – SPACE is a newly-funded EU Centre of Excellence focused on astrophysical and cosmological applications.

Relevance

This project can provide potential feedback on the use of HPC at scale.

Core Partner Involvement

Atos

Co-design with EUPEX.

 

CPPBioSim

Time Frame

? – ?

Description

The Collaborative Computational Project for Biomolecular Simulation (CCPBioSim) aims to bring the UK biochemistry and biophysics communities together by providing training, events and software tools.

 

http://www.ccpbiosim.ac.uk/

Core Partner Involvement

UCL

Member of Management Group

UOXF

Member of Management Group

UEDIN (not EPCC)

Member of Management Group

 

CRyPTIC

Time Frame

Oct 2015 – June 2021

Description

The Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) has collected over 20,000 clinical tuberculosis samples. Each sample has had its whole genome sequenced and its susceptibility to 13 different antibiotics determined using a 96-well broth microdilution plate. The main goal of CRyPTIC is to discover the remaining genetic variation that is responsible for resistance to a range of antituberculars, thereby improving and encouraging the adoption of genetic-based clinical microbiology. This is the largest dataset of TB isolates to date and was instrumental in the recent release of the first WHO catalogue of tuberculosis mutations associated with resistance.

 

http://www.crypticproject.org/

Relevance

Providing unrivalled clinical dataset for ML and MD projects predicting resistance ab initio.

Core Partner Involvement

UOXF

PI, Analysis team

 

DataLoch

Time Frame

Jan 2019 – Dec 2024

Description

Linking primary and secondary health and social care datasets across the Lothian, Fife and Borders regions to support research and improve service provision.

 

https://ddi.ac.uk/projects/dataloch/


Relevance

Data management expertise

Core Partner Involvement

EPCC

Co-PI

 

DEEP-EST

Time Frame

1 July 2017 – 31 March 2021

Description

The DEEP-EST (“DEEP – Extreme Scale Technologies”) project will create a first incarnation of the Modular Supercomputer Architecture (MSA) and demonstrate its benefits.

 

https://cordis.europa.eu/project/id/754304

 

Relevance

Useful in co-design

Core Partner Involvement

BSC

Core Partner

EPCC

Core Partner

LRZ

Core Partner; data centre data base – monitoring and analytics

 

DICE – Data Infrastructure Capacity for EOSC

Time Frame

Jan 2021 – Jun 2023

 

Description

Big data storage and management is the cornerstone of digital services, and Europe cannot afford to leave its digital infrastructure lacking. One of the key tools for researchers and science professionals in the EU is the European Open Science Cloud (EOSC), which offers a multitude of services, including storage, data management, processing and analysis. The EU-funded DICE project will provide cutting-edge data management services and a significant amount of storage resources for the EOSC. The data services offered via DICE through EOSC are designed to be multidisciplinary and to fulfil the needs of different research communities. The goal is to enhance the EOSC infrastructure and ensure the best possible support to guide European research and innovation into the future.

 

https://www.digimed-bayern.de

 

Relevance

Data management and secure IT infrastructure

 

Core Partner Involvement

SURF

Task leader for Training activities. Leader CompBioMed use cases in DICE.

 

DigiMed Bayern

Time Frame

Oct 2018 – Sept Nov 2023

 

Description

DigiMed Bayern combines comprehensive clinical and epidemiological datasets, enriched with state-of-the-art multi-dimensional -omics characterization (genomics, transcriptomics, proteomics and metabolomics). For the integrative analysis of the resulting „Big Data“, an ethically and legally compliant and highly secure IT infrastructure will be fundamentally designed and implemented.

 

In addition, the infrastructure created by DigiMed Bayern will be sustainable and transferable to other institutions and disease areas. The population will benefit from concrete improvements in health management as well as the resulting advances in pre- diction, targeted prevention, diagnosis and therapy. The project, funded by the Bavarian State Ministry of Health and Care, is a pilot project within the Bavarian State ́s master plan “BAYERN DIGITAL II”.

 

https://www.digimed-bayern.de

 

Relevance

Data management and secure IT infrastructure

 

Core Partner Involvement

LRZ

Collaborating as IT partner in the project, responsible for IT infrastructure and system related topics.

 

EDITH

Time Frame

01/Oct/2022 – 30/Sep/2024

Description

EDITH is a Coordination and Support Action for the creation of an ecosystem for Digital Twins in Healthcare.

https://www.edith-csa.eu/

Relevance

Useful in co-design

Core Partner Involvement

UNIBO

Work package leader, Task leader.

UvA

Task leader.

BSC

Participating institution.

 

EEAB

Time Frame

Same as CompBioMed2

Description

EEAB is our External Expert Advisory Board

 

https://www.compbiomed.eu/innovation/innovation-advisory-board/

Relevance

Useful in co-design

Core Partner Involvement

CBK

Paul Best is a member

EPCC

Gavin Pringle is a member

 

EMBL-EBI

Time Frame

Jan 2018 – Dec 2022

Description

EMBL-EBI collaborates with scientists and engineers all over the world, and provides the infrastructure needed to share data openly in the life sciences.

 

Understanding how genetics affects the health of humans, plants and animals is essential to advances in disease prevention, food security and biodiversity.

 

They develop databases, tools and software that make it possible to align, verify and visualise the diverse data produced in publicly-funded research, and make that information freely available to all.

 

https://www.ebi.ac.uk/about/our-impact

 

EOSC Hub

Time Frame

Start: January 2018,  End: December 2020

Description

https://www.eosc-hub.eu/

 

EOSC-hub brings together multiple service providers to create the Hub: a single contact point for European researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research.

 

For researchers, this will mean a broader access to services supporting their scientific discovery and collaboration across disciplinary and geographical boundaries.

 

The project mobilises providers from the EGI Federation, EUDAT CDI, INDIGO-DataCloud and other major European research infrastructures to deliver a common catalogue of research data, services and software for research.

