© 2006 step consortium multiscale modelling strand

© 2006 STEP Consortium

Multiscale Multiscale Modelling StrandModelling Strand


Multiscale Multiscale ModellingModelling


• Multiscale modelling is a quantitative, integrative and experimentally based approach for studying biological processes and dynamics that span multiple spatial (typically nanometers to meters – 109) and temporal (typically microseconds to decades – 1015) scales with the view to transfer knowledge and information across scales, as well as support modularity and interactivity.

DefinitionDefinition


• 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 IntegrationValidation and Integration


• 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).

DataData


• 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 IssuesICT Issues


Position PaperPosition Paper


• 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)Common Objectives (1)


• 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.



• 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.



• 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.



• 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 ChallengesResearch Challenges


• 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 RequiredResources Required


• 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 andEthical, Legal andGender Issues (1)Gender Issues (1)


• 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 andEthical, Legal andGender Issues (2)Gender Issues (2)


• 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 ModelOrganisational Model


• 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 Community Building InitiativesInitiatives


• 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 AnalysisImpact Analysis


• 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 ModelsDissemination Models


• 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 &Exploitation Models &Long-Term SustainabilityLong-Term Sustainability


• All of the above…

Recommendation forRecommendation fora Concrete Implementationa Concrete Implementation

© 2006 step consortium multiscale modelling strand

Documents

data slide

step consortium data

step consortium validation

step consortium structural

definition slide

integration slide

experimental data

tb of data