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.
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.
|Padi, Megha; Quackenbush, John (2018) Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl 4:16|
|Berrios, Christian; Padi, Megha; Keibler, Mark A et al. (2016) Merkel Cell Polyomavirus Small T Antigen Promotes Pro-Glycolytic Metabolic Perturbations Required for Transformation. PLoS Pathog 12:e1006020|
|Malleshaiah, Mohan; Padi, Megha; Rué, Pau et al. (2016) Nac1 Coordinates a Sub-network of Pluripotency Factors to Regulate Embryonic Stem Cell Differentiation. Cell Rep 14:1181-1194|
|Fischer, Martin; Grossmann, Patrick; Padi, Megha et al. (2016) Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks. Nucleic Acids Res 44:6070-86|
|Padi, Megha; Quackenbush, John (2015) Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators. BMC Syst Biol 9:80|
|Rozenblatt-Rosen, Orit; Deo, Rahul C; Padi, Megha et al. (2012) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins. Nature 487:491-5|