Read More at Fight Aging!
Here find an interesting view of aging as the evolution of a complex dynamic system, at the point of criticality between stability and instability, towards a lesser ability to recover from perturbations. Eventually the usual small day to day changes that occur throughout life, to the environment, to exposure to stresses, to the normal operation of biological systems, will push an old organism into catastrophic failure, where a young organism would easily recover.
Aging is a very slow process that occurs at characteristic time scales far exceeding times associated with molecular processes or operations of an organism’s functional subsystems. Typically, such a hierarchy of scales arises from criticality, which is a special case of a dynamic system operating close to a tipping point separating the stable and unstable region. We proposed that aging results from inherent dynamic instability of the underlying regulatory networks and manifests itself as small deviations of the organism state variables (physiological indices) get exponentially amplified and lead to the exponential acceleration of mortality. The first principal component score is then an approximation to the order parameter characterizing the unstable phase and having the meaning of the total number of regulatory errors accumulated in the course of life of the animal. Hence, we believe that aging at criticality conjecture provides a good explanation for the success of Principal Components Analysis (PCA) as a semi-quantitative tool in aging research.
However, the abilities of linear rank reduction techniques, such as PCA, to unravel an accurate dynamic description of aging are limited for the following reasons. First, there are no reasons to believe that the effects of non-linear interactions between different dynamic subsystems are small. That is why a biomarker produced from such a linear analysis cannot be expected to perform well in different biological contexts. To compensate for the drawbacks of PCA, we combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan.