Quantitative studies for mechanobiology of cancer via data-driven in silico replication of human tissues
Mahsa Dabagh, PhD
Assistant Professor, Biomedical Engineering Department
University of Wisconsin-Milwaukee
The in silico replication of patient-specific tissues has tremendous potential for screening to early diagnosis, prevention, and treatment of cancer via facilitating the study of cellular and molecular mechanisms predisposing the progression of the disease. Empowering in silico models provides an insight into other diseases and health conditions such as chronic wounds, atherosclerotic cardiovascular diseases, hypertension, aneurysm and so forth. The long-term goal of my research is to develop data-driven in silico replication of human tumor tissue that enables the modeling of tissues with all associated complexities and heterogeneities observed in patients. The state-of-the-art in silico model will reveal the mechanisms underlying the changes occurring in tissue components during disease progression, assist medical decision-makers in delivering precision therapies, and establish a unique knowledge on resisting factors against treatments on a per-patient basis.
In this seminar, I will first discuss my earlier study on the mechanobiology in cancer that elucidated the mechanisms facilitating cancer cells extravasation from blood flow to a secondary location. Then I will discuss my group’s current activities on developing in silico replication of tumor tissues. Finally, I will provide an outline for my ongoing and future research where computational, imaging, experimental, and deep learning methods will be linked to develop drug screening platform, explore how the resistance against drug therapies can be stopped, and develop an in silico-trained-deep learning platform to predicting cancer cells’ response to treatments. This will guide pathologists in deciding for an effective patient-specific treatment.