Internship Site #1 BioDiscovery Lead mentor: Bruce Hoff, Ph.D. Students will use BioDiscovery’s software tools to analyze gene or protein microarray data that is provided by another participating site. Students will provide critical feedback regarding the applicability of the software to a given analysis and will share ideas for novel tools. The BioDiscovery software will be either current format or prototype extensions, and the student may be involved in development of new tools.

Internship Site #2 Chen Laboratory, University of Southern California (be sure to use the link to Dr. Chen’s research homepage)Chen Laboratory, University of Southern California Lead Mentor: Tim Ting Chen Dr. Chen works on genome assembly, gene-finding, genome rearrangements, mass spectrometry data analysis, microarray data analysis and protein-protein interactions. Possible projects: development of programs and user-interface for mass spectrometry data analysis; development of methods and tools for analyzing protein-protein interaction networks; and the development of tools for microarray data analysis.

Internship Site #3 Larson Laboratory, Division of Molecular Medicine, Beckman Research Institute, City of HopeDivision of Molecular Medicine Lead mentor: Garry Larson, Ph.D. Dr. Larson, together with Dr. Ted Krontiris, seeks to identify disease alleles and gene interactions in candidate genes important in risk assessment for cancer (predominately breast and prostate). Research involves microsatellite, microarray expression, associated transcription factor binding site, and SNP analyses on DNA from cancer patients who are sibling pairs.Ted Krontiris

Internship Site #4 Nordborg Laboratory, University of Southern California Lead Mentor: Magnus Nordborg, Ph.D. Dr. Nordborg focuses on natural variation in the model organism, the plant Arabidopsis. He is constructing a haplotype map for this species. Possible projects: construction of web-interfaces for visualizing data, database programming, implementation of analysis methods, automation of post-processing of sequence data.

Internship Site #5 Protein Pathways Lead Mentor: Matteo Pellegrini, Ph.D. Development of computational techniques to reconstruct protein networks from genome sequences, expression microarray and literature data. Possible projects: extension of network methodologies, development of dynamic network models that account for molecular concentrations and their fluctuations, methodologies to discover the states of proteins and their biological impact, and novel methods to determine drug-protein associations.

Internship Site #6 Vialogy Lead mentor: David J. Robbins, Ph.D. Dr. Robbins is a specialist in the area of active signal processing and quantum resonance interferometry (QRI). He has been working on developing applications of active signal processing to biomedical instrumentation. Applications have included analysis of DNA microarray data and DNA sequencing data. Possible projects: development of more sensitive analyses of microarray data, real-time PCR, 2-D gels, and mass spectrometry data. Students will learn how to evaluate analytical technologies currently used in the pharmaceutical and biotech fields. They will participate in the design of studies that must address the issues of concern for both the manufacturers and the users, and will determine the added value needed to justify incorporation of QRI into the current analytical technologies.

Internship Site #7 Wold Laboratory, Caltech Lead Mentor: Barbara Wold, Ph.D. Dr. Wold is interested in cell differentiation pathways and cell simulation. She investigates pathways by means of microarray analysis. Her group has used both synthetic and biological microarray data to undertake critical analyses of the efficacy of clustering algorithms for inferring transcriptional regulatory pathways. Possible project: population of a pathway database with literature-supported reactions and rate constants suitable for computational modeling of pathways.

Internship Site #8 Yeates Laboratory, UCLA Lead Mentor: Todd Yeates, Ph.D. Yeates’ group has developed a set of computational approaches for assigning functions to novel protein sequences and for elucidating the way proteins are connected into functional networks in the cell. In more recent work his group has made a surprising bioinformatics discovery -- in a certain branch of the phylogenetic tree, archaeal microorganisms have intracellular proteins that are rich in disulfide bonds. The group is currently investigating both the scientific implications and the potential practical applications arising from this discovery, and will be using new proteomics methods in their approaches. Possible projects: bioinformatics approaches to functional genomics and proteomics of archeabacteria; use of novel modeling software to compare classes of proteins from bacteria and other microorganisms.

Ranking of Internship Preferences In order to make internship assignments that will be mutually rewarding, we will be taking into account –your level of interest in each group –comments from prospective mentors –your academic, research, and professional experience for six sites, we will be attempting to assign teams of two with combined expertise in biological and computer- related sciences Please fill out the attached Word document (Internship Preferences.doc) and it as an attachment to no later than 9 AM, Wednesday, June 18th. If you are unable to , turn it in as hardcopy in the morning session, June 18th.Internship Preferences.doc We will do our best to match you with one of your first three choices.