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.