Download - Biomedical computing Michael Welge, Ian Brooks, Victor Jongeneel

Biomedical computingMichael Welge, Ian Brooks, Victor Jongeneel

• Biomedicine identified as being of high importance to NCSA, but definition of strategic plan still in early stages• Expect to have a fully formed strategy by end 2010

• Overall process for developing a plan:• Understand the strengths of NCSA and be prepared to capitalize

on them; identify weakness that need to be addressed• Define the overall areas of opportunity for high-performance

computing in the biomedical area• Identify strategic partnerships on- and off-campus, with the

potential to initiate projects congruent with the above• Engage in substantive discussions with partners and define the

scope of common projects

NCSA Strategic Planning Presentation (April 20,2010)

Understanding the strengths of NCSA

• Most relevant NCSA activities (View of NCSA):• High-performance data services (2)

• Science visualization for scientists and engineers (5)

• Cyber-applications for scientific and engineering communities (6)

• Security technologies and software (11)

• High-performance computing services (1)

• Core competence in large-scale data mining• Handling and extracting information from massive datasets

• Building on past successes• Continuing work on NAMD and its applications

• NCSA seminal contribution to MIDAS (Models of Infectious Disease Agents Study)

• Collaborative projects with IGB (e.g. Evolution Highway)

• International and national disease monitoring and control


Defining areas of opportunity(from 2006 NSF workshop)

• Biomolecular Structure Modeling (for example extending classical Molecular Dynamics calculations to account for quantum mechanical effects, multidimensional free energy surfaces, transition state ensembles)

• Modeling Complex Biological Systems (for example developing models of cell and organ function)

• Genomics (for example search calculations mapping phylogeny to ontogeny)

• Customized Patient Care (for example computing drug interactions in the context of individual physiology and blood chemistry)

• Ecological component of earth system modeling (for example adding plant cover to climate models)

• Infectious disease modeling (for example modeling of disease spreading and the likely impact of containment strategies)


DIVISION OF BIOMEDICAL SCIENCESMAYO CLINIC

Information-based medicine

Additional areas of opportunity

• Data management solutions for high-throughput biology• “Next generation” sequencing currently generates tens of TB raw

data per experiment, steep increases likely• Other technologies are also rapidly increasing output:

proteomics with prior spatial and/or chemical separation, high-throughput high-resolution imaging, …

• Understanding the relationship between genotype and phenotype• Rapidly increasing production of full genome sequences from

individuals within one species (mostly human) and from different species; millions of differences observed, thousands of genomes being sequenced

• Identifying genomic determinants of phenotypic differences is a major data mining / statistical problem


High-throughput biology - one recent example


190,000 movies recording over 19 million cell divisions…

B Neumann et al. Nature 464, 721-727 (2010) doi:10.1038/nature08869

Data analysis and hit detection.

A schema for implementing individualized medicine

NCSA Strategic Planning Presentation (April 20, 2010)

Strategic Partnerships• On campus

• IGB – medium-scale sequencing projects, comparative genomics, evolution• Beckman, Bioeng. – high-throughput imaging and analysis

• Genomic Institute of Singapore• Existing collaboration with Ed Liu, institutional ties, immediate need for

expertise

• Mayo Clinic• MoU in place, unmatched patient records, committed to developing

individualized medicine, poised to boost sequencing capability

• Genome Center at Wash U• One of the pre-eminent genome centers worldwide• Faced with critical data management and analysis problems in spite of

heavy investments

• Possible new partnerships• Wellcome Trust Sanger Institute – where much of high-throughput biology is

happening• EMBL/EBI, Broad Institute, BGI-Shenzhen, Genoscope, ???


Scope of projects (with partners)

• Discussions are ongoing, nothing decided yet• Overall themes emerging:

• Practical solutions for large-scale data management in the life sciences

• Development of tools, including visualization, for comparative genomics (within or between species)

• Data mining for personalized medicine – integration of “omic” data with phenotypic records

• Disease prevention through monitoring and modeling• Real-time medical decision support

• Needed soon: a WOW project highlighting the know-how and capabilities of NCSA


Sources

• NSF Workshop Report: Petascale Computing in the Biological Sciences (2006)• Edited by Allan Snavely, Gwen Jacobs, and David A. Bader

• Presentation from Division of Biomedical Sciences on “Information-based Medicine”

• Discussion with Roberto Fabbretti, Jacques Rougemont, Ioannis Xenarios (Lausanne) on data needs of next-gen sequencing

• Many discussions with faculty and staff from NCSA, IGB, Bioengineering, other UIUC Departments

• Dozens of recent papers, particularly in Nature and Nature Genetics


Download - Biomedical computing Michael Welge, Ian Brooks, Victor Jongeneel

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