Relevance

EOSC Marketplace includes a listing of CompBioMed services

 

Core Partner Involvement

UCL

Applied for EOSC project

Partner in EOSC-dih

EPCC

May be involved with SURF/LRZ EOSC project

 

Also members of WP2 strategy and Business Development

SURF

Collaboration: Participation in Call: “INFRAEOSC-07-2020 / sub-topic a2 data services” 

LRZ

May be involved with SURF/LRZ EOSC project

EPEEC

Time Frame

Jan 2018 – Dec 2022

Description

EPEEC’s main goal is to develop and deploy a production-ready parallel programming environment that turns upcoming overwhelmingly-heterogeneous exascale supercomputers into manageable platforms for domain application developers. The consortium will significantly advance and integrate existing state-of-the-art components based on European technology (programming models, runtime systems, and tools) with key features enabling 3 overarching objectives: high coding productivity, high performance, and energy awareness.

 

https://epeec-project.eu/

EPI 

Time Frame

Jan 2018 – Dec 2022 (phase 1)

Jan 2022 – Dec 2024 (phase 2)

Description

The European Processor Initiative (EPI) is a project which implemented under the first stage of the Framework Partnership Agreement signed by the Consortium with the European Commission (FPA: 800928) and currently running the Specific Grant Agreement No 101036168 (for EPI SGA2), whose aim is to design and implement a roadmap for a new family of low-power European processors for extreme scale computing, high-performance Big-Data and a range of emerging applications.

 

https://www.european-processor-initiative.eu/

Core Partner Involvement

SURF

Core Partner, member of WP1 working on benchmarks of applications for architecture evaluation.

ATOS

Coordinator and Core Partner.

 

EPiGRAM-HS

Time Frame

Jan 2018 – Dec 2021

Description

EPiGRAM-HS is a European Commission Funded project with the goal of designing and delivering a programming environment for Exascale heterogeneous systems in order to support the execution of large scale applications.

 

https://epigram-hs.eu/

Relevance

Programming models for exascale machines

Core Partner Involvement

EPCC

Core Partner, member of WP1-6, bar WP4.

 

ETP4HPC

Time Frame

Jan 2018 – Dec 2022

Description

ETP4HPC – the European Technology Platform (ETP) for High-Performance Computing (HPC) – is a private, industry-led and non-profit association. Our main mission is to promote European HPC research and innovation in order to maximise the economic and societal benefit of HPC for European science, industry and citizens. Our main task is to propose research priorities and programme contents in the area of HPC technology and usage, by issuing a Strategic Research Agenda (SRA). This SRA is used by the EuroHPC Joint Undertaking (JU) to define the contents of the HPC Technology Work Programmes.

 

https://www.etp4hpc.eu/

 

Core Partner Involvement

EPCC

CBM1 WP Leader for co-design and collaboration.

 

EUDAT

Time Frame

Jan 2018 – Dec 2022

Description

The EUDAT Collaborative Data Infrastructure (or EUDAT CDI) is one of the largest infrastructures of integrated data services and resources supporting research in Europe. It is sustained by a network of more than 20 European research organisations, data and computing centres that on September 2016 have signed an agreement to maintain the EUDAT CDI for the next 10 years and in 2018 have supported the establishment of the limited liability company, EUDAT Ltd.

 

This infrastructure and its services have been developed in close collaboration with over 50 research communities spanning across many different scientific disciplines and involved at all stage of the design process.

 

https://www.eudat.eu/

 

Core Partner Involvement

 

SARA

Member of the EUDAT CDI partnership and is member of the Executive Board, member the secretariat, is Technical Coordinator, is shareholder of the EUDAT Ltd and offers B2SAFE, B2HANDLE and B2DROP as paid services through EUDAT Ltd.

 

Exa2Pro

Time Frame

1 May 2018 – 30 April 2021

Description

The vision of EXA2PRO is to develop a programming environment that will enable the productive deployment of highly parallel applications in exascale computing systems.

 

https://exa2pro.eu/

 

Examode

Time Frame

Jan 2019 – Dec 2022

Description

Exascale volumes of diverse data from distributed sources are continuously produced. Healthcare data stand out in the size produced (production is expected to be over 2000 exabytes in 2020), heterogeneity (many media, acquisition methods), included knowledge (e.g. diagnosis), and commercial value. The supervised nature of deep learning models requires large labeled, annotated data, which precludes models to extract knowledge and value.

EXA MODE solves this by allowing easy & fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.

 

https://www.examode.eu/

Relevance

Collaboration for ML/DL workflows in clinical setups.

Core Partner Involvement

SURF

Core partner of the project.

Collaboration: working to get Examode as CompBioMed associate partner.

 

ExaQUte

Time Frame

Jan 2018 – Dec 2022

Description

The ExaQUte project aims at constructing a framework to enable Uncertainty Quantification and Optimization Under Uncertainties in complex engineering problems, using computational simulations on Exascale systems.

 

https://exa2pro.eu/

ExCALIBUR

Time Frame

Jan 2018 – Dec 2022

Description

ExCALIBUR is a £45.7m programme to address the challenges and opportunities offered by computing at the exascale, led by the Met Office and the Engineering and Physical Sciences Research Council, and is a UK Research and Innovation (UKRI) Strategic Priorities Fund programme. The Strategic Priorities Fund is one of the UK Government’s largest programmes to work on multi-disciplinary and inter-disciplinary research and innovation, and has the ultimate aim of boosting the productivity and competitiveness of the economy. In the context of ExCALIBUR, this includes work on currently intractable problems of strategic importance such as drug and vaccine research, climate and weather prediction, and fusion power and green energy sources.

 

https://www.epcc.ed.ac.uk/news/excalibur-exascale-computing

Relevance

Exascale design consultation, including exascale meshes

Core Partner Involvement

UCL

UCL is a partner in a number of the programme’s eight groups, and leads The Materials and Molecular Modelling Exascale Design and Development Working Group

EPCC

EPCC is a partner in three of the programme’s eight groups, The Materials and Molecular Modelling Exascale Design and Development Working Group; leads ELEMENT: the Exascale Mesh Network; EXALAT: Lattice Field Theory at the Exascale Frontier

 

EXSCALATE4CoV (E4C) 

Time Frame

Jan 2018 – Dec 2022

Description

E4C is a public-private consortium supported by the European Commission’s Horizon 2020 tender for projects to counter the Coronavirus pandemic and improve the management and care of patients.

