University of Hertfordshire

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Research output: Contribution to journalArticlepeer-review

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The Human Body as a Super Network : Digital Methods to Analyze the Propagation of Aging. / Whitwell, Harry J.; Bacalini, Maria Giulia; Blyuss, Oleg; Chen, Shangbin; Garagnani, Paolo; Gordleeva, Susan Yu; Jalan, Sarika; Ivanchenko, Mikhail; Kanakov, Oleg; Kustikova, Valentina; Mariño, Ines P.; Meyerov, Iosif; Ullner, Ekkehard; Franceschi, Claudio; Zaikin, Alexey.

In: Frontiers in Aging Neuroscience, Vol. 12, 136, 25.05.2020.

Research output: Contribution to journalArticlepeer-review

Harvard

Whitwell, HJ, Bacalini, MG, Blyuss, O, Chen, S, Garagnani, P, Gordleeva, SY, Jalan, S, Ivanchenko, M, Kanakov, O, Kustikova, V, Mariño, IP, Meyerov, I, Ullner, E, Franceschi, C & Zaikin, A 2020, 'The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging', Frontiers in Aging Neuroscience, vol. 12, 136. https://doi.org/10.3389/fnagi.2020.00136

APA

Whitwell, H. J., Bacalini, M. G., Blyuss, O., Chen, S., Garagnani, P., Gordleeva, S. Y., Jalan, S., Ivanchenko, M., Kanakov, O., