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Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multidisciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.

Developing computational analysis of time-lapse imaging of calcium events in astrocytes is a challenging task in neuroscience. Here we report the implementation of an algorithm that solves this task. After noise reduction with the block-matching and 3D filtering (BM3D) algorithm, calcium activity is identified as fluorescence elevation above the baseline level. Individual events are detected by sliding window approach applied to the variation of pixel intensity relative to the baseline level. The maximal projection and duration of astrocytic calcium events are then assessed. The novelty of the proposed method is an adaptive construction of the baseline level. The statistical results generated by our program are consistent with the previous algorithm reported and used by us for the reference. The software is publicly available.

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