Zaman Aubhishek, Bivona Trever G
Department of Medicine, University of California, San Francisco, CA 94158, USA.
UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA.
Cancers (Basel). 2022 Oct 26;14(21):5254. doi: 10.3390/cancers14215254.
Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.
生物科学是一项跨学科事业。受高通量数据量的巨大变化以及数据密集型技术的随时可得性驱动，数学和定量方法在生物科学中变得越来越普遍。例如，最近向细胞和表型的定量描述转变，正在取代传统的定性描述，这在从实验室到临床的癌症组学、化学生物学和药理学领域产生了巨大的前景和机遇。然而，与任何新兴领域一样，定量癌症研究仍然缺乏共享的标准化框架。在此，在癌症背景下，我们提出了一个从实验室到临床的定量研究和治疗的基本框架及指南。我们概述了一些基本概念及其在化学 - 蛋白质相互作用方面的平行应用案例。除了对实验设置和条件的若干建议外，我们还编目了这些定量技术在该领域一些最广泛使用的发现流程和分析方法中的应用。我们相信遵循这些指南将改善实验设计、减少变异性并使定量数据集标准化。
