学术报告报告题目：Mathematical models to uncover how mechanical signatures are crucial drivers of tumour progression
报 告 人：Fabian Spill 博士（英国伯明翰大学）
报告时间：2019年7月11日（星期四）上午10:30-11:30
报告地点：4号楼4224室
邀 请 人：杨启贵 教授
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数学学院
2019年7月9日
报告摘要：
Biology and Medicine are currently undergoing a quantitative revolution. New technologies enable researchers to gather unprecedented amounts of quantitative data. For instance, we can infer the genetic mutations of individual cancers. We can also quantify how individual cancer cells react to different treatments. However, despite the enormous amount of data that was generated, we lack a holistic understanding of cancer with its many signatures at different scales.
Mathematical models are being developed to understand the enormous amount of data. These can help to address basic research questions as well as to inform clinical decisions. For instance, the models can predict how fast tumours grow and spread, how cancer cells behave in different environments (e.g. in different organs), or how tumours react to different drugs. Mathematical oncology is thus a new field of research that drives the development of new mathematical models and methods to revolutionise cancer research. I will give a general introduction to this new exciting field and then discuss some examples from my own research. Specifically, I will show that besides genetic signatures, mechanical signatures are now increasingly appreciated as crucial drivers of tumour progression. I will show some recent results about mechanochemical models that demonstrate how cancer cells integrate mechanical signals to make decisions, or how mechanics plays a critical role during cancer metastasis – the major step making cancer a deadly disease.
报告人简介：
Fabian is a Lecturer at the University of Birmingham, specialising in cancer modelling. He was previously a postdoc at MIT, Boston University and the University of Oxford, and developed a wide range of models to understand the effect of mechanics in tumour progression or the effect of noise in biological systems. Fabian holds a PhD from Imperial College London, where he worked n string theory and integrable models, and also worked as a quantitative analyst in an investment bank。