 

At the core of E4C is Exscalate (EXaSCale smArt pLatform Against paThogEns), at present the most powerful and cost-efficient intelligent supercomputing platform in the world. Exscalate has a “chemical library” of 500 billion molecules and a processing capacity of more than 3 million molecules per second. The E4C consortium, coordinated by Dompé Farmaceutici , is composed by 18 institutions from seven European countries.

 

https://www.exscalate4cov.eu/

FAIRsFAIR 

Time Frame

Mar 2019 – Mar 2022

Description

FAIRsFAIR – Fostering Fair Data Practices in Europe – aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. Emphasis is on fostering FAIR data culture and the uptake of good practices in making data FAIR. FAIRsFAIR will play a key role in the development of global standards for FAIR certification of repositories and the data within them contributing to those policies and practices that will turn the EOSC programme into a functioning infrastructure. In the end, FAIRsFAIR will provide a platform for using and implementing the FAIR principles in the day to day work of European research data providers and repositories. FAIRsFAIR will also deliver essential FAIR dimensions of the Rules of Participation (RoP) and regulatory compliance for participation in the EOSC. The EOSC governance structure will use these FAIR aligned RoPs to establish whether components of the infrastructure function in a FAIR manner.

 

https://www.fairsfair.eu/

Relevance

Potential collaboration for Data Management and FAIR data policies.

Core Partner Involvement

SURF

SURF is one of the partners in this project, and is most actively engaged in WP2 “FAIR Practices: Semantics, Interoperability, and Services”.

 

ff4EuroHPC

Time Frame

Sept 2020 – yy nnnn

Description

Promoting HPC to EU SMEs

 

Fortissimo2

Time Frame

Sept 2019 – Sept 2020 (continued as self-sufficient project after ff2 project came to an end)

Description

https://www.fortissimo-project.eu/ including software, hardware and expertise.

The Fortissimo Marketplace provides one-stop, pay-per-use, on-demand access to advanced simulation, modelling and data analytics resources including software, hardware and expertise.

Relevance

This project is an example of a one-stop-shop provider of HPC services.

Core Partner Involvement

EPCC

PI, Core Partner, Helpdesk Manager

BULL

https://www.fortissimo-project.eu/partners/bull-sas

 

GoutSMART

Time Frame

?

Description

Gout self-management aiming to reach target urate (GoutSMART) is a collaboration between NHS Lothian rheumatology department and the University of Edinburgh. It was developed in response to a call from patient supporters of Arthritis Research UK for new approaches to the management of all forms of arthritis, specifically for approaches that put patients at the heart of the decision making process. The approach has been made possible by finger prick testing kits of urate which mean that people with gout can now monitor the effectiveness of their treatments directly. The development of the smartphone app which integrates these results into the patients usual NHS care has been generously supported by NHS Lothian endowment funds.

 

https://secure.epcc.ed.ac.uk/goutsmart/static/Gout_information.html

Relevance

Using Apps to help patients self-manage, where such apps was one exemplar which arose from the then IAB.

Core Partner Involvement

EPCC

PI, Core Partner

 

GPAS

Time Frame

Ten year donation by ORACLE

Description

The Genome Pathogen Analysis Service (GPAS) aims to simplify and commoditise the genetics processing analysis steps in pathogen sequencing. A user uploads the raw read files output by their genetic sequencer and gets back relevant clinical information — for SARS-CoV-2 this will be the lineage, WHO classification, consensus genome and Spike mutations whereas for tuberculosis it will be the lineage, consensus genome and antibiogram.

 

GPAS is enabled by a large donation of cloud resource and developer time by ORACLE to UOXF and aims to launch a minimal viable product for SARS-CoV-2 in Fall 2021.

 

https://gpas.cloud/

Relevance

GPAS will potentially provide large clinical datasets for machine learning (subject to the data owners’ permission) which in turn can be added to the pathogen pipeline(s) to add extra functionality.

Core Partner Involvement

UOXF

Core partner

 

HDR-UK

Time Frame

?

Description

The application of cutting-edge data science to health and medical data in order to address population health challenges is an exciting and fast moving new field of research. Health Data Research (HDR) UK, a pioneering national institute, was formed in April 2018 to support world-leading research in this area.

 

The institute is a joint investment led by the Medical Research Council, together with eight public and charitable organisations. HDR UK brings together the country’s leading expertise in data analytics, computing and statistics with health data, medical research and genomics, with the aim of developing cutting-edge analytical tools and methodologies to address the most pressing health research challenges.

 

https://www.hdruk.ac.uk/

 

Relevance

Health Data Research UK incorporates data management and ML in a health/medicine environment.

Core Partner Involvement

EPCC

PI, Core Partner 

https://www.epcc.ed.ac.uk/blog/2018/07/16/health-data-research-uk

 

HEALTH-RI

Time Frame

Jan 2018 – Dec 2022

Description

Health-RI is a public-private partnership of organizations involved in health research and care. More than 70 organizations in the Netherlands endorse our efforts.

 

Our mission is to build an integrated health data research infrastructure accessible for researchers, citizens and care providers.

 

Facilitate and foster the optimal use of knowledge, tools, facilities, health data and samples to enable a learning healthcare system and accelerate sustainable and affordable personalized medicine and health.

 

https://www.health-ri.nl/

Core Partner Involvement

SURF is a  partner of Health RI and provides services for the HRI community, eg. Helpdesk, identity & access management, hosting of services. HRI & SURF try to offer a uniform service portfolio for Health Research in the Netherlands.

