Leili Shahriyari
Research areas include Quantitative and Systems Pharmacology (QSP) models, which are a system of differential equations modeling the dynamic interactions between drug(s) and a biological system, have been commonly used to discover, validate, and test drugs. These mathematical models provide an integrated "systems level" approach to determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono or combination therapy to obtain effective treatments. We employ a combination of machine learning, mathematical, and statistical methods to improve available QSP modeling approaches so that they could be used for suggesting personalized cancer treatments.
Current Research
Since each cancer has its own unique characteristics, each one can respond differently to the same treatments. Therefore, the creation of a digital twin (DT) of cancer can assist us in predicting the evolution of an individual's cancer through modeling each tumor's characteristics and response to treatment. Hence, we take advantage of new advances in computational approaches and combine mechanistic, machine learning, and stochastic modeling approaches to create a DT platform, which utilizes biological, biomedical, and EHR data sets. For each patient, the DT receives their information as input and predicts the evolution of their cancer.
Academic Background
- Postdoctoral Fellow, Mathematical Biosciences Institute, Ohio State University, Sep 2014 - Sep 2017.
- Postdoctoral Scholar, Institute for Genomics & Bioinformatics, University of California Irvine, Feb 2014 - Sep 2014.
- Postdoctoral Scholar, Department of Mathematics & Department of Ecology and Evolutionary Biology, University of California Irvine, Jan 2013 - Jan 2014.
- Ph.D. Mathematics, Johns Hopkins University, 2013.
- M.S.E. Computer Science, Johns Hopkins University, 2012.
- M.S. Mathematics, Sharif University of Technology, 2005.
- B.S. Mathematics, Isfahan University of Technology, 2003.
