Directory of EU HPC e-Infrastructure

6 December, 2023.

Table of Contents

Introduction

Access to European Research Infrastructures

Summary of European Research Infrastructures

Initiatives, strategies and networks

Funding opportunities

List of EU e-Infrastructures with CompBioMed Core Partner involvement.

Introduction

Throughout CompBioMed and until 30th of November, 2023, this document served as a central repository of all HPC e-Infrastructure bodies available to CompBioMed partners, both Core and Associate.  This document was designed to enable application development.

 

There is a sister document, our current collaborations/partnerships of CompBioMed2 Core Partners, which can be found here. This document promotes synergy and thereby fosters our relationships.

Access to European Research Infrastructures

Summary of European Research Infrastructures

The following information is a summary, including corrections, of the ERI page: https://ec.europa.eu/info/research-and-innovation/strategy/strategy-2020-2024/our-digital-future/european-research-infrastructures_en

 

NB The two key programmes for CompBioMed are ESFRI and EOSC, where these programmes are expanded and links provided within the following subsection, and are highlighted in bold font.

Initiatives, strategies and networks

  • European Strategy Forum on Research Infrastructures (ESFRI)
    • The ESFRI develops a strategic roadmap identifying investment priorities in European Research Infrastructures for the next 10-20 years.
    • https://www.esfri.eu/
      • Events: https://www.esfri.eu/esfri-events
      • Data & Computing: https://www.esfri.eu/data-computing
      • Supercomputing resources for Covid19 research: https://www.esfri.eu/covid-19-action-categories/supercomputing
      • Health & Food: https://www.esfri.eu/health-food, including but not limited to the following
        • EU-IBISBA
          • European Industrial Biotechnology Innovation and Synthetic Biology Accelerator
          • An accelerator for research and innovation in Industrial Biotechnology and Synthetic Biology
        • ISBE
          • Infrastructure for System Biology Europe
          • A coordination effort to interconnect the best experimental and modelling facilities for Systems Biology
        • BBMRI ERIC
          • Biobanking and BioMolecular Resources Research Infrastructure
          • A gateway for access to biobanks and biomolecular resources for health research
        • EATRIS ERIC
          • European Advanced Translational Research Infrastructure in Medicine
          • A new development pathway for translating novel biological insights into effective solutions
        • ECRIN ERIC
          • European Clinical Research Infrastructure Network
          • A network for multinational, high-quality, clinical trials for top-level medical research
        • ELIXIR
          • A distributed infrastructure for life-science information
          • A sustainable infrastructure for interoperability of public biological and biomedical data resources
        • ERINHA
          • European Research Infrastructure on Highly Pathogenic Agents
          • The European coordination for the study of highly pathogenic micro-organisms classified as RG4
        • EU-OPENSCREEN
          • European Infrastructure of Open Screening Platforms for Chemical Biology
          • The high-throughput screening platforms and chemistry resources for Life Sciences
        • Euro-BioImaging
          • European Research Infrastructure for Imaging Technologies in Biological and Biomedical Sciences
          • The large-scale open physical user access to state-of-the-art biological and biomedical imaging technologies
        • INFRAFRONTIER
          • European Research Infrastructure for the generation, phenotyping, archiving and distribution of mouse disease models
          • A collection of services on mouse models to unravel the role of gene function in human health and disease
        • INSTRUCT ERIC
          • Integrated Structural Biology Infrastructure
          • A peer-reviewed access to a broad range of technology, expertise and training in structural biology
        • European Open Science Cloud (EOSC)

Funding opportunities

List of EU e-Infrastructures with CompBioMed Core Partner involvement.

 

This main section is updated by all Core Partners of CompBioMed, and lists their involvement with key EU Infrastructures, including CoEs, exascale hardware, AI and ML, data management, end-users, and user communities in software, hardware, AI and ML, plus other bodies.

 

  • 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, DEEP-SEA , 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
      • AIDD
      • PRIMAGE, VESTEC
      • SilicoFCM
      • In Silico World
      • EDITH
    • Data Management
      • DICE, ELIXIR, EOSC Hub, EUDAT, EXSCALATE4CoV (E4C), LEXIS, RDA, VESTEC
    • User Communities
      • End-Users
        • Hospitals
        • VPH Institute
        • Avicenna Alliance (industries)
        • Others
          • HEALTH-RI, IRB, Mobilise-D
        • 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, RAMP
          • AI/ML
            • HEALTH-RI
            • OpenMM
            • Groq, Cerebras Systems
          • Data Management
            • Bavarian Genomes, DigiMed Bayern, HEALTH-RI

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.