 

Hi-Fi Turb

Time Frame

1 July 2019 – 30 June 2022

Description

Modelling turbulent flows using computational fluid dynamics has progressed rapidly over the last decades and given rise to significant changes in the design processes of aircraft, cars and ships. New models are needed to enhance prediction of the laminar flow transition to turbulence for better control of the fluid flow. Against this backdrop, the EU-funded HIFI-TURB project will use high-fidelity large-eddy simulations and direct numerical simulations to predict complex flows. New artificial intelligence and machine learning algorithms will allow researchers to identify important correlations between turbulent quantities. Improved models for complex fluid flows will offer the potential of further reducing energy consumption, emissions and noise of aircraft, ships and cars.

HPC-Europa3

Time Frame

Jan 2018 – Dec 2022

Description

HPC-Europa3 Transnational Access programme offers:

  • access to world-class HPC systems to academic and industrial researchers
  • scientific collaboration with host researchers in any field
  • technical support by the HPC centres
  • travel and living expenses reimbursed

 

http://www.hpc-europa.eu/

Relevance

Collaboration for the CompBioMed Exchange visitor programme.

Core Partner Involvement

UCL

Hosted visitors

EPCC

Core Partner, WP leader and Task leader, Host, Wp1-5, bar Wp4.

SURF

Core Partner, Host.

UVA

Hosted visitors.

iCAIRD

Time Frame

November 2018 – ?

Description

The iCAIRD project is working to establish a world-class centre of excellence in the application of artificial intelligence to digital diagnostics. The intention is that iCAIRD will allow clinicians, health planners and industry to work together, enabling research-active clinicians to collaborate with innovative SMEs to better inform clinical questions, and ultimately to solve healthcare challenges more quickly and efficiently.

 

https://icaird.com/

Relevance

AI and ML in healthcare

Core Partner Involvement

EPCC

Tech provider: hosting the National Data Safe Haven and the national archive of digital pathology images. The archive is expected to grow to a very substantial amount, in the order of many hundreds of TB. https://www.epcc.ed.ac.uk/blog/2019/icaird

 

INSIST

European Union’s Horizon 2020 research and innovation programme under grant agreement No 777072.

Time Frame

November 2017 – April 2022

Description

The INSIST consortium is the result of a truly multi-disciplinary and multi-sectorial effort. It is headed by an interventional neuroradiologist and combines the knowledge of multiple experts in computational science, biology, biophysics, biomedical engineering, epidemiology, and radiological, neurological, and cardiovascular clinical research. The crucial involvement of academic and large teaching hospitals will secure access to patient data and clinical expertise. This will ensure that the in silico tools resulting from this project have a direct impact in the clinic. All the relevant end users (neurologists and intervention radiologists, as well as device- and pharma industry) are member of the INSIST consortium and involved in the entire process to ensure a successful and relevant development of the in silico tools.

 

URL: https://www.insist-h2020.eu/

Relevance

Scientific Collaboration

Core Partner Involvement

UniGe

 

UvA 

PI, Core partner

 

Interpretable pathological test for Cardio-vascular disease

Collaboration between UniGe and two other partners:

  1. Université Libre de Bruxelles, CHU de Charleroi (Professor Karim Zouaoui-Boudjeltia)
  2.     University of Warwick (Professor Ritabrata Dutta)

Time Frame

2015 – ongoing

Description

Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present clinical tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. With this collaboration, we propose a stochastic platelet deposition model (calibration using Palabos-npFEM) and an inferential scheme for uncertainty quantification of these parameters using Approximate Bayesian Computation and distance learning.

Relevance

Scientific Collaboration

Core Partner Involvement

UniGe

 

IMAGE-INE

Time Frame

November 2018 – Dec 2022

Description

Image analysis project opens the way to personalised radiotherapy treatment

 

A research project funded by the Chief Scientist’s Office (Scotland) is to investigate the effectiveness of image analysis techniques in predicting side effects of radiotherapy treatment for patients with head and neck cancer.

 

The project, called IMAGE-INE, will apply image analysis to radiotherapy scans to assess a patients’ likelihood of developing specific side effects. This would allow treatment plans to be adapted if the patient is judged to be at risk, introducing the potential for personalised medicine in radiotherapy.

 

https://www.epcc.ed.ac.uk/news/image-analysis-project-opens-way-personalised-radiotherapy-treatment

Relevance

Image analysis for healthcare, potentially of interest to USHEFF’s bone image processing application

Core Partner Involvement

EPCC

Optimises image analysis software developed by researchers at the Edinburgh Cancer Centre, in Edinburgh’s Western General hospital, ready to trial the technique with patient data being curated at the University of Cambridge using a state-of-the-art TomoTherapy HiArt scanner. The data will be analysed on Cirrus, which has the capacity to allow the analysis technique to be fine-tuned over the whole patient dataset.

 

INSIST

Time Frame

November 2017 – Apr 2022

Description

Stroke is the number one cause of disability in the Western world and the 3rd most common cause of death. Despite new treatment options with intra-arterial thrombectomy, still 2 out of 3 patients still have a poor outcome. The main goal of INSIST is to advance treatments of ischemic stroke and its introduction in clinical practice by realizing in silico clinical stroke trials in which stroke and treatment are modeled. We will generate virtual populations of stroke patients, generate and validate in silico models for intra-arterial thrombectomy, thrombosis and thrombolysis, and microvascular perfusion and neurological deterioration after stroke, and integrate the in silico models to realize an in silico clinical stroke trial. We are uniquely positioned by the availability of a large pool of clinical, imaging, histopathological, and outcome data from multiple recently finalized stroke trials, a large registry (totaling 4500 patients), and new trials that will start later this year (totaling 2500 patients). We will build a population model that takes this input to generate virtual populations of stroke patients addressing the wide variety of patient characteristics. We will build on existing and emerging in silico models to validate reusable models for stroke and stroke treatment with a strong interaction with experimenting modeling in laboratories. The in silico models and virtual populations will be combined to simulate clinical trials and validated by simulating and comparing finalized and currently running trials. The in silico models will be used to simulate clinical trials to evaluate effectiveness and safety of novel devices and medication, both for the device as well as the pharmacological industry. For the device industry, we will evaluate the optimal configuration of thrombectomy stents for reduction of thrombus fragmentation. From the perspective of the pharmacy industry, we will simulate the effect of increased TAFIa on the effectiveness of alteplase.

 

Relevance

external to CompBioMed2 example of a replica workflow with extensive HPC needs; biomedical use case.

Core Partner Involvement

SURF

Support simulation campaign to be able to build surrogate models for so-called thrombectomy treatments to run on Cartesius. Connection through UvA.

 

I4MS ICT

Time Frame

Jan 2018 – Dec 2022

Description

I4MS, ICT Innovation for Manufacturing SMEs, is a European initiative supporting manufacturing SMEs and mid-caps in the widespread use of information and communication technologies (ICT) in their business operations. Under I4MS, SMEs can apply for technological and financial support to conduct small experiments allowing them to test digital innovations in their business via open calls. The I4MS project is now in its third phase with each phase having complementary objectives.

 

https://i4ms.eu/

IRB

Time Frame

Jan 2018 – Dec 2022

Description

IRB Barcelona is a world-class research centre devoted to understanding fundamental questions about human health and disease. It was founded in October 2005 by the Government of Catalonia and the University of Barcelona, and is located at the Barcelona Science Park.

 

https://www.irbbarcelona.org/en/about-us

Core Partner Involvement

BSC

delete this subsection if ‘none’

 

ISARIC4C

Time Frame

July 2020 – ?

Description

ISARIC, the International Severe Acute Respiratory and Emerging Infection Consortium, is a global federation of clinical research networks, providing a proficient, coordinated, and agile research response to outbreak-prone infectious diseases. ISARIC4C is the consortium’s response to COVID-19, and EPCC is working with clinical and genomic experts from Oxford, Liverpool, Manchester and Edinburgh universities, including Edinburgh’s Roslin Institute, to provide a data and computing environment to help search for possible genetic markers in COVID-19 patients.

 

https://isaric.tghn.org/

Relevance

Data management and ML

Core Partner Involvement

EPCC

Manages the Scottish COVID-19 Data Repository has been assembled within the secure data management zone over the last few months, and is now operational and receiving weekly updates of testing and clinical data from eDRIS. Research projects authorised under a COVID-19 fast-track approvals process by the Scottish national Public Benefit and Privacy Panel are already underway.

 

LEXIS

Time Frame

Jan 2018 – Dec 2021

Description

 

“The LEXIS project will build an advanced engineering platform at the confluence of HPC, Cloud and Big Data which will leverage large-scale geographically-distributed resources from existing HPC infrastructure, employ Big Data analytics solutions and augment them with Cloud services.

 

“Driven by the requirements of the pilots, the LEXIS platform will build on best of breed data management solutions and advanced distributed orchestration solutions augmenting them with new, efficient hardware capabilities in the form of Data Nodes and federation, usage monitoring and accounting/billing supports to realize an innovative solution.”

 

LEXIS is an EU project which aims to build an advanced, geographically-distributed, HPC infrastructure for Big Data analytics. In practice, various HPC and cloud infrastructure services from LRZ and IT4I are made available.

 

The users of Lexis will provide Lexis with a) a workflow template which describes how to perform their calculations, and b) input datasets, using a web-based LEXIS front-end. After approval, workflows can be executed, and resulting datasets are included in the LEXIS Distributed Data Infrastructure (DDI) for ease of access, publication, search, etc, using FAIR principles.

 

Access to the actual hardware is mediated via an orchestrator which decides how to divide the workflow pieces among the possible execution hardware.

 

The LEXIS APIs and web front-end are replicated at LRZ and IT4I.

 

The DDI is based on iRODS (backed by e.g. LRZ DSS), and provides seamless backup, replication among centers and connection to EUDAT services such as B2SAFE and B2STAGE (e.g. handle identifiers, and GridFTP access).

 

Authentication is based on OpenID Connect and access to datasets distinguishes between private (user-level) data, project-level data, and public data.

 

At the current state of the project, APIs for the handling of data and metadata are available for testing, and the orchestrator can execute workflows related to three LEXIS pilots (respectively in Aerospace, Weather and Earthquake fields).

 

Users with basic knowledge in computer science can log in to the portal, request core hours, choose a pre-existing workflow, stage their data, and run their workflows. 

 

https://lexis-project.eu/web/

Relevance

LEXIS provides up to a certain level, a functional data management system with orchestration. At that point in the project, certain workflows are possible to be executed on the LEXIS platform. Having also combined some of EUDAT services with our system, this is a good opportunity for CompBioMed2 to use the LEXIS platform and run some simulations and workflows to see if such a system–especially the data management system is applicable and sufficient for their needs. CMB2 have to note that iRODS as a core for a distributed data management system is a prerequisite for some EUDAT services such as B2SAFE, and B2STAGE. This could help CompBioMed in deciding if they want to move forward with an iRODS-EUDAT solution or try something else. In the case of the first option, LEXIS has gained experience in designing such a system and it’s possible to share our experiences.

 

LEXIS uses some of EUDAT services within its Distributed Data Infrastructure(DDI): EUDAT B2HANDLE, B2SAFE, and B2STAGE. B2HANDLE is used to assign persistent identifiers to datasets. B2SAFE takes care of replicating data among different data centers hosting the DDI. B2STAGE connects any data source with a GridFTP endpoint to the LEXIS platform by allowing actors to move data into the DDI.

 

Status

Lexis open call for collaborations: https://lexis-project.eu/web/open-call

CBM2 is an invited project.  Providing computing time at LRZ and IT4I, and storage at RLZ and IT4I (LRZ have 50TB for all lexis collabs) https://lexis-project.eu/web/services/

Core Partner Involvement

BULL

Core Partner

EPCC

Leader of Task 3.3 Data Staging, and created Resilient Workflow for Urgent Computing on Exascale Systems, and led the collaboration

SURF

Collaboration: meeting with Lexis partner for integration CompBioMed-Lexis data platform.

LRZ

Lexis Partner: WP3 Leader

CompBioMed WP3 leading joint telcos to determine possible synergies.

 

Mobilise-D

Time Frame

Apr 2019 – Mar 2024

Description

Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement.

 

This very large IMI project aims to develop and qualify with major regulators for the use in drug trials, next-generation algorithms for the extraction of Digital Mobility Outcomes from the signals recorded by wearable Inertial Measurement Units, as a quantitative biomarker of mobility performance.

 

https://www.mobilise-d.eu/ 

Relevance

Connecting in-silico medicine to clinical outcomes; use of digital health technologies in the assessment of new medical products (In Silico Trials).

 

Core Partner Involvement

UNIBO

CBM2 and non-CBM2 staff.

Core Partner, WP5 leader, Task leader: algorithm VV&UQ, regulatory qualification.

 

OpenMM

Time Frame

From 2017

Description

OpenMM is the most widely-used open source GPU-accelerated framework for biomolecular modeling and simulation. Its Python API makes it widely popular as both an application (for modelers) and a library (for developers), while its C/C++/Fortran bindings enable major legacy simulation packages to use OpenMM to provide high performance on modern hardware. OpenMM has been used for probing biological questions that leverage the $14B global investment in structural data from the PDB at multiple scales, from detailed studies of single disease proteins to superfamily-wide modeling studies and large-scale drug development efforts in industry and academia. Originally developed with NIH funding by the Pande lab at Stanford, the team work will focus on the transition toward a community governance and sustainable development model and extend its capabilities to ensure OpenMM can power the next decade of biomolecular research. To fully exploit the revolution in QM-level accuracy with machine-learning (ML) potentials, they will add plug-in support for ML models augmented by GPU-accelerated kernels, enabling transformative science with QM-level accuracy. To enable high-productivity development of new ML models with training dataset sizes approaching 100M+ molecules, we will develop a Python framework to enable OpenMM to be easily used within modern ML frameworks such as TensorFlow and PyTorch. Together with continued optimizations to exploit inexpensive GPUs, these advances will power a transformation within biomolecular modeling and simulation, much as deep learning has transformed computer vision. URL: http://openmm.org/

Funding sources:

  • May 2020-April 2021For the period May 2020-April 2021, OpenMM is currently (May 2020-Apr 2021) was funded by a Chan Zuckerberg Initiative Essential Open Source Software for Science grant.
  • July 2021 – Mar 2025 award nr R01GM40090 of the NIH

Relevance

software

Core Partner Involvement

Acellera / UPF

 

Palabos User Community

Time Frame

2019 – ongoing

Description

The Palabos library is a framework for general-purpose computational fluid dynamics (CFD), with a kernel based on the lattice Boltzmann (LB) method. It is used both as a research and an engineering tool: its programming interface is straightforward and makes it possible to set up fluid flow simulations with relative ease, or, if you are knowledgeable of the lattice Boltzmann method, to extend the library with your own models. Palabos stands for Parallel Lattice Boltzmann Solver.

 CompBioMed benefits from Palabos and its community on biomedical fluid flows.

 

URL: https://palabos-forum.unige.ch/

Relevance

software

Core Partner Involvement

UniGe

 

PICTURES

Time Frame

Dec 2019 – ?

Description

Each year millions of clinical images such as X-rays, CT, MRI, ultrasound, nuclear medicine, and retinal images are generated by the NHS in Scotland and stored in the national imaging database. In addition to containing important clinical information, these images also potentially contain a great deal of information about the health of the individual which is not currently made use of in health care.

 

The PICTURES project will make use of the approximately 30 million NHS images collected since 2006, employing artificial intelligence (AI) to search for ‘warning signs’ in the images which predict the development of diseases. This will allow doctors in the future to make use of this information in routine care, greatly enhancing the clinical utility of routine scans.

 

The project has been funded by the Medical Research Council (MRC), Engineering and Physical Sciences Research Council (EPSRC) and industry partners to develop the technology that will unlock the huge additional potential of these images.

 

https://www.epcc.ed.ac.uk/blog/2019/12/predicting-disease-artificial-intelligence

Relevance

Medical images, data management and AI/ML, perhaps on interest to USHEFF and their bone image processing application.

Core Partner Involvement

EPCC

Tech provider

 

PRIMAGE

Time Frame

Dec 2018 – Nov 2022

Description

The project, financed by the European Commission, has 16 European partners that are participating in the consortium and has an implementation duration of 4 years. Internationally recognized researchers in in-silico technologies and clinical experts in pediatric cancer are part of the staff of PRIMAGE.

 

This project proposes an open cloud-based platform to support decision making in the clinical management of two paediatric cancers, Neuroblastoma (NB), the most frequent solid cancer of early childhood, and the Diffuse Intrinsic Pontine Glioma (DIPG) the leading cause of brain tumour-related death in children.

 

PRIMAGE platform implements the latest advancement of in-silico imaging biomarkers and modelling of tumour growth towards a personalised diagnosis, prognosis and therapies follow-up.

 

https://www.primageproject.eu/ 

Relevance

At the core of PRIMAGE there is a complex patient-specific, multiscale model of the growth of a neuroblastoma (a paediatric cancer), which poses some major scalability challenges.  We expect PRIMAGE to seek scalability support from CBM2, starting form Q2/2021, when the first complete implementation of the model will be available.

 

Core Partner Involvement

UNIBO

Lead of scale-bridging strategies.  Developer of the multiscale model orchestration software component.

 

PRACE

Time Frame

Jan 2018 – Dec 2022

Description

The mission of PRACE (Partnership for Advanced Computing in Europe) is to enable high-impact scientific discovery and engineering research and development across all disciplines to enhance European competitiveness for the benefit of society. PRACE seeks to realise this mission by offering world class computing and data management resources and services through a peer review process.

 

PRACE also seeks to strengthen the European users of HPC in industry through various initiatives. PRACE has a strong interest in improving energy efficiency of computing systems and reducing their environmental impact.

 

https://prace-ri.eu/

Relevance

Collaboration for resources access and management.

Core Partner Involvement

EPCC

non-CBM2 staff: WP Leader for Applications; Manager of SHAPE Programme, WP2, 4, 6, 7, and 8.

