We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byRobert Skye
Modified over 2 years ago
© 2006 STEP Consortium Multiscale Modelling Strand
© 2006 STEP Consortium Multiscale Modelling
© 2006 STEP Consortium Multiscale modelling is a quantitative, integrative and experimentally based approach for studying biological processes and dynamics that span multiple spatial (typically nanometers to meters – 10 9 ) and temporal (typically microseconds to decades – 10 15 ) scales with the view to transfer knowledge and information across scales, as well as support modularity and interactivity.Definition
© 2006 STEP Consortium Validation against experimental data. Verification through numerical experi- ments, mathematical analysis and access to models/data used in publications (open for scrutiny). Low level details can be approximated at higher levels, but ideally key parameters (that are experimentally verifiable) are ported to higher levels. Validation and Integration
© 2006 STEP Consortium Structural, simulation and functional data. From 100s of MB to a few TB of data. Remote storage of data, executables and metadata access speed. Data safety and security (must be transpa- rent to the user). Visualisation using OpenGL-based rendering software. Need a repository for clinical data (legal and ethical issues, as well as reluctance from some to share data).Data
© 2006 STEP Consortium Data transfer and storage/access. Not fast/convenient. Need access to structu- ral/functional data. Need more useable and user-friendly mo- delling environments (e.g. access to HPC facilities). Proper software engineering approach. ICT Issues
© 2006 STEP Consortium Position Paper
© 2006 STEP Consortium Favour research that requires interaction between in silico, in vitro and in vivo work. Training of interdisciplinary scientists to support computational systems biology. Develop proofs of concept (i.e. demos). Apply ideas and concepts from non- biological sciences to biology. Address problems inherent to peer- reviewing of interdisciplinary projects. Set up of a modelling network to share ideas, expertise, results, etc. Common Objectives (1)
© 2006 STEP Consortium Support efforts where the added value of modelling can easily and quickly be shown. Demand online depository of any model-ling tools and data used in published works. Promote the co-location and close inte- gration of interdisciplinary fields. Define tools and processes which will get experimentalists involved. Support exchange of specialists. Common Objectives (2)
© 2006 STEP Consortium Facilitate discussion of existing published models. Standardise practices in terms of modelling development, data, etc. Focus on what Europe can improve or do better rather than reinvent the wheel. Common Objectives (3)
© 2006 STEP Consortium Experimentalists: modelling may help them both in their research and teaching. Modellers: agree on standards (to facilitate exchange of models of various dimensions). Industry and health policy makers: demos where modelling has paid off. Clinicians: modelling can benefit patient care (insight into disease mecha-nisms, aid diagnosis and treatment). Society: 3 Rs (Replacement, Reduction and Refinement) and personalised medicine. Common Objectives (4)
© 2006 STEP Consortium Physiome Project, i.e. development of quantitative and integrative models descri- bing life from conception to death and from genes to whole organism. Wherever possible, these models have to be human specific Improve health. Need an even stronger interaction between experimentalists and modellers. Low level mechanisms are important, but what actually matters is the organism. Research Challenges
© 2006 STEP Consortium Financial: need to shift from a reductionist to an integrative approach to science. Human: mathematicians with a biological background, life scientists with a mathema- tical background, and biomathematicians. Infrastructural: modelling and computing platforms, supercomputers, especially desi- gned knowledge bases, as well as mecha- nisms for sharing data, ideas, expertise, results, etc. Resources Required
© 2006 STEP Consortium Modelling as such doesn’t raise ethical/ legal issues, it’s what you do with it that does. Have to take responsibility for what a model could be used for, but difficult to enforce. Ethical, Legal and Gender Issues (1)
© 2006 STEP Consortium Low level of interest from some stakehol- ders. We are too few to tackle the issues at hand. Mathematical/computational limitations. Lack of collaboration between research groups. Potential lack of interest from the industry. Our incomplete knowledge of the biological mechanisms we are trying to model. Ethical, Legal and Gender Issues (2)
© 2006 STEP Consortium Need knowledge bases (experimental data, models and modelling environments). Computing platforms and resources. Thematic and technical networks. Have output that demonstrates the effect we are having on wealth/health. Have to be familiar with the metrics used by local governments. Set of standards, quality rules/assurance. Mirror of repository. Organisational Model
© 2006 STEP Consortium Easy “opt-in” procedures for interested parties. Strategy with specific agenda and deliverables in terms of infrastructures, networks of platforms (common standards for publication and model deposition, financial support to curate/validate models/data/etc.), thematic and technical networks, and scientific achievements. Federation of Physiome Projects. Community Building Initiatives
© 2006 STEP Consortium Two obvious areas are therapeutic innova- tion and public health (involve industry). Improve diagnosis. Industrial angle is different for diagnostics and therapies. Get involved in pre-clinical setting and then into clinical, once link well established. Improve research by coming up with hypo- theses that can be experimentally tested and that can result in the exclusion of at least one hypothesis. Impact Analysis
© 2006 STEP Consortium Researchers: as currently + tutorials on what the models actually do. Industry: provide demos, publish in profes- sional journals, take part in exhibitions, contact associations. Policy makers: provide demos, talk to existing Eropean agencies. Society: get involved with the media by, for instance, talking to big pharmaceutical companies. Dissemination Models
© 2006 STEP Consortium Convince stakeholders of the benefits of modelling by involving them as early as possible in a very specific project. Provide success stories that are based on quantitative modelling studies where, for instance, drug development can be applied. Need major refocus of funding and an increase of support for such studies (see US and Japan for instance). Exploitation Models & Long-Term Sustainability
© 2006 STEP Consortium All of the above… Recommendation for a Concrete Implementation
© 2006 STEP Consortium Towards the EuroPhysiome roadmap STEP CONFERENCE#1 Hard Tissue Strand discussion Monday, May 15 th Fulvia Taddei.
1 European policies for e- Infrastructures Belarus-Poland NREN cross-border link inauguration event Minsk, 9 November 2010 Jean-Luc Dorel European Commission.
The ERA-NET TRANSCAN-2, in continuity with the preceding ERA-NET TRANSCAN, aims at linking translational cancer research funding programmes in 15 Member.
1 Direction scientifique Networks of Excellence objectives Reinforce or strengthen scientific and technological excellence on a given research topic.
Challenges of Disseminating Information to Broad Stakeholder Groups Elizabeth Cummings and Paddy Nixon University of Tasmania, Australia.
© 2006 STEP Consortium ICT Infrastructure Strand.
Type of funding scheme: STREP Work programme topics addressed: PHC 30 – 2015 Digital representation of health data to improve disease diagnosis and treatment.
ISBE An infrastructure for European (systems) biology Martijn J. Moné Seqahead meeting “ICT needs and challenges for Big Data in the Life Sciences” Pula,
The European Innovation Partnership for asthma: an opportunity for change Samantha Walker PhD, Asthma UK Project Coordinator, EARIP (European Asthma Research.
The opportunities and challenges of sharing genomics data with the pharmaceutical industry Shahid Hanif, Head of Health Data & Outcomes, ABPI DNA digest.
Research Infrastructures WP 2012 Call 10 e-Infrastructures part Topics: Construction of new infrastructures (or major upgrades) – implementation.
Ian Foster The Computation Institute. 2 Type Ia Supernova: SN 1994D.
GEO Work Plan Symposium 2012 ID-05 Resource Mobilization for Capacity Building (individual, institutional & infrastructure)
A complementary view from the DIGOIDUNA study Paolo Bouquet, University of Trento, Italy SMART 2010/0054.
INTEGRATING INDIGENOUS KNOWLEDGE (IK) INTO UGANDA’S POVERTY ERADICATION ACTION PLAN (PEAP) By Joyce N. Muwanga Assistant Executive Secretary Uganda National.
OPEN SCIENCE AND RESEARCH LEADS TO SURPRISING DISCOVERIES AND CREATIVE INSIGHTS Welcome from Ministry of Education and Culture The NeIC 2015 Conference,
Key Barriers for the ICT Research Sector in Serbia, and Recommendations for Future EU- Serbia Collaboration Miodrag Ivkovic, ISS Milorad Bjeletic, BOS.
© 2006 STEP Consortium What is the Virtual Physiological Human? Marco Viceconti Istituti Ortopedici Rizzoli (IT) STEP Scientific Coordinator.
European network for Health Technology Assessment | JA | EUnetHTA European network for Health Technology Assessment THL Info.
Digital public services and innovation
© 2017 SlidePlayer.com Inc. All rights reserved.