In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.

IF 4.4 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2026-06-22 DOI:10.1038/s41540-026-00769-1

Matteo Italia, José Garcia Otero, Juan Jiménez-Sánchez, Fabio Dercole, Juan Belmonte-Beitia

引用次数: 0

Abstract

Computational medicine uses mathematical modelling, high-performance computing, and the availability of large-scale biomedical data to study multiscale complex diseases. This review synthesises recent developments in in silico oncology, neurology, and epidemiology, highlighting their shared methodological foundations in systems theory and complex networks. We discuss how combining mechanistic approaches with machine learning integrates heterogeneous data. Finally, we identify translation barriers and outline future directions like digital twins for predictive, personalised healthcare.

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肿瘤学、神经学和流行病学的计算机模型：系统级和多尺度视角。

计算医学使用数学建模、高性能计算和大规模生物医学数据的可用性来研究多尺度复杂疾病。本文综合了计算机肿瘤学、神经学和流行病学的最新进展，强调了它们在系统理论和复杂网络中的共同方法论基础。我们讨论了如何将机械方法与机器学习相结合来集成异构数据。最后，我们确定了翻译障碍，并概述了未来的发展方向，如预测性个性化医疗保健的数字双胞胎。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.