The self-organization model reveals systematic characteristics of aging

Abstract

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.

The Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis

“…Aging and disease models were first constructed, respectively; then, an improved inflamm-aging model was developed by including inflamm-aging markers and adjusting the disease predictor ( Supplementary Figure S5 ). The following steps were performed for the inflamm-aging model: 1) To summarize the interactions among key markers according to aging/disease, the gene expression profiles were transformed (or replaced) based on the results of our previous study ( Wang et al, 2020b ). The Pearson correlation coefficient was used to evaluate relevance and redundancy: where the phenotype was set as 0 (control) versus 1 (disease) in the disease model, or as the transformation of the age in the aging model: …”

“…1) To summarize the interactions among key markers according to aging/disease, the gene expression profiles were transformed (or replaced) based on the results of our previous study ( Wang et al, 2020b ). The Pearson correlation coefficient was used to evaluate relevance and redundancy:…”

“… ② Aging markers and candidate disease markers were further filtered using the Kruskal–Wallis test ( p < 0.05 and FDR < 0.2). ③ Inflamm-aging markers were identified by transforming selected inflammatory markers combined with the aging markers [also based on a previous study ( Wang et al, 2020b ) as in step ( Lopez et al, 2021 )], where an interaction was retained if p < 0.05 and FDR < 0.2 using the Kruskal–Wallis test. ④ Disease markers were also transformed based on the inflammatory markers.…”

“…③ Inflamm-aging markers were identified by transforming selected inflammatory markers combined with the aging markers [also based on a previous study ( Wang et al, 2020b ) as in step ( Lopez et al, 2021 )], where an interaction was retained if p < 0.05 and FDR < 0.2 using the Kruskal–Wallis test.…”