# Leili Shahriyari

Associate Professor

**We develop frameworks to employ a combination of machine learning and statistical methods as well as mathematical techniques to arrive at personalized cancer therapies.**

**Digital Twins of Cancer Patients:** 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 "My Virtual Cancer", a DT platform. This DT platform utilizes biological, biomedical, and EHR data sets. For each patient, the DT receives their information as input and predicts the evolution of their cancer. In this project, we will focus on one common cancer type (breast cancer) and one rare cancer type (uveal melanoma) to evaluate the performance of the DT for both common and rare cancers.

**Developing Data-Driven Models for Obtaining Personalized Cancer Treatments:** 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. Existing parameter estimation methods for QSP models often use assembled data from various sources rather than a single curated dataset. These datasets are usually obtained through various biological experiments, in vitro and in vivo animal studies. We develop a framework to 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. To obtain values of parameters of the QSP model for each patient separately, we first developed a TumorDecon software, which is a combination of recently developed methods, to predict the relative number of variables of the QSP model from patients’ data. We use patients’ data of primary tumor to estimate the values of parameters of the QSP model for each patient separately, instead of the common approach of assuming these parameters have the same values across all patients and using animal studies to estimate them. This new approach provides us with a unique opportunity to predict the efﬁcacy of various treatments for each patient and suggest an effective personalized treatment strategy for cancer patients.

** Python Software Package TumorDecon:** provides an estimation of the relative number of cell types, including various immune cells, in a mixed cell population of tumors using gene expression profiles of tumor. First version released in June 2020 https://pypi.org/project/TumorDecon/. Supported by NIH-NCI ITCR program.

R.A. Aronow, S. Akbarinejad, T. Le, S. Su, L. Shahriyari, TumorDecon: A digital cytometry software, SoftwareX, 2022.

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Education

Ph.D. in Mathematics, Johns Hopkins University, 2013

M.S.E in Computer Science, Johns Hopkins University, 2012

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RESEARCH INTERESTS

Computational and Mathematical Biology, Data Science, Bioinformatics

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Selected Publications

- Y Hu, N Mohammad Mirzaei, L Shahriyari, Bio-Mechanical Model of Osteosarcoma Tumor Microenvironment: A Porous Media Approach,
*Cancers*, 2022. - Stahlberg EA, Abdel-Rahman M, Aguilar B, Asadpoure A, Beckman RA, Borkon LL, Bryan JN, Cebulla CM, Chang YH, Chatterjee A, Deng J, Dolatshahi S, Gevaert O, Greenspan EJ, Hao W, Hernandez-Boussard T, Jackson PR, Kuijjer M, Lee A, Macklin P, Madhavan S, McCoy MD, Mohammad Mirzaei N, Razzaghi T, Rocha HL, Shahriyari L, Shmulevich I, Stover DG, Sun Y, Syeda-Mahmood T, Wang J, Wang Q and Zervantonakis I, Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation,
*Front. Digit. Health*, 2022. - D. Sofia, N. Mohammad Mirzaei, L. Shahriyari, Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment,
*Journal of Personalized Medicine*, 2022. - N. Mohammad Mirzaei, Z. Tatarova, W. Hao, N. Changizi, A. Asadpoure, Y. Hu, I.K. Zervantonakis, Y.H. Chang, L. Shahriyari, A PDE model of breast tumor progression in MMTV-PyMT mice,
*Journal of Personalized Medicine*, 2022. - R.A. Aronow, S. Akbarinejad, T. Le, S. Su, L. Shahriyari, TumorDecon: A digital cytometry software,
*SoftwareX*, 2022. - N. Mohammad Mirzaei, N. Changizi, A. Asadpoure, S. Su, D. Sofia, Z. Tatarova, I.K. Zervantonakis, Y.H. Chang, L. Shahriyari, Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model,
*PLoS Computational Biology*, 2022. - N. Mohammad Mirzaei, S. Su, D. Sofia, M. Hegarty, M.H. Abdel-Rahman, A. Asadpoure, C. Cebulla, Y.H. Chang, W. Hao, P.R. Jackson, A.V. Lee, D.G. Stover, Z. Tatarova, I.K. Zervantonakis, L. Shahriyari, A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration,
*Journal of Personalized Medicine*, 2021. - T. Le, S. Su, L. Shahriyari, Investigating optimal chemotherapy options for osteosarcoma patients through a mathematical model,
*Cells*, 2021. - A. Budithi, S. Su, A. Kirshtein, L. Shahriyari, Data driven mathematical model of FOLFIRI treatment for colon cancer,
*Cancers*, 2021. - T. Le, S. Su, A. Kirshtein, L. Shahriyari, Data driven mathematical model of osteosarcoma,
*Cancers*, 2021. - S.Su, S. Akbarinejad, L. Shahriyari, Immune Classification of Clear Cell Renal Cell Carcinoma,
*Scientific Reports*, 2021. - A. Kirshtein, S. Akbarinejad, W. Hao, T. Le, S. Su, R. Aronow, L. Shahriyari, Data driven mathematical model of colon cancer progression,
*Journal of Clinical Medicine*, 2020. - T. Le, R. Aronow, A. Kirshtein, L. Shahriyari, A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells,
*Briefing in Bioinformatics*, 2020.