 

Established collaboration as CompBioMed with SHAPE regarding T2.4 application form

LRZ

Hosting site for PRACE aisbl; core partner PRACE 6ip; lead WP 5; contributor WP 4, 7, and 8; PRACE training centre

SURF

Core partner of the project. Contributor to WP3,4,5,6,7 and 8

Quantum Internet Alliance

Time Frame

Oct 2018 – Sept 2021

Description

The European Quantum Internet Alliance (QIA) addresses the Quantum Flagship strategic objectives related to the development of entanglement-based networks by developing a Blueprint for a pan-European entanglement-based Quantum Internet. To achieve this goal, we designed an approach where we take into account the potential user demands of such a quantum internet and we push forward the development of hardware (end processing nodes, quantum repeaters) and software (efficient control plane and software stack) to enable quantum internet real-world applications. This is combined with a feasibility and scalability analysis based on the network requirements that can enable end-to-end qubit transmission. This Network Architecture Blueprint will give crucial insights into the relative importance of the different hardware parameters that will need to be optimised. As a final step we will perform an overall systems test (Blueprint demo) to demonstrate the integration between the combined hardware and software stack by executing a high-level application on a demonstration network connecting multiple network nodes.

 

https://epcced.github.io/ramp/

Relevance

Quantum computing research collaborations

Core Partner Involvement

SURF

SURF participates in this project by providing computing time and support (SURF) but also providing support and information related with the optical fibre structure and properties within the Netherlands (SURFnet).

 

RAMP

Time Frame

May 2020 – Dec 2021

Description

Rapid Assistance in Modelling the Pandemic (RAMP) initiative, convened by the Royal Society in the UK to enhance existing COVID-19 initiatives. The motivation was to enhance the modelling teams who inform Government policy through channels such as SPI-M (Scientific Pandemic Influenza Modelling Group), which reports to SAGE (Scientific Advisory Group for Emergencies).

 

https://epcced.github.io/ramp/

Relevance

Covid-19 modelling

Core Partner Involvement

EPCC

Project management

 

RDA

Time Frame

May 2020 – ?

Description

The Research Data Alliance (RDA) builds the social and technical bridges to enable the open sharing and re-use of data.

 

The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government’s National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data.

 

RDA has a grass-roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects.

 

https://www.rd-alliance.org/

Relevance

Data user community

Core Partner Involvement

EPCC

Active members

 

RIKEN

Time Frame

Jan 2018 – Dec 2022

Description

RIKEN is Japan’s largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines

 

https://www.riken.jp/en/

 

SAGE2

Time Frame

July 2018 – December 2021

Description

Exascale data management project

 

SAGE-2 (Percipient StorAGe for Exascale Data Centric Computing 2) will create a next-generation data storage system to enable extreme scale computational workflows. The project will build on the Mero object store to create a system where data-centric computing can be undertaken, moving compute to data rather than data to compute.

Relevance

Exascale data management processes

Core Partner Involvement

EPCC

Integrating new non-volatile memory technologies with the object store and storage hierarchy, and enabling byte-level access to data for applications.

 

SIRIUS

Time Frame

Jan 2019 – December 2024

Description

 

Cancer and bacterial infections are projected to kill 18 million people worldwide annually by 2050, which creates a pressing need for fast and reliable diagnostics to enable early and targeted treatments.

 

Microfluidics plays a key role in the miniaturisation of disease diagnostics, which in turns makes possible portable and low-cost point-of-care devices.

 

The SIRIUS project will numerically investigate the underlying physical mechanisms and develop the first predictive toolkit for engineering applications of IPMF. In particular, the effects of cell softness, finite cell concentration and the challenging behaviour of small particles (such as bacteria or blood platelets) will be explored.

 

SIRIUS will pursue an innovative simulation campaign based on the Lattice-Boltzmann Method, validated with experimental data, to generate both physical insight and shortcut methods for simulation-driven design.

Relevance

Microfluidics, health, disease diagnosis

Core Partner Involvement

EPCC

Tech provider

 

STriTuVaD

Time Frame

Feb 2018 – Jul 2022

Description

DEVELOPING IN SILICO TRIALS TO FIGHT TUBERCULOSIS

 

Tuberculosis is one of the world’s deadliest diseases: it infects one third of the world’s population in developing countries and it is becoming very dangerous in developed countries as well.

 

The high costs, long duration and poor compliance with the therapy, may lead to the development of multidrug-resistant bacterial strains, which makes it much harder to eradicate this disease. The STRITUVAD project aims to develop computer simulations to test the efficacy of new therapies, significantly reducing costs and duration of human clinical trials.

 

https://www.strituvad.eu/ 

Relevance

The core model used in STRITUVAD is an agent-based code (Universal Immune System Simulator), which poses peculiar problems in terms of scalability. The consortium is working on a GPU version of the code; when completed we consider to invite the model developer, University of Catania as associate partner in CBM2, and add UISS-GPU among our portfolio of highly scalable solutions for computational medicine.

 

Core Partner Involvement

UNIBO

WP6 (Dissemination) leader, marco.viceconti@unibo.it

UNIBO Is involved in STriTuVaD in silico trial design, and STriTuVaD tuberculosis vaccine is proposed as a test case for an in silico augmented clinical trial in CompBioMed2 Subtask 2.3.3 (Virtual patients’ expansions).

 

VECMA

Time Frame

Jan 2018 – Dec 2022

Description

Verified Exascale Computing for Multiscale Applications

 

The purpose of the VECMA project is to enable a diverse set of multiscale, multiphysics applications to run on current multi-petascale computers and emerging exascale environments with high fidelity such that their output is “actionable”. That is, the calculations and simulations are certifiable as validated (V), verified (V) and equipped with uncertainty quantification (UQ) by tight error bars such that they may be relied upon for making important decisions in all the domains of concern. The central deliverable is an open source toolkit for multiscale VVUQ based on generic multiscale VV and UQ primitives, to be released in stages over the lifetime of this project, fully tested and evaluated in emerging exascale environments, actively promoted over the lifetime of this project, and made widely available in European HPC centres.

