Pablo Meyer  Pablo Meyer photo       

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Team Leader
Thomas J. Watson Research Center, Yorktown Heights, NY USA

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Pablo is a Team leader in the Translational Systems Biology and Nanobiotechnology group and Research Staff member in the IBM Computational Biology Center and Adjunct Professor of Genetics and Genomics Sciences at Mount Sinai’s Icahn School of Medicine. He joined IBM research in 2010 and received his Undergraduate degree in Physics from the University of Mexico UNAM (2000) and a Masters degree from the University of Paris VII/XI his Ph.D. in Genetics from the Rockefeller University (2005). He was awarded a Helen Hay Whitney fellowship as a posdoctoral fellow in Columbia university.

 

General interests

I am overall interested in how events at the molecular level and gene circuits determine mesoscopic or higher order phenomena from circadian bevahiors in flies, influences on apoptosis and prediction of olfactory responses from molecular structures (see full text of recent paper in Science).

See the BIRS workshop I organize and a workshop on cancer I recently participated in. 

 

Check my TEDx talk on DREAM challenges, "Breaking the Piñata of Science".

 

Systems Biology of Single Cell Metabolism  

 We want to generate single cell datasets for the analysis and development of computational tools in order to predict metabolic, transcriptional and macromolecular states in cells. Determining the state of a metabolic network in a cell and the regulation of enzymes enabling it, will be inferred through the spatial localization of fluorescently tagged enzymes in single cells. The underlying argument is that the distribution of an enzyme in the cytoplasm mirrors its activity state. We will further measure using fluorescent time-lapse microscopy the rates of single cell growth, DNA replication and analyze how gene-circuits regulate these states. In-house whole cell computational models and methods developed through the Dialogue on Reverse Engineering Assessments and Methods (DREAM) will be used to explore the relationships between metabolism, gene expression and parameter estimation. As shown in the B.subtilis movie below, we want to understand how cells having same genomes and same external conditions of growth make different decisions. 

 

 

                                                       

 My research application rests on a comprehensive survey and assessment of computational approaches used to analyze cell metabolic regulation. It should pave the way to the identification of metabolic biomarkers predicting the phenotype of cancer cells/tissues that could be used for personalized medicine and cancer prevention. Results should be useful not only for the particular field of metabolic regulation in cancer, but also as a case study for developing computational methods to understand how cellular metabolic defects are linked to other diseases.