Genetic variation and regulatory networks: Mechanisms and complexity

Summary

Principal Investigator: DANA PE ER
Abstract: The focus of the proposed research is to understand the effect of sequence variation on the function of molecular networks. We will develop computational algorithms that integrate genotype, gene expression and phenotype data to construct models that describe how sequence variation perturbs the regulatory network, alters signal processing and is manifested in cellular phenotypes. Our approach is based on Bayesian networks, a framework we pioneered for the reconstruction of molecular networks from high-throughput data. We recently applied this framework to develop the Geronemo algorithm which we applied to yeast and uncovered a novel relationship between the sequence specific RNA factor PUF3 and P-Bodies, as well as a Single Nucleotide Polymorphism (SNP) in MKT1 that modulates this relationship. Both novel findings were experimentally validated subsequent to their discovery. Our approach is based on the complementary duality between genetic sequence and functional genomics. A significant influence of genotype on phenotype is induced by fine tuned perturbations to the complex regulatory network that governs a cell's activity. Variation in the expression of a single gene is more tractable and can be used as an intermediary to help associate genetic factors to the more complex downstream changes in phenotype in a hierarchical fashion. Conversely, DNA sequence polymorphisms are effective perturb-agens which provide a rich source of variation to help uncover regulatory relations in the molecular network as well as direct their causality. We will develop our methods using a large collection of highly variable yeast strains, for which we have generated robust quantitative growth curves under numerous environmental conditions. The methodologies piloted in yeast will be extended to genotype and gene expression data derived from tumor samples to attempt to elucidate the multiple genetic factors that drive their proliferation. These tools will be made publicly available, including a friendly graphical user interface and visualization.
Funding Period: 2007-09-30 - 2012-08-31
more information: NIH RePORT

Top Publications

  1. pmc Modularity and interactions in the genetics of gene expression
    Oren Litvin
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA
    Proc Natl Acad Sci U S A 106:6441-6. 2009
  2. pmc Harnessing gene expression to identify the genetic basis of drug resistance
    Bo Juen Chen
    Department of Biological Sciences, Columbia University, New York, NY, USA
    Mol Syst Biol 5:310. 2009
  3. pmc JISTIC: identification of significant targets in cancer
    Felix Sanchez-Garcia
    Department of Computer Science, Columbia University, New York, NY, USA
    BMC Bioinformatics 11:189. 2010
  4. pmc An integrated approach to uncover drivers of cancer
    Uri David Akavia
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA
    Cell 143:1005-17. 2010
  5. pmc viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
    El Ad David Amir
    Department of Biological Sciences, Columbia Initiative for Systems Biology, Columbia University, New York, New York, USA
    Nat Biotechnol 31:545-52. 2013
  6. pmc Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development
    Sean C Bendall
    Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA Department of Pathology, Stanford University, Stanford, CA 94305, USA
    Cell 157:714-25. 2014
  7. pmc Principles and strategies for developing network models in cancer
    Dana Pe'er
    Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA
    Cell 144:864-73. 2011

Scientific Experts

  • Dana Pe'er
  • Sean C Bendall
  • El Ad David Amir
  • Felix Sanchez-Garcia
  • Uri David Akavia
  • Helen C Causton
  • Oren Litvin
  • Bo Juen Chen
  • Erin F Simonds
  • Garry P Nolan
  • Kara L Davis
  • Michelle D Tadmor
  • Daniel K Shenfeld
  • Eyal Mozes
  • Tiffany J Chen
  • Jacob H Levine
  • Smita Krishnaswamy
  • Levi A Garraway
  • Jessica Kim
  • Panisa Pochanard
  • Dylan Kotliar
  • Denesy Mancenido
  • Ethan O Perlstein
  • Noel L Goddard

Detail Information

Publications7

  1. pmc Modularity and interactions in the genetics of gene expression
    Oren Litvin
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA
    Proc Natl Acad Sci U S A 106:6441-6. 2009
    ..Thus, different cellular states occur not only in response to the external environment but also result from intrinsic genetic variation...
  2. pmc Harnessing gene expression to identify the genetic basis of drug resistance
    Bo Juen Chen
    Department of Biological Sciences, Columbia University, New York, NY, USA
    Mol Syst Biol 5:310. 2009
    ..Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug...
  3. pmc JISTIC: identification of significant targets in cancer
    Felix Sanchez-Garcia
    Department of Computer Science, Columbia University, New York, NY, USA
    BMC Bioinformatics 11:189. 2010
    ..As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect...
  4. pmc An integrated approach to uncover drivers of cancer
    Uri David Akavia
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA
    Cell 143:1005-17. 2010
    ..Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer...
  5. pmc viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
    El Ad David Amir
    Department of Biological Sciences, Columbia Initiative for Systems Biology, Columbia University, New York, New York, USA
    Nat Biotechnol 31:545-52. 2013
    ..viSNE can be applied to any multi-dimensional single-cell technology...
  6. pmc Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development
    Sean C Bendall
    Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA Department of Pathology, Stanford University, Stanford, CA 94305, USA
    Cell 157:714-25. 2014
    ..This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer...
  7. pmc Principles and strategies for developing network models in cancer
    Dana Pe'er
    Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA
    Cell 144:864-73. 2011
    ..In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics...