AGH University of Science and Technology, Krakow, PL

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Presentation transcript:

AGH University of Science and Technology, Krakow, PL From the grid medical consortium, through virtual labs and bioinformatic environments, to the center of excellence for personalized diagnostics and medical therapy Marian Bubak Academic Computer Centre Cyfronet, Department of Computer Science, AGH University of Science and Technology, Krakow, PL http://dice.cyfronet.pl/; bubak@agh.edu.pl

Area of Research Investigation of methods for complex scientific collaborative applications Elaboration of environments and tools for eScience Integration of large-scale distributed computing infrastructures Knowledge-based approach to services, components, and their composition

Grid Environment for Interactive Applications Expanding the Grid for a new category of applications in medicine, environmental control, and physics, running interactively, and extending the Grid infrastructure across eleven European countries. Efficient development of these kinds of applications on the Grid required new tools for verification of source code, performance prediction, evaluation and on-line analysis. The Grid was equipped with new components for monitoring of application performance, efficient distributed data access, specific resource management, as well as portals and mobile personalised user interfaces. 5FP EU Project – CrossGrid, 2002-2005 http://cordis.europa.eu/pub/ist/docs/grids/crossgrid_achievement.pdf

ViroLab Virtual Laboratory Design of a laboratory for virologists, epidemiologists and clinicians investigating the HIV virus and the possibilities of treating HIV-positive patients Based on notion of in-silico experiments built and refined by cooperating teams of programmers, scientists and clinicians Employed full concept-prototype- refinement-production circle for virology tools Set of dedicated yet interoperable tools bind together programmers and scientists for a single task Support for system-level science with concept of result reuse between different experiments 6FP EU Project – ViroLab, 2006-2009 http://www.virolab.org/

VPH-Share EU 7FP Project VPH-Share project has developed the organisational fabric (the infostructure) and integrated the optimised services to expose and share data and knowledge, jointly develop multiscale models for the composition of new VPH workflows, facilitate collaborations within the VPH community VPH-Share provides the essential services, as well as the computational infrastructure, for the sharing of clinical and research data and tools, facilitating the construction and operation of new VPH workflows, and collaborations between the members of the VPH community. http://www.vph-share.eu ; http://dice.cyfronet.pl

Infostructure for VPH

VPH-Share federated cloud

The EurValve EU H2020 Project Valvular heart disease currently affects 2.5% of the population; it is overwhelmingly a disease of the elderly. EurValve will implement, test and validate a modelling based decision support system (DSS) for aortic and mitral valve diseases that allows simulating, comparing and understanding the effects and risks of different treatment strategies. The DSS will improve knowledge of disease mechanisms by applying a holistic assessment of cardiovascular function that includes haemodynamic data at all cardiovascular compartments (ventricle, valve, vessels) and multiscale components that couple organ with cell function. http://www.eurvalve.eu ; http://dice.cyfronet.pl

From Research Environment to DSS Research Computing Infrastructure Development of models for DSS Clinical Computing Environment Real-time Multiscale Visualization Model Execution Environment Data Collection and Publication Suite DSS Execution Environment ROM 0-D Model Patient Data ROM 3-D Model Model A Images Population data Security System Model B Infrastructure Operations Data Source 1 Data Source 2 HPC Cluster Cloud Workstation Provide elaborated models and data for DSS

ROM and sensitivity analysis Data and action flow consists of: full CFD simulations sensitivity analysis to acquire significant parameters parameter estimation based on patient data uncertainty quantification of various procedures The flow of CFD simulations and sensitivity analysis is part of clinical patient treatment

Flow of medical data Secure locally hosted service BLOB Data handled based on the confidentiality level: Step 1 (all levels) – data is sent via encrypted channel to the service Step 2-3 (high) – data encrypted and stored on disk Step 4-5 (high) – data decrypted and retrieved Step A-B (lo) – data stored directly to disk Step 6 (all) – data sent back to the user DB Records: Step 1b – data are stored via the encrypted channel to the DB service in secured location Step 2b – data are retrieved from the service via encrypted channel REST (1b) (2b) SQL Database access

Model Execution Environment API – Application Programming Interface REST – Representational state transfer Rimrock – servis used to submit jobs to HOC cluster Atmosphere – provides access to cloud resources git – a distributed revision control system

http://dice.cyfronet.pl/ ; http://dice-cyfronet.github.io/#history DICE Team skillset Interactive compute- and data-intensive applications, knowledge-based workflow composition, programming models CrossGrid, K-Wf Grid, CoreGRID 2002-2008 Script-based composition of applications, GridSpace Virtual Laboratory ViroLab, GREDIA 2006-2009 Federating cloud resources for VPH compute- and data-intensive applications, DataNet – metadata models VPH-Share, PL-Grid 2009-2015 Common Information Space for Early Warning Systems, big data storage and access, analysis tools UrbanIFlood, ISMOP 2009-2016 Computational strategies, software and services for distributed multiscale simulations MAPPER 2010-2013 Executable Papers; 1st prize in Elsevier competition at ICCS2011 (Elsevier follow-up project) Collage 2011-2013 Optimization of workflow applications on cloud resources PaaSage 2013-2016 Infrastructure for large-scale simulations in medicine EurValve 2016-2019 Providing computing solutions for exascale challenges PROCESS 2017-2020 http://dice.cyfronet.pl/ ; http://dice-cyfronet.github.io/#history

