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 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 totumor formation. Then they develop computational tools that help identify specific targets for drug-based intervention.
Efroni has developed a method that uses pathway knowledge to characterize and quantify network modifications between two different biological phenotypes.
Efroni and his team promote the concept of the network as the most efficient biomarker in stratifying patients into clinical groups, and use this method in biological samples from cancer patients. They find that by quantifying network behavior in clinical groups they are able to affiliate this behavior with clinical features in a statistically relevant manner. The specific sub-network that surfaces as the most informative biomarker is tagged for further analyses.
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.
In biological systems, most processes take place under varying levels of control, react to multiple events, and interact over multiple levels, signals and conditions. Efroni and others have developed a computational approach for simulating complex biological systems and termed this approach Reactive Animation (RA).
RA facilitates the simulation of complex biological systems and is built in two main layers: the first allows a bottom-up integration of discrete multi-level experimental data of theinteracting agents (cells and molecules), and defines the logic and dynamics behind these interactions. The second layer is a front-end visualization of the simulation, capable of real-time interactive manipulation of the simulated biological objects. RA allows the experimenter to intervene mid-simulation, suggest new hypotheses for cellular and molecular interactions, apply them to the simulation and observe their resulting outcomes “on-line.”