Dr. Manuel Llinás manuel@derisilab.ucsf.edu Post Doctoral Researcher Biochemistry and Biophysics University of California San Francisco San Francisco, CA

Dr. Llinás is a postdoctoral fellow in the laboratory of Joseph DeRisi using whole genome approaches to investigate the functional genomics of Plasmodium falciparum, the causative agent of the deadliest form of human malaria.

In the Spring of 2005, Dr. Llinás will be starting as an assistant professor at the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

Malaria is one of the most deadly and profound human health problems in existence and results in approximately 1.5 to 2.7 million deaths annually. The most fatal and prevalent form of malaria is caused by the blood-borne pathogen Plasmodium falciparum. Beyond the lives this parasite claims every year, hundreds of millions of people will become clinically ill. The socioeconomic impact of this disease to developing countries, especially those on the African continent, is beyond measure.

With the completion of the genome of P. falciparum, we have put this sequence information to work in the form of DNA microarrays and functional genomics. DNA microarrays, consisting of solid substrates onto which thousands of unique DNA samples may be printed, provide a means to assay relative mRNA abundance levels simultaneously for whole genomes. We are now using the lessons learned in model organisms and applying them to malaria, a problem of real and immediate human importance. We are using global gene expression to rapidly map and elucidate biochemical and transcriptional pathways simultaneously through systematic perturbation analysis. We are combining expression profiling with chromatin-immunoprecipitation, cellular fractionation, and drug SAR studies to assemble a complete genomic portrait of this parasite. All of these experiments are focusing on the erythrocytic phases of the parasite's life cycle.

The P. falciparum microarrays represent a novel departure from how we have used technology these past six years. Instead of depositing small amounts of PCR (Polymerase Chain Reaction) amplified genes, we have adopted a strategy whereby long oligonucleotides (70 bases) are synthesized and directly spotted onto an array. This enables us to target regions of genes which are maximally unique, thus eliminating the irksome problem of cross hybridization while retaining sensitivity. In addition, it obviates the expensive and time consuming process of enzymatically amplifying and verifying thousands of DNA templates. This is especially important for P. falciparum. The genome of this organism possesses a very high A/T content and is rife with gene families with high sequence similarity. The use of synthetic long oligos negates these issues.

Dr. Richard Simon rsimon@mail.nih.gov Chief, Biometric Research Branch Division of Cancer Treatment and Diagnosis National Cancer Institute Rockville, MD

Chief Statistician for the Division of Treatment and Diagnosis of the National Cancer Institute (NCI). Provide statistical and mathematical leadership for national programs of drug discovery, molecular diagnosis, biomedical imaging, radiation research and clinical trials. Responsible for statistical review of all NCI sponsored clinical trials, oversight of coordinating centers and represent NCI in data monitoring and reporting issues. Provide leadership for an active research program in molecular statistics, bioinformatics, and biomathematics applied to understanding the mechanisms of cancer development and for the discovery and development of molecular diagnostics and treatments. Conduct a training program in computational biology and bioinformatics for mathematical, computational and physical scientists.

Current research interests include Bayesian methods in clinical trial design and analysis, and the development of methods for the analysis of genome sequence and expression data to identify cancer related genes, elucidate their functions, determine the steps of tumor development, identify molecular targets and develop genome based approaches to the prevention, detection, diagnosis and treatment of cancer.