Research Fellows
Gloria Sheynkman
Research Fellow
education
PhD, University of Wisconsin 2014
B.A., University of Notre Dame 2006
contact Info
GloriaM_Sheynkman@dfci.harvard.edu
Alternative splicing is frequently misregulated during carcinogenesis, contributing to rewiring of cellular pathways that drive proliferation, apoptosis, and other hallmarks of cancer. Much knowledge of splicing stems from studies on differential expression of alternatively spliced mRNAs rather than on protein function. Consequently, there is a large gap in our knowledge of the function and significance of alternatively spliced proteins in normal and cancer biology, especially on a proteome-wide scale. The Center for Cancer Systems Biology (CCSB) recently mapped the first protein isoform interactome, demonstrating that alternative isoforms are involved in diverse organizational, functional, and cross-tissue network dynamics. Remarkably, two isoforms from the same gene can be functionally just as different from each other as distinct proteins encoded by two separate genes. Expanding upon the original isoform interactome study, Gloria has begun work towards empirically building and analyzing the first cancer-specific isoform interaction network. This project will be the first global picture of the consequences and functional effects of aberrant splicing in cancer.
Research Fellow
education
PhD, University of Wisconsin 2014
B.A., University of Notre Dame 2006
contact Info
GloriaM_Sheynkman@dfci.harvard.edu
Alternative splicing is frequently misregulated during carcinogenesis, contributing to rewiring of cellular pathways that drive proliferation, apoptosis, and other hallmarks of cancer. Much knowledge of splicing stems from studies on differential expression of alternatively spliced mRNAs rather than on protein function. Consequently, there is a large gap in our knowledge of the function and significance of alternatively spliced proteins in normal and cancer biology, especially on a proteome-wide scale. The Center for Cancer Systems Biology (CCSB) recently mapped the first protein isoform interactome, demonstrating that alternative isoforms are involved in diverse organizational, functional, and cross-tissue network dynamics. Remarkably, two isoforms from the same gene can be functionally just as different from each other as distinct proteins encoded by two separate genes. Expanding upon the original isoform interactome study, Gloria has begun work towards empirically building and analyzing the first cancer-specific isoform interaction network. This project will be the first global picture of the consequences and functional effects of aberrant splicing in cancer.
Aaron Richardson
Research Fellow
education
PhD, Albert Einstein College of Medicine 2013
B.A., Boston University 1999
contact Info
Aaron_Richardson@dfci.harvard.edu
With comprehensive reference interactome maps nearing completion, robust systematic and high-throughput strategies are needed to explore the functional information that exists within these extensive biophysical networks. One way to extract this functional information on a large scale is to query how different edgetic mutations affect interactome network properties. We are developing a forward and reverse genetics platform that will allow us to interrogate the edges that exist between the nodes in any given interactome subnetwork and identify the overlap of interaction-defective alleles that also are defective for function. What we learn from the edgetic dissection of our yeast Rpd3 histone deacetylase complex model will ultimately be applicable to higher eukaryotes through the use of emerging CRISPR technologies.
Research Fellow
education
PhD, Albert Einstein College of Medicine 2013
B.A., Boston University 1999
contact Info
Aaron_Richardson@dfci.harvard.edu
With comprehensive reference interactome maps nearing completion, robust systematic and high-throughput strategies are needed to explore the functional information that exists within these extensive biophysical networks. One way to extract this functional information on a large scale is to query how different edgetic mutations affect interactome network properties. We are developing a forward and reverse genetics platform that will allow us to interrogate the edges that exist between the nodes in any given interactome subnetwork and identify the overlap of interaction-defective alleles that also are defective for function. What we learn from the edgetic dissection of our yeast Rpd3 histone deacetylase complex model will ultimately be applicable to higher eukaryotes through the use of emerging CRISPR technologies.
Soon Gang Choi
Research Fellow
education
PhD, Mount Sinai School of Medicine 2012
M.S., University of Michigan, Ann Arbor 2006
B.S., Illinois Institute of Technology 2004
contact Info
SoonG_Choi@dfci.harvard.edu
I study how protein-protein interactions (PPIs) can be targeted by small molecule drugs. One of major bottlenecks for developing PPI drugs is the lack of robust high-throughput technologies that can systemically identify drug-like compounds against PPIs in human proteome, given the fact that there are a large numbers of PPIs in the human proteome (~600,000 estimate) and vast chemical space for small molecules. To this end, I’ve been developing and employing state-of-art PPI technologies including Nanoluc-two-hybrid, DNA-barcoded PPI assay, and reverse yeast two-hybrid that can measure the formation and disruption of PPIs by compounds in high-throughput fashions with high sensitivity. Attaining the ability to specifically modulate PPIs by small molecules will help us to develop novel drugs for various human diseases including cancers and will lead to deeper understanding of human protein interactome network.
Research Fellow
education
PhD, Mount Sinai School of Medicine 2012
M.S., University of Michigan, Ann Arbor 2006
B.S., Illinois Institute of Technology 2004
contact Info
SoonG_Choi@dfci.harvard.edu
I study how protein-protein interactions (PPIs) can be targeted by small molecule drugs. One of major bottlenecks for developing PPI drugs is the lack of robust high-throughput technologies that can systemically identify drug-like compounds against PPIs in human proteome, given the fact that there are a large numbers of PPIs in the human proteome (~600,000 estimate) and vast chemical space for small molecules. To this end, I’ve been developing and employing state-of-art PPI technologies including Nanoluc-two-hybrid, DNA-barcoded PPI assay, and reverse yeast two-hybrid that can measure the formation and disruption of PPIs by compounds in high-throughput fashions with high sensitivity. Attaining the ability to specifically modulate PPIs by small molecules will help us to develop novel drugs for various human diseases including cancers and will lead to deeper understanding of human protein interactome network.
