自组织模型揭示了老龄化的系统性特征。

Q1 Mathematics
Yin Wang, Tao Huang, Yixue Li, Xianzheng Sha
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引用次数: 1

摘要

背景:衰老是一个基本的生物学过程,关键生物标志物相互作用,协同调节衰老过程。因此,衰老功能障碍会诱发许多疾病。基于多组学数据(如甲基化、转录等)寻找衰老标记和重建网络对研究衰老过程具有重要意义。然而,尽管确定衰老相关疾病的潜在分子机制至关重要,但尚未系统地对模型进行优化以预测衰老。方法:采用一系列监督学习方法对老龄化自组织系统进行建模,从系统层面研究复杂的老龄化分子机制:优化老龄化网络;总结衰老标志物之间的相互作用;模块内老化标记物的积累规律;老化自组织系统的有序参数寻优。结果:在这项工作中,正常的衰老过程是基于不同组织的多组学图谱建模的。此外,计算管道旨在对衰老自组织系统进行建模,研究衰老与相关疾病(如癌症)之间的关系,从而提供有用的衰老相关疾病指标,有助于提高诊断的预测能力。结论:通过对自组织系统的建模,可以对衰老过程进行深入研究,从而确定衰老与癌症之间的关键功能和相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The self-organization model reveals systematic characteristics of aging.

The self-organization model reveals systematic characteristics of aging.

The self-organization model reveals systematic characteristics of aging.

The self-organization model reveals systematic characteristics of aging.

Background: Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging related diseases.

Methods: This paper aims to model the aging self-organization system using a series of supervised learning methods, and study complex molecular mechanisms of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters in the aging self-organization system.

Results: In this work, the normal aging process is modeled based on multi-omics profiles across different tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indicators of aging related diseases and could help to improve prediction abilities of diagnostics.

Conclusions: The aging process could be studied thoroughly by modelling the self-organization system, where key functions and the crosstalk between aging and cancers were identified.

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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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