文章基本信息

标题：Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology
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作者：Anum S. Kazerouni ; Manasa Gadde ; Andrea Gardner 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：12
页码：1-16
DOI：10.1016/j.isci.2020.101807
语种：English
出版社：Elsevier
摘要：SummaryWe provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, andin vivoimaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.Graphical AbstractDisplay OmittedBioengineering; Systems Biology; Cancer;In SilicoBiology