Centre for New Methods in Computational Diagnostics and Personalised Therapy

Mission of CECM Project CECM EU H2020 „Teaming for Excellence” project develops a Business Plan to establish in Krakow a European Centre of Excellence for computational medicine. The CECM Consortium is going to build a world-class centre of excellence, attractive to foreign partners, with a significant impact at both regional and national scales, providing benefits for the pan-European society. It is a consortium of leading European science and innovation institutions in all domains of the new CoE. ACC Cyfronet AGH has a long record of efficient support for scientists in the computational life science EU and PL research projects. Małopolska and Kraków are well positioned for a key role in the computational medicine.

CECM Partners Leading European science and innovation institutions: University of Sheffield and Insigneo Institute – experts in translation of in silico modelling and simulations to clinics Forschungszentrum Jülich – experts in modern HPC and data techniques, applied for science and industry Fraunhofer ISI – experts in systemic multi-domain solutions and innovation in healthcare They will work together with Partners from Poland: ACC Cyfronet AGH – experts in simulation and provisioning computing infrastructure for science Klaster LifeScience Kraków – Poland’s top cluster of industry, academia and hospitals for the life science domain NCBiR - the Polish National Centre for Research and Development (project coordinator)

Regional Position Poland / Kraków are well positioned for a key role in computational medicine: Kraków educates large numbers of medical and IT professionals There is a high concentration of research hospitals in and around the city The entrepreneurial community has entered a phase of rapid growth. ACC Cyfronet AGH, the project leader: Has a long record of efficient support for PL/EU computational scientists Already hires personnel experienced in computational life science domain Provides considerable set of hardware and software resources Proved the capacity to act as a leader for large scientific projects. .

Objectives of the New CoE A: Development of new computation-based solutions for diagnostics and therapy in daily healthcare. B: Systematic involvement of regional biomed businesses, specialising in technologies and services for personalised medicine, in high-profile research projects and clinical adoption of their outcome. C: Development of education initiatives to train knowledge workers with the skills in data analytics, simulation, and HPC/Big Data, to respond to the growing demand for skilled workforce in medical devices and bio-engineering. D: Strong advancement of algorithms, models and technologies involved in personalised medicine, including design of holistic, replicable, generic framework for simulation-based Decision Support Systems (DSS) creation.

CoE Value Chain Centre’s Customers come from two distant sides of the value chain The aim is a replicable, refined workflow, rather than a single system Bridging the gap will locate the Centre as a crucial actor of the process Re-iteration as an inherent excellence-building mechanism

CoE Methodology

CECM Partnership

Model and sources of funding Centre activity Model and sources of funding Opportunities and potential assessment Initially by the Teaming Phase 2 EC Grant and Polish matching program, later by both public and industrial revenue streams. DSS creation R&D Research grants (both EU and PL) acquired for development of specific medical methods. Late-TRL projects will be covered by cooperation with industry. Promotion, dissemination and impact maximisation Individual projects’ dissemination and exploitation budgets. Limited Teaming Phase 2 funding is expected for some activities (e.g. managing technology transfer databases, organising showcases and workshops etc.). Support: HPC, IT, HR, accounting, legal and similar. Polish and regional structural funds (hardware, offices, etc.) and individual projects’ overhead (personnel). Our precise alignment with the national and regional Smart Specialisation is an important enabling factor . Overall management and governing structure Individual project management will be covered from the project’s management budgets. Management of the Teaming Phase 2 Project will be covered from the Teaming budget. IP management will be funded from revenue.

Call for Collaborators Industrial large & small enterprises present in the clinical DSS value chain in all stages, from product conception to certification and deployment Clinical pilot studies with retrospective and prospective patient data common projects on the most demanding clinical cases Scientific simulation aiming at assisting personalised therapy computation- and data-based diagnostic tool

Summary http://dice.cyfronet.pl/projects/details/CECM bubak@agh.edu.pl The CECM will exploit these advantages to build a world-class centre of excellence, attractive to foreign partners, and having a significant impact at both regional and national scales, with lasting benefits for pan-European society. http://dice.cyfronet.pl/projects/details/CECM bubak@agh.edu.pl