SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2017 can be found here.
Students pursuing opportunities at JPL must be U.S. citizens or U.S. permanent residents.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor.
Announcements for external summer programs are listed here.
|Project:||Using networks to explore biological systems|
|Disciplines:||Biology, Computational Biology, Statistics, Mathematics, Computer Science, Physical Sciences, or other relevant disciplines|
Director, Center for Cancer Computational Biology, (BBE),
|Mentor URL:||http://compbio.dfci.harvard.edu (opens in new window)|
NOTE: This project is being offered by a Caltech alum and will take place in Boston, Massachusetts.
We used to think of biological systems as being driven by simple associations—a single mutation in a single gene leads to a single disease phenotype. We now understand that biological systems are far more complex, with multiple interacting elements, many with individually small effects, working together to influence the phenotypes we see. Our research group has been developing models in systems and computational biology as applied to the study of complex disease and we have been using these models to address a wide range of open questions, ranging from how we best estimate genetic susceptibility, to modeling disease evolution, to understanding sexual dimorphism in disease risk, development, and progression.
We have a number of projects underway using large data sets to model gene regulatory networks and compare them between disease states, to estimate the genetic risk that different genetic variants confer for disease, and to understand how gene expression changes over time. The project would depend, in part, on the data available and the student’s interest and expertise.
Past student projects have included modeling gene regulatory networks in breast cancer, chronic obstructive pulmonary disease, and Mycobacterium tuberculosis, studying the changes in expression of oncogenes and tumor suppressor genes over decades, and exploring how small genetic effects work in combination to influence disease.
Students are expected to have working knowledge of a programming language such as R or python (and students typically spend their first week reviewing programming in R), have an interest in learning about network methods, and have a passion to explore the biological factors that are encoded in the structure of networks.
Here are four papers on the BioRxiv that summarize some of our work:
Sparse-aware multi tissue normalization:
Comparing primary tissues and derived cell lines:
Sexual dimorphism across 31 tissues:
eQTL network analysis in 11 tissues:
I would suggest the student complete the free, online datacamp course in R:
This AO can be done under the following programs:
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