One of the greatest challenges in modern biomedical research is to understand the link between genotype and phenotype and, more importantly, to create models that allow us to predict the response of biological systems to various perturbations that can affect them. Despite an explosion of data that has emerged from genomic technologies, and continued efforts to develop models that capture the complexity of biological systems, we have not yet made significant progress in achieving these goals. Here we propose to develop new, phenomenological approaches that leverage empirical data together with information from other sources such as the biomedical literature, to create predictive models that can be further tested and validated. To understand the global properties of transcriptional networks and to predict the elements with the greatest potential to effect phenotypic changes, we will map our models to thermodynamic spin systems which allow us to identify robust gene modules and associated order parameters. We hypothesize that these order parameters drive phenotypic transitions in a manner analogous to the pressure and temperature dependent phase transitions of water between solid, liquid and gas. To develop and test this approach, we will use data currently being generated through the NHGRI-funded Center for Excellence in Genome Science (CEGS) program in which we are investigating the mechanisms by which oncovirus-derived genes perturb cellular networks to drive cellular transformation. The modules we identify will be used to make concrete predictions about key parameters essential for transformation and these predictions will be tested by using RNAi to perturb the cellular networks and evaluating the response by gene expression analysis and phenotypic assays. Working with my mentor, Dr. John Quackenbush, and co-mentors, Dr. Karl Munger and Dr. Giovanni Parmigiani, this project will allow me to develop a research program that will facilitate my transition from physics to biology and help me to secure an independent research position. To further support my career development, my mentors and I have developed a rigorous training program that will provide me with formal training in statistics and computational biology, and hands-on training in modern laboratory molecular biology.

Public Health Relevance

Knowing how genes interact in cellular networks to influence the way cells change from one state to another is essential for understanding a wide variety of processes ranging from development to human disease. Our project will use data on viral transformation of human cells, together with other publicly-available information, to develop computational models of gene-gene interactions and their role in this process;these models will then be mapped to equivalent systems borrowed from theoretical physics to identify crucial parameters governing phenotypic transitions. This general framework will provide insight into why particular cell states are robust and the best ways to perturb them to alter their phenotype.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25HG006031-02
Application #
8326605
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Bonazzi, Vivien
Project Start
2011-09-01
Project End
2016-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
2
Fiscal Year
2012
Total Cost
$119,398
Indirect Cost
$8,844
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
02215