多尺度建模用于预测性了解癌细胞代谢的重要综述

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology Pub Date : 2024-07-20 DOI:10.1016/j.coisb.2024.100531

Marco Vanoni , Pasquale Palumbo , Stefano Busti , Lilia Alberghina

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引用次数: 0

摘要

新陈代谢的重编程是癌症的既定标志，它促进了癌细胞的生长和增殖。全基因组代谢模型越来越能够描述癌症的生长。多尺度模型可捕捉癌细胞的其他相关特征及其与肿瘤微环境的关系。多尺度代谢模型与人工智能的结合可带来肿瘤学的范式转变，并有可能产生针对特定患者的个性化数字双胞胎。

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A critical review of multiscale modeling for predictive understanding of cancer cell metabolism

Metabolism, whose reprogramming is an established cancer hallmark, promotes growth and proliferation in cancer cells. Genome-wide metabolic models are becoming increasingly capable of describing cancer growth. Multiscale models may allow the capture of other relevant features of cancer cells and their relationship with the tumor microenvironment. The merging of multiscale metabolic modeling and artificial intelligence can lead to a paradigm shift in oncology, possibly leading to patient-specific personalized digital twins.

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来源期刊

Current Opinion in Systems Biology Mathematics-Applied Mathematics

CiteScore

7.10

自引率

2.70%

发文量

期刊介绍： Current Opinion in Systems Biology is a new systematic review journal that aims to provide specialists with a unique and educational platform to keep up-to-date with the expanding volume of information published in the field of Systems Biology. It publishes polished, concise and timely systematic reviews and opinion articles. In addition to describing recent trends, the authors are encouraged to give their subjective opinion on the topics discussed. As this is such a broad discipline, we have determined themed sections each of which is reviewed once a year. The following areas will be covered by Current Opinion in Systems Biology: -Genomics and Epigenomics -Gene Regulation -Metabolic Networks -Cancer and Systemic Diseases -Mathematical Modelling -Big Data Acquisition and Analysis -Systems Pharmacology and Physiology -Synthetic Biology -Stem Cells, Development, and Differentiation -Systems Biology of Mold Organisms -Systems Immunology and Host-Pathogen Interaction -Systems Ecology and Evolution