Introduction to computational modelling methods in cancer скачать в хорошем качестве

Introduction to computational modelling methods in cancer

Atelier sur la modélisation informatique de la biologie et des traitements du cancer / Workshop on Computational Modelling of Cancer Biology and Treatments (19 juillet 2021 / 19 July, 2021) http://www.crm.umontreal.ca/2021/comp... Morgan Craig (Université de Montréal, Canada) Introduction to computational modelling methods in cancer Workshop overview: Cancer biology and treatment involves complex, dynamic interactions between cancer cells, the tumour microenvironment, and therapeutic molecules. Quantitative approaches combining mechanistic disease modelling and computational strategies are increasingly leveraged to rationalize preclinical and clinical studies, and to establish effective treatment strategies. In this way, mathematical approaches lay the foundation for computational "virtual laboratories" that offer fully controlled, and non-invasive conditions in which we can investigate emergent clinical behaviours and interrogate new therapeutic strategies. As an introduction to such virtual laboratories, this workshop will provide an overview of techniques used in computational oncology, with a focus on model development, data fitting, in silico clinical trials and agent-based models (ABMs). Theoretical and practical examples of these techniques applied in research will be provided from experts in the field of mathematical oncology. In addition, there will be break-out group projects and tutorials to practice the relevant techniques. By the end of this workshop, participants will have a comprehensive understanding of computational modelling in oncology, the explicit knowledge for how to design, code, and simulate an agent-based model, and an understanding of how to account for within- and between-patient heterogeneity by deploying in silico clinical trials. In summary, the learning outcomes are: develop a computational model of a problem in oncology understand the distinction between the different paradigms of ABMs understand the relationship between PDEs and ABMs develop an agent-based model using PhysiCell estimating parameter values from real-world data generating virtual patients and running in silico trials