Anupama Yadav
Research Fellow
education
PhD, Tata Institute of Fundamental Research 2016
B.S., University of Delhi 2010
contact Info
Anupama_Yadav@dfci.harvard.edu
Having lived in multiple locations in the culturally and geographically diverse India, variation has been a constant presence in my life. Traveling to different continents simply emphasized the abundance of this variation. In my Ph.D., I studied the genetic regulation of this high phenotypic diversity in ecologically and geographically diverse yeast populations, and identified gene-gene and gene-environment interactions which contribute to their robustness and plasticity.
As an academic, I am interested in applying evolutionary concepts and tools to better understand the genotype-phenotype map and address questions of current relevance. Specifically, I am interested in understanding how the vast genetic diversity present in human populations modifies the molecular interactome, and utilize this information to study long standing questions of incomplete penetrance of variants, differential susceptibility to infections, differential sensitivity to therapeutics etc.
Research Fellow
education
PhD, Tata Institute of Fundamental Research 2016
B.S., University of Delhi 2010
contact Info
Anupama_Yadav@dfci.harvard.edu
Having lived in multiple locations in the culturally and geographically diverse India, variation has been a constant presence in my life. Traveling to different continents simply emphasized the abundance of this variation. In my Ph.D., I studied the genetic regulation of this high phenotypic diversity in ecologically and geographically diverse yeast populations, and identified gene-gene and gene-environment interactions which contribute to their robustness and plasticity.
As an academic, I am interested in applying evolutionary concepts and tools to better understand the genotype-phenotype map and address questions of current relevance. Specifically, I am interested in understanding how the vast genetic diversity present in human populations modifies the molecular interactome, and utilize this information to study long standing questions of incomplete penetrance of variants, differential susceptibility to infections, differential sensitivity to therapeutics etc.
Yang Wang
Research Fellow
education
PhD, Peking University 2014
B.S., South China University of Technology 2009
contact Info
Yang_Wang2@dfci.harvard.edu
From about two decades ago, geneticists have used gene deletions to understand the function of each gene and how they pertain to human disease. As the human reference interactome nears completion, however, there is still no efficient way to systematically remove each interaction from the interactome and test their biological function. By using forward and reverse “edgetics” techniques, Yang and coworkers are building a platform that not only efficiently breaks, but also restores, interactions from the interactome network. They are learning what makes a mutation edgetic or compensable, how mutations compensate each other from 3D structures point of view, what makes a biophysical compensatory mutation also have the corresponding phenotypic outcome and vice versa. This will further allow us to make computational predictions and test if the learned knowledge fits our model. This platform can potentially be used to perform an interactome-wide edge deletion screen using CRISPR technology and introduce compensatory mutations for each known edgetic human disease mutation to have a clearer understanding of the function of each interaction. Yang is also leading the data management, quality control, automation and downstream bioinformatics analysis for the yeast interactome project to deliver the highest quality yeast PPI network possible.
Research Fellow
education
PhD, Peking University 2014
B.S., South China University of Technology 2009
contact Info
Yang_Wang2@dfci.harvard.edu
From about two decades ago, geneticists have used gene deletions to understand the function of each gene and how they pertain to human disease. As the human reference interactome nears completion, however, there is still no efficient way to systematically remove each interaction from the interactome and test their biological function. By using forward and reverse “edgetics” techniques, Yang and coworkers are building a platform that not only efficiently breaks, but also restores, interactions from the interactome network. They are learning what makes a mutation edgetic or compensable, how mutations compensate each other from 3D structures point of view, what makes a biophysical compensatory mutation also have the corresponding phenotypic outcome and vice versa. This will further allow us to make computational predictions and test if the learned knowledge fits our model. This platform can potentially be used to perform an interactome-wide edge deletion screen using CRISPR technology and introduce compensatory mutations for each known edgetic human disease mutation to have a clearer understanding of the function of each interaction. Yang is also leading the data management, quality control, automation and downstream bioinformatics analysis for the yeast interactome project to deliver the highest quality yeast PPI network possible.
Luke Lambourne
Research Fellow
education
PhD, University College London 2014
M.Phys., University of Warwick 2010
contact Info
luke_lambourne@dfci.harvard.edu
I’m a computational biologist, interested using the huge amount of information generated by high-throughput systematic protein-level experiments in order to answer biological questions. I build custom statistical models based on details of the experiment in order to disentangle the variety of different factors that underlie the data. Currently, I’m working on the human reference interactome project, where I’m modeling the variance of confidence and ease of detection of interactions within the dataset.
Research Fellow
education
PhD, University College London 2014
M.Phys., University of Warwick 2010
contact Info
luke_lambourne@dfci.harvard.edu
I’m a computational biologist, interested using the huge amount of information generated by high-throughput systematic protein-level experiments in order to answer biological questions. I build custom statistical models based on details of the experiment in order to disentangle the variety of different factors that underlie the data. Currently, I’m working on the human reference interactome project, where I’m modeling the variance of confidence and ease of detection of interactions within the dataset.