Computational Methods for Gene Expression Analysis in Neuroscience
Refining classical anatomic boundaries of the brain
Organizers:
Mike Hawrylycz, Sayan Pathak
Allen Institute for Brain Science, Seattle, WA
Overview
Classical neuroanatomy of the mammalian brain is the result of detailed and painstaking dissection and analysis of pathways and processes in model organisms. The large scale analysis of gene expression and genomic level approaches to the nervous system areproviding a new window of possibility into refining or redefining neuroanatomic structure through the study of emerging patterns and correlations in gene transcription. Modern neuroscience recognizes the importance of cellular level data resolution and cell type specific analysis for the complete understanding of function in the brain. Several novel data modalities, such as in situ hybridization and transgenic reporter gene analysis allow for systematic screens of gene expression in both the embryo and adultbrain.
The goal of redefining classical anatomy is challenging and requires a variety of data modalities and methods. In order to draw meaningful inferences from gene expression experiments, it is also important that each target be surveyed under a variety ofconditions including time series and in response to perturbation. Such complex data sets may be analyzed using methods with increasing depth of inference. Additionally, computational models may serve as a test-bed for the development of these emerging inference techniques in neuroscience. For example, using these models the dynamic behavior of pathways and co-expression can be linked to select network architecture including underlying molecular links and their interactions.
Understanding the functional boundaries of conventional neuroanatomy will also require accurate expression detection methods and these are increasingly computational in nature. Progress has been made in this arena with work on probabilistic expression and other higher dimensional atlases but much remains open. The techniques of microarray analysis have been useful in understanding large scale structural expression but do not provide the necessary detail for micro and local variation. Successful methods of analysis increasingly involve advanced computational approaches of a statistical nature, machine learning and simulation theory.
The goal of this workshop is to present and develop emerging problems and informatics tools needed for atlas based gene expression. Speakers with representative expertise in neuroscience and neuroinformatics, computational methods in gene expression analysis and brain atlas development is being assembled who will present current problems and techniques. We encourage contributions describing progress on new informatics techniques applicable to this area and work that is substantially different from standard approaches.
Time and Place
- Saturday, December 10, 2005
- Workshop Sessions: 7:30 a.m. - 10:30 a.m. and 4:00 p.m. - 7:00 p.m.
- Whistler, Canada
- This is a one day workshop with short presentations, with plenty of time for discussion.
Confirmed Speakers
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Amir Assadi
, Univ. of Wisconsin,Madison, WI
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David Craig, TGEN, Phoenix, AZ
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Desmond Smith, UCLA School of Medicine, Los Angeles, CA
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Giorgio Ascoli, George Mason University, Fairfax, VA
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James Gee, UPenn, Philadelphia, PA
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Jonathan D Victor, Weill Medical College of Cornell U., New York, NY
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Mike Hawrylycz, Allen Institute for Brain Science, Seattle, WA
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Paul Pavlidis, Columbia University Medical Center, New York, NY
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Sayan Pathak, Allen Institute for Brain Science, Seattle, WA
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Teiichi Furuichi, Riken Brain Science Institute, Wako, Japan
Program
This is a day long workshop on discussing strategies to redefine brain anatomy using neuroinformatics. The summarized workshop program provides a tentative schedule we have been able to put together. The abstracts provided by the speakers can be found in the detailed workshop program.
Contacts
For more information, please contact:
Sayan Pathak at SayanP@alleninstitute.org
Mike Hawrylycz at MikeH@alleninstitute.org