 

VECMA in a Nutshell

 

Computer simulations are being used to predict the weather and climate change, model refugees, understand materials, develop nuclear fusion, and inform medical decisions. But if we are to use simulations in order to make predictions on the global climate emergency, guide aid to migrants fleeing combat, create new materials, help invent the first fusion reactor, and allow doctors to test medication on a virtual you (before the real you), then those simulations need to be reliable. In other words, they need to be validated, verified, and their uncertainty quantified, so that they can feed into real life applications and decisions. The VECMA project is developing software tools in order to validate, verify, and quantify the uncertainty on each of these simulation applications, and many besides.

 

https://www.vecma.eu/

 

Core Partner Involvement

CBK

Board member

UvA

PI, Board member

UCL

PI

Board member

LRZ

Board member?

UNIBO

Associate partner, marco.viceconti@unibo.it.

SURF

Advisory board member.

 

Collaborating for deployment of the VECMA-tk on housed HPC system.

BULL/ATOS

Core partner

In charge of VECMA-tk scalability aspects

 

In Silico World

Time Frame

Jan 2021 – Dec 2024

Description

Lowering barriers to ubiquitous adoption of In Silico Trials(ISW)

 

The  overall  aim  of  the InSilicoWorld(ISW)  project  is  to  accelerate  the  uptake  of  modelling  and  simulation technologies for the development and regulatory assessment of medicines and medical devices (hereinafter referred generically as In Silico Trialstechnologies), by developing innovative solutions that address the main barriers to their uptake. Ultimately, the consortium expects theresults of the ISW project to accelerate the adoption of In Silico Trials,  increase  the  trust  in  these  innovative  technologies  by  the  main  stakeholders  (mainly  medical  industry, regulatory  agencies,  clinicians,  healthcare  providers,  healthcare  payers,  policy  makers),  change  the  design  of regulatory trials to include in silico technologies, and consolidate the regulatory pathways based on In Silico Trials. The long-term impactwill be the reduction of the costs and duration of the development and regulatory assessment of new medical products, while maintaining or improving the level of safety provided by conventional approaches. This innovation is also expected to change the process in ways that will allow for the first time to learn from failures, providing a causal explanation to the lack of safeness or of effectiveness of the new product, which hopefully can guide to some redevelopment rather than restarting from scratch, as it is done nowadays.

 

http://insilico.world/

Relevance

Overlap of the computational biomedicine domain

Core Partner Involvement

UvA

PI, Board member

 

UNIBO

Coordinator, marco.viceconti@unibo.it

WP1 (Coordination and Management) and WP10 (Ethics requirements) Leader, Task Leader

 

VESTEC

Time Frame

?

Description

VESTEC is a European funded project that builds a flexible toolchain to combine multiple data sources, efficiently extract essential features, enable flexible scheduling and interactive supercomputing, and realise 3D visualization environments for interactive explorations by stakeholders and decision makers.

 

VESTEC will develop and evaluate methods and interfaces to integrate high-performance data analytics processes into running simulations and real-time data environments. Interactive ensemble management will launch new simulations for new data, building up statistically more and more accurate pictures of emerging, time-critical phenomena. Innovative data compression approaches, based on topological feature extraction and data sampling, will result in considerable reductions in storage and processing demands by discarding domain-irrelevant data.

Relevance

They offer urgent computing solutions

Core Partner Involvement

EPCC

Co-PI, WP leader

 

EUPEX

Time Frame

Jan 2022 – Dec 2025

Description

The EUPEX pilot brings together academic and commercial stakeholders to co-design a European modular Exascale-ready pilot system. Together, they will deploy a pilot hardware and software platform integrating the full spectrum of European technologies, and will demonstrate the readiness and scalability of these technologies, and particularly of the Modular Supercomputing Architecture (MSA), towards Exascale.

 

EUPEX’s ambition is to actively support the European industrial ecosystem around HPC, as well as to prepare applications and users to efficiently exploit future European exascale supercomputers.

Relevance

Key project for future exascale hw and sw.

Core Partner Involvement

Atos

Coordinator and deeply involved in both hw and sw aspects including co-design on the selected set of applications.

 

VPH Institute / Avicenna Alliance

Time Frame

2010 – ….

Description

The emerging Community of Practice working on computational biomedicine has been represented since 2010 by a not-for-profit organisation known as VPH Institute.  The institute mission is to ensure that in silico medicine technologies are fully realised, universally adopted, and effectively used both in research and clinical practice.  Founded by a CBM2 investigator Prof Marco Viceconti, the institute is currently led by Prof Liesbet Geris.  Supporting members include INRIA, University of Auckland, the Insigneo Institute, the National University of Ireland, the Luxembourg Centre for Systems Biomedicine, the Norwegian University of Science and Technology, the Agencia De Qualitat I Avaluacio Sanitaries De Catalunya, and the University College London. 

 

Since 2016 the VPH Institute drove the creation of a twin organisation, established as a separate legal entity, named the Avicenna Alliance.  Where the VPH Institute represents the academic organisations working in computational medicine, the Alliance represents the industrial organisations active in the field.

 

https://www.vph-institute.org/

https://avicenna-alliance.com/

 

Relevance

The VPH Institute and the Avicenna Alliance are collaborating closely with CBM2 around the dissemination and the engagement.

 

Core Partner Involvement

UNIBO

Member, marco.viceconti@unibo.it

 

3D-PLTS

Time Frame

June 2022 – May 2025

Description

The 3D-PLTS is a Eureka-Eurostar industrial project whose goal is to design and commercialize a new medical device to determine platelets function and, in case of pathology, to identify which platelet property is affected (adhesion rate aggregation rate, spreading property,…). This project has three academic partners, ULB, Fraunhofer Institute and UNIGE. UNIGE brings the expertise developed in CompBioMed to describe platelets transport in blood and the modeling of the deposition process.

 

Relevance

A industrial outcome of the knowledge developed in CompBioMed.Demonstrate the need of HPC simulations to develop better platelets analyzer device for a clinical use.

Core Partner Involvement

UNIGE

Simulation of the design of the medical device, and software to infer platelet properties from their deposition patterns.

 

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