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Patient-Specific Mathematical Model Of The Clear Cell Renal Cell Carcinoma Microenvironment, Dilruba Sofia, Navid Mohammad Mirzaei, Leili Shahriyari
Patient-Specific Mathematical Model Of The Clear Cell Renal Cell Carcinoma Microenvironment, Dilruba Sofia, Navid Mohammad Mirzaei, Leili Shahriyari
Mathematics and Statistics Department Faculty Publication Series
The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level …
Data-Driven Mathematical Model Of Osteosarcoma, Trang Le, Sumeyye Su, Arkadz Kirshtein, Leili Shahriyari
Data-Driven Mathematical Model Of Osteosarcoma, Trang Le, Sumeyye Su, Arkadz Kirshtein, Leili Shahriyari
Mathematics and Statistics Department Faculty Publication Series
As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had …
A Mathematical Model Of Breast Tumor Progression Based On Immune Infiltration, Navi Mohammad Mirzaei, Sumeyye Su, Dilruba Sofia, Maura Hegarty, Mohamed H. Abel-Rahman, Alireza Asadpoure, Colleen M. Cebulla, Young Hwan Chang, Wenrui Hao, Pamela R. Jackson, Adrian V. Lee, Daniel G. Stover, Zuzana Tatarova, Ioannis K. Zervantonakis, Leili Shahriyari
A Mathematical Model Of Breast Tumor Progression Based On Immune Infiltration, Navi Mohammad Mirzaei, Sumeyye Su, Dilruba Sofia, Maura Hegarty, Mohamed H. Abel-Rahman, Alireza Asadpoure, Colleen M. Cebulla, Young Hwan Chang, Wenrui Hao, Pamela R. Jackson, Adrian V. Lee, Daniel G. Stover, Zuzana Tatarova, Ioannis K. Zervantonakis, Leili Shahriyari
Mathematics and Statistics Department Faculty Publication Series
Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes …
Data Driven Mathematical Model Of Colon Cancer Progression, Arkadz Kirshtein, Shaya Akbarinejad, Wenrui Hao, Trang Le, Sumeyye Su, Rachel A. Aronow, Leili Shahriyari
Data Driven Mathematical Model Of Colon Cancer Progression, Arkadz Kirshtein, Shaya Akbarinejad, Wenrui Hao, Trang Le, Sumeyye Su, Rachel A. Aronow, Leili Shahriyari
Mathematics and Statistics Department Faculty Publication Series
Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated …