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Existence and Clinical Implications of Traveling Wave Solutions in Reaction Diffusion Models of Brain Cancer Growth
时间:2020年12月13日 19:23 点击数:

报告人:Yang Kuang

报告地点:腾讯会议

报告时间:2020年12月14日星期一10:00-11:00

邀请人:范猛

报告摘要:

Glioblastoma multiforme (GBM) is an aggressive brain cancer that is extremely fatal. It is characterized by aggressive proliferation and fast migration, which contributes to the difficulty of treatment. Based on the so-called go or grow hypothesis, existing models of GBM growth often include two separate equations to model proliferation or migration processes. Motivated by an in vitro experiment data set of GBM growth, we formulate, validate, simulate, study and compare two plausible models of GBM growth. We propose first a single equation which uses density dependent diffusion to capture the behavior of both proliferation and migration. We analyze the model to determine the existence of traveling wave solutions. To prove the viability of the density-dependent diffusion function chosen, we compare our model with the in vitro experimental data. Our second model is build on the Go or Grow hypothesis since glioma cells tend to exhibit a dichotomous behavior: a cell either primarily proliferates or primarily migrates. For this model, different solution types are examined via approximate solution of traveling wave equations and we determine conditions for various wave front forms.

会议ID:312122973

主讲人简介:

Yang Kuang is a professor of mathematics at Arizona State University (ASU). He received his Ph.D degree from the University of Alberta. Dr. Kuang is the author of more than 180 refereed journal publications and 16 books and the editor of Mathematical Biosciences and Engineering. He is well known for his efforts in developing practical theories to the study of delay differential equation models and models incorporating resource quality in biology and medicine. Recently he focus on the formulation and validation of scientifically well-grounded and computationally tractable mathematical models to describe the rich dynamics of various within-host diseases and their treatments. These models have the potential to speed up much-needed personalized medicine development.

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