 

Home

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 – December 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.

 

JAN

Janssen is a core partner in this project with multiple PhD students co-supervised with universities of Helmholtz Zentrum München, TU Dortmund, Univ of Linz, Univ Luxembourg, and Aalto University. Topics of study include chemical synthesis prediction, analysis of microscopic imaging for molecular design, QM learning for reactivity prediction, interpretable latent representations for toxicity prediction etc.

 

EVO

Evotec (EVO) is a core partner that is involved in development of the multiple AI/ML-, QM-based tools and quantum computing platform for drug discovery. EVO supports and co-supervised PhD students with UCL, University of Manchester and University of Suffolk that are involved in the further development of these approaches and converting them into fully automated algorithms, prepared for HPC and exascale machines. These approaches open a new avenue of applying these methods to the design and analysis of the large numbers of lead-like molecules drug candidates.

 

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 is 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.

 

 

 

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 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.

 

Home

Relevance

Modelling and simulation in cardiology

Core Partner Involvement

BSC

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

 

 

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 utilisation 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 Centre 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/

Relevance

Might this project provide potential clients?

Core Partner Involvement

EPCC

Collaborating as Excellerat member in workshop including co-design

 

CoE CoEC

Time Frame

Jan 2018 – Dec 2022

Description

The Center of Excellence in Combustion (CoEC) has been created in order to apply exascale computing technologies to promote and develop advanced simulation software that can support the decarbonisation goals of the European Union within the energy and transportation sectors.

 

https://coec-project.eu/

Relevance

It supports developments in Alya which can impact biomedical applications.

Core Partner Involvement

BSC

BSC coordinates this project. Alya is used as an exascale core simulation software for the combustion modelling vertical.

 

 

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/

Relevance

Might this project provide potential clients?

Core Partner Involvement

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.

 

Home Page

Relevance

It supports developments in Alya which can impact biomedical applications.

Core Partner Involvement

BSC

Alya is used as an exascale core simulation software for modelling windmills and wind farms in the vertical of clean energy production.

 

 

 

CoE ESiWACE2

Time Frame

Jan 2018 – Dec 2022

Description

for future exascale weather and climate simulations

 

https://www.esiwace.eu/

Relevance

Might this project provide potential clients?

Core Partner Involvement

 

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.

 

Homepage

Relevance

It supports developments in Alya which can impact biomedical applications.

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

Might this project provide potential clients?

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

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/

Relevance

Might this project provide potential clients?

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/

Relevance

Might this project provide potential clients?

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/

Relevance

Might this project provide potential clients?

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

Project coordinator. In PoP there is no CBM2 staff involved.

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

Relevance

Might this project provide potential clients??

Core Partner Involvement

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

Might this project provide potential feedback on the use of HPC at scale??

Core Partner Involvement

Atos

Co-design with EUPEX.

CPPBioSim

Time Frame

December 2020 – November 2025

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/

Relevance

Might this project provide potential clients??

Core Partner Involvement

UCL

Member of Management Group

UOXF

Member of Management Group

UEDIN (not EPCC)

Member of Management Group

 

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. There is no CBM2 staff involved.

EPCC

Core Partner

LRZ

Core Partner; data centre database – monitoring and analytics

 

DEEP-SEA

Time Frame

1 April 2021 – 31 March 2024

Description

DEEP-SEA (DEEP – Software for Exascale Architectures) will deliver the programming environment for future European exascale systems, adapting all levels of the software stack to support highly heterogeneous compute and memory configurations. It will also allow code optimisation across existing and future architectures and systems. The software stack includes low-level drivers, computation and communication libraries, resource management, and programming abstractions with associated runtime systems and tools.

 

Home page

Relevance

Useful in co-design

Core Partner Involvement

BSC

Core Partner. There is no CBM2 staff involved.

EPCC

Core Partner

LRZ

Core Partner

ATOS

Core Partner

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.

 

 

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 usecase in DICE.

 

ELIXIR

Time Frame

? – ?

Description

ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. These resources include databases, software tools, training materials, cloud storage and supercomputers.

 

The goal of ELIXIR is to coordinate these resources so that they form a single infrastructure. This infrastructure makes it easier for scientists to find and share data, exchange expertise, and agree on best practices. Ultimately, it will help them gain new insights into how living organisms work.

 

https://elixir-europe.org/

 

Relevance

Life science data management.

Core Partner Involvement

 

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

Might this project provide potential clients?

 

EOSC Marketplace includes a listing of CompBioMed services

 

Core Partner Involvement

UCL

Applied for EOSC project

Partner in EOSC-dih

EPCC

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/

Relevance

Might this project provide potential clients?

Core Partner Involvement

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.

 

Home

Relevance

Might this project provide potential clients?

Core Partner Involvement

SURF

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

ATOS

Coordinator and Core Partner.

BSC

Core Partner. There is no CBM2 staff involved.

 

 

EPiGRAM-HS

Time Frame

Jan 2018 – Dec 2022

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/

Relevance

Might this project provide potential clients?

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 stages of the design process.

 

https://www.eudat.eu/

Relevance

Might this project provide potential clients?

Core Partner Involvement

EPCC

Paid end-user as CompBioMed partner

 

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.

 

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Relevance

Might this project provide potential clients?

Core Partner Involvement

 

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.

 

 

Driven by data, developed for patients

Relevance

Collaboration for ML/DL workflows in clinical setups.

Core Partner Involvement

SURF

Core partner of the project.

Collaboration: Working towards Examode becoming a 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.

 

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Might this project provide potential clients?

Core Partner Involvement

EXDCI-2

Time Frame

Mar 2018 – Dec 2020

 

Description

Through the joint action of PRACE and ETP4HPC, EXDCI-2 mobilises the European HPC stakeholders. The project builds upon the achievements of EXDCI and will continue its participation in the support of the European HPC Ecosystem.

 

https://exdci.eu/

 

Core Partner Involvement

Core Partners participating as PRACE-linked third parties include: BSC, EPCC, UCL

 

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/

Relevance

Might this project provide potential clients?

Core Partner Involvement

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”.

 

 

FENIX

Time Frame

2020 – currently running.

Description

The ICEI (Interactive Computing E-Infrastructure for the Human Brain Project) project is funded by the European Commission under the Framework Partnership Agreement of the Human Brain Project (HBP). In the project five leading European Supercomputing Centres are working together to develop a set of e-infrastructure services that will be federated to form the Fenix Infrastructure.

Relevance

Useful list of both current and planned HPC resources

https://fenix-ri.eu/infrastructure/resources

 

Within WP5 Container to HPC task, USFD has applied for access to both VM and Scalable Computing infrastructure (Call #11). Application is currently under review (as of Nov 2022).

Core Partner Involvement

BSC

 

 

 

ff4EuroHPC

Time Frame

Sept 2020 – yy nnnn

Description

Promoting HPC to EU SMEs

 

Essentially, this is fortissimo3 (https://www.fortissimo-project.eu/) including software, hardware and expertise, where the Fortissimo Marketplace provided one-stop, pay-per-use, on-demand access to advanced simulation, modeling and data analytics resources including software, hardware and expertise.   ff4EuroHPC no longer offers pay-per-use.

 

Relevance

Fortissimo2 was an exemplar in one-stop-shop provider of HPC services.

 

Core Partner Involvement

ATOS

EPCC was in Fortissimo2.

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.

Relevance

It supports developments in Alya which can impact biomedical applications.

Core Partner Involvement

BSC

BSC is a core partner. Alya is used as an exascale core simulation software for turbulence modelling .

 

 

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

 

 

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 realising in silico clinical stroke trials in which stroke and treatment are modelled. 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 realise 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 finalised 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 modelling in laboratories. The in silico models and virtual populations will be combined to simulate clinical trials and validated by simulating and comparing finalised 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, UNIGE

Support simulation campaign to be able to build surrogate models for so-called thrombectomy treatments to run on Snellius. 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.

 

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Might this project provide potential clients?

Core Partner Involvement

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

Relevance

Might this project provide potential clients?

Core Partner Involvement

 

 

 

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 realise 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 centres 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.

 

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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. CBM2 must 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 centres 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

Attended ‘next steps’ telco

SURF

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

LRZ

Lexis Partner: WP3 Leader and T3.3 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 modelling and simulation. Its Python API makes it widely popular as both an application (for modellers) 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 modelling 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 modelling and simulation, much as deep learning has transformed computer vision.

