Sol Efroni
Sol is an Associate Professor at the Mina & Everard Goodman Faculty of Life Sciences. Sol’s background is in physics, computer science and in immunology. Prof. Sol Efroni, a returning scientist from the National Cancer Institute at the NIH in Maryland, is a member of the Institute of Nanotechnology and Advanced Materials (BINA), and a senior lecturer at the Mina and Everard Goodman Faculty of Life Sciences. As head of BIU’s Systems BioMedicine Lab, Efroni performs pioneering systems biology network analysis in order to identify and quantify the network-wide changes that occur during the development of the malignant disease. His ultimate goal is to understand the cancer phenotype, in particular, breast cancer, ovarian cancer, and liver cancer, and to identify targets for therapeutic intervention.
Using high throughput sequencing and computational tools such as RNA-seq, also known as Whole Transcriptome Sequencing, Efroni and his team conduct genome-wide network analysis in order to pinpoint the malfunctions in the signaling pathways that are critical to tumor formation. Then they develop computational tools that help identify specific targets for drug-based intervention. Efroni’s research team also uses gene expression, protein and genomic data to help analyze basic mechanisms in tumor formation. They are currently characterizing network motivated intervention, which may be a powerful tool for targeting a specific sub-network in the whole transcriptome network. This targeting may present itself as the proper way to intervene with tumor-induced modifications, where single gene intervention does not produce the needed results.
Sol’s group looks at millions of single immune cells, studies their ever-diverse T cell and B cell repertoire, and uses cutting edge big-data interpretation techniques, such as deep learning, to connect such measurements to human health, human diversity and human aging.
Using high throughput sequencing and computational tools such as RNA-seq, also known as Whole Transcriptome Sequencing, Efroni and his team conduct genome-wide network analysis in order to pinpoint the malfunctions in the signaling pathways that are critical to tumor formation. Then they develop computational tools that help identify specific targets for drug-based intervention. Efroni’s research team also uses gene expression, protein and genomic data to help analyze basic mechanisms in tumor formation. They are currently characterizing network motivated intervention, which may be a powerful tool for targeting a specific sub-network in the whole transcriptome network. This targeting may present itself as the proper way to intervene with tumor-induced modifications, where single gene intervention does not produce the needed results.
Sol’s group looks at millions of single immune cells, studies their ever-diverse T cell and B cell repertoire, and uses cutting edge big-data interpretation techniques, such as deep learning, to connect such measurements to human health, human diversity and human aging.