TY - JOUR
T1 - The Human Body as a Super Network
T2 - Digital Methods to Analyze the Propagation of Aging
AU - Whitwell, Harry J.
AU - Bacalini, Maria Giulia
AU - Blyuss, Oleg
AU - Chen, Shangbin
AU - Garagnani, Paolo
AU - Gordleeva, Susan Yu
AU - Jalan, Sarika
AU - Ivanchenko, Mikhail
AU - Kanakov, Oleg
AU - Kustikova, Valentina
AU - Mariño, Ines P.
AU - Meyerov, Iosif
AU - Ullner, Ekkehard
AU - Franceschi, Claudio
AU - Zaikin, Alexey
N1 - © 2020 Whitwell, Bacalini, Blyuss, Chen, Garagnani, Gordleeva, Jalan, Ivanchenko, Kanakov, Kustikova, Mariño, Meyerov, Ullner, Franceschi and Zaikin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PY - 2020/5/25
Y1 - 2020/5/25
N2 - 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 multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
AB - 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 multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
KW - aging
KW - digital medicine
KW - inflammaging
KW - network analysis
KW - propagation of aging
UR - http://www.scopus.com/inward/record.url?scp=85086269052&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2020.00136
DO - 10.3389/fnagi.2020.00136
M3 - Article
C2 - 32523526
AN - SCOPUS:85086269052
VL - 12
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 136
ER -