Funding sources:

  • May 2020-April 2021Chan Zuckerberg Initiative Essential Open Source Software for Science grant.
  • July 2021 – Mar 2025 award nr R01GM40090 of the NIH

URL: http://openmm.org/

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

 

ParOSol

Time Frame

From 2011 to present

Description

ParOSol is a scalable memory efficient multigrid solver for performing micro-finite element (mFE) analyses of bone samples. mFE analyses has been used since the early 1990s, and current clinical imaging devices (e.g. Scanco XtremeCT II) include mFE software to aid clinical decision making. This is possible because in addition to the morphological measures obtained directly from the images, mFE provides mechanical measures such as bone stiffness and strength.

 

The state of the art of this class of mFE software was significantly advanced by ParOSol. The original version of this code was developed at ETH Zürich (Prof Peter Arbenz group, PhD work of Cyril Flaig) and has been available since 2011. It included a host of new features, such as a linearised Octree with pointer-less traversal, matrix-free form of matrix-vector multiplication, use of the HDF5 file format and Eigen and MPI libraries, geometric multigrid preconditioning, defining interface functions using C++ templates and domain partitioning and load balancing across parallel processors. As such, it leverages compile-time code optimisation, and achieves a significantly smaller memory footprint than predecessor mFE codes. Scalability tests reported by Dr Flaig and conducted on a Cray XT-5 supercomputer demonstrated nearly perfect scalability up to 8000 cores and good weak scaling up to 32000 cores.

 

Currently, ParOSol is being used and actively developed by research groups across Europe and the UK. Recent developments in research groups based in (but not limited to) ETH Zürich, KU Leuven, TU Vienna have led to predictions of bone remodelling, detection of fracture planes, accounting for material nonlinearity. At USFD, the group of Pinaki Bhattacharya has added to ParOSol the ability to predict contact interactions between two bodies (such as between bones at an articular joint). This has required redefining input/output structures in HDF5 format, redefining some matrix-vector operations, including increment and iteration loops around the core solver. We are collaborating with KU Leuven, ETH Zürich and TU Vienna to develop a common specification for the HDF5 file format with a view to facilitate information exchange between research groups using/developing ParOSol. With an outlook to VV we are contributing to robust software verification using the method of manufactured solutions. Funding from UKRI has been secured to develop spectral stochastic mFE capability in ParOSol with the aim to improve UQ in animal testing of osteoporotic drugs.

 

Funding sources:

EPSRC New Investigator Award Grant No. EP/V050346/1

Relevance

Software

Core Partner Involvement

USFD

 

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 paediatric 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 – Jun 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.

 

PRACE HOME

Relevance

Might this project provide potential clients?

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.

 

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).

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

 

 

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 2021

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.

 

Home New

Relevance

Might this project provide potential clients?

 

CBM is an associate partner

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 Trials technologies), by developing innovative solutions that address the main barriers to their uptake. Ultimately, the consortium expects the results 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 impact will 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.

 

Homepage

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

EDITH

Time Frame

Oct 2022 – Aug 2024

Description

The overall aim of the EDITH project is to foster an inclusive ecosystem for Digital Twins in healthcare in Europe and to prompt the convergence of such an ecosystem towards a common strategy conducive to its further development. This will be achieved by mapping and analysing the status of the fields which are crucial for the growth, uptake and use of digital twins in healthcare, including in silico medicine, health data, HPC, etc.

A vision for the integrated human digital twin will be developed and a roadmap to realize this vision will be articulated. Additionally, a federated cloud-based repository will be established, to bring together currently available resources and best practices. The ecosystem will be leveraged to create a repository catalogue with available resources and recruit resources into the repository during the project. Conditions for integration in the repository in terms of required standards, regulations, meta-data, etc. will be identified.

Finally, building on available infrastructure, a framework for a simulation platform will be put forward with pre-selected prototypes demonstrating proof of concept. User communities (research, industry, patients and healthcare professionals) will be actively involved in the process to ensure their needs are built into the architecture. Several activities will focus on the commercial exploitation of (parts of) the repository and simulation platform.

Throughout the entire EDITH action, the community, its stakeholders and relevant international partners will be consulted via advisory boards, public meetings, community challenges and other public activities in order to firmly establish a durable ecosystem allowing to realize the vision of the integrated digital twin for personalized healthcare.

 

Home

Relevance

Defining the necessary ecosystem to many of the biomedical simulations.

Core Partner Involvement

UvA

PI, Core partner

BSC

Core partner

 

 

 

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 visualisation 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.

BSC

BSC’s spinoff company ELEM Biotech (associate partner of CBM2) is a member,

 

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