- Assistant Professor, Molecular and Cellular Biology
- Assistant Professor, Cellular and Molecular Medicine
- Assistant Professor, Cancer Biology - GIDP
I’ve always been fascinated by using mathematical models to understand the origins of emergent phenomena. I completed my Ph.D. at Harvard University in the field of string theory, the leading contender for a theory of quantum gravity. By studying the way in which black holes emerge from this theory, I was able to shed light on the experimental testability and solution space of string theory, and to take the first steps towards modeling the horizon of astronomical black holes.
Since then, I have focused on understanding how complex phenotypes and diseases emerge from a combination of genomic factors. During my postdoc at Harvard and Dana-Farber Cancer Institute, I developed computational methods for analyzing the network of gene interactions in the cell, in order to predict which genes drive disease, and which features of the network structure represent important pathways and potential drug targets. We also used network analysis to find that tumor viruses can transform cells in ways that mirror the pattern of mutations in non-viral cancers. Following up on this discovery, I developed a panel of cell lines to inducibly express viral oncogenes, and assayed their dynamics during the initial stages of cellular transformation using both RNA-sequencing and single-cell fluorescence time-lapse microscopy. By combining network science and targeted experiments, we aim to understand how genomic perturbations can push cells into a disease state, and to discover therapies that could reverse this process.
I started my group at the University of Arizona in January 2018 and am currently recruiting postdocs and graduate students.
- Ph.D. Theoretical high-energy physics
- Harvard University, Cambridge, Massachusetts, United States
- B.S. Physics, Biology
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Research Fellow, ETH Zurich (2017)
- Instructor, Harvard Medical School, Boston, Massachusetts (2014 - 2017)
- Postdoctoral Fellow, Dana-Farber Cancer Institute and Harvard School of Public Health (2009 - 2014)
All cells contain a large network of interacting genes and proteins that carry out necessary functions. But we still don’t understand how this network is disrupted in complex diseases like cancer, heart disease, diabetes, or asthma. The Padi lab develops new computational approaches to integrate genomic “big data” and model how cellular networks are functionally altered by disease, genetic variation, epigenetics, and other factors. Our ultimate goal is to identify drivers of disease, propose new therapeutic targets, and predict prognosis and drug response in a patient-specific manner. For example, we are using network analysis to understand how viruses induce tumorigenesis, how genetic variants combine to produce complex phenotypes, and how cancer cell lines and tumors respond to drugs. We use cell culture experiments to refine and validate our predictive network models. If you are interested in working in the field of computational genomics, systems biology, and network science, please contact Dr. Megha Padi at email@example.com.
ResearchCBIO 900 (Fall 2018)
Research ConferenceCBIO 695A (Fall 2018)
- Padi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 4, 16.More infoComplex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.
- Sharma, A., Halu, A., Decano, J. L., Padi, M., Liu, Y. Y., Prasad, R. B., Fadista, J., Santolini, M., Menche, J., Weiss, S. T., Vidal, M., Silverman, E. K., Aikawa, M., Barabási, A. L., Groop, L., & Loscalzo, J. (2018). Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ systems biology and applications, 4, 25.More infoProbing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS -values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways' relationship to T2D.