Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.
Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.
复制标题
DOI：
10.1016/j.isci.2020.101807
复制
发表时间：
2020-12-18
期刊：
影响因子：
5.8
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Kazerouni AS;Gadde M;Gardner A;Hormuth DA 2nd;Jarrett AM;Johnson KE;Lima EABF;Lorenzo G;Phillips C;Brock A;Yankeelov TE
我们提供了对使用生物测定来校准和初始化基于机制的癌症现象模型的概述。尽管人工智能方法目前在计算肿瘤学领域占据主导地位，但寻求明确将生物机制纳入其形式体系的数学模型正日益引起人们的兴趣。这些模型可以指导实验设计，并为癌症进展的潜在机制提供见解。从历史上看，这些模型包含了无数难以在生物学相关系统中量化的参数，限制了它们的实际见解。然而，最近，人们对利用（例如）RNA测序、时间分辨显微镜和体内成像等可获得的定量测量来校准基于生物学的模型非常感兴趣。在这篇文章中，我们总结了各种实验方法如何从分子到组织尺度量化肿瘤特征，并描述了如何将这些数据直接与基于机制的模型相结合，以提高对肿瘤生长和治疗反应的预测。生物工程;系统生物学;癌症;在硅生物学中
We 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, and in vivo imaging. 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. Bioengineering; Systems Biology; Cancer; In Silico Biology
登录
查看更多内容
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
4.3
作者：
Bouhaddou M;Barrette AM;Stern AD;Koch RJ;DiStefano MS;Riesel EA;Santos LC;Tan AL;Mertz AE;Birtwistle MR
通讯作者：
Birtwistle MR
影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
DOI：
10.1098/rsta.2016.0153
发表时间：
2016-11-13
期刊：
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
影响因子：
--
作者：
Coveney PV;Dougherty ER;Highfield RR
通讯作者：
Highfield RR
影响因子：
5.7
作者：
Belgodere JA;King CT;Bursavich JB;Burow ME;Martin EC;Jung JP
通讯作者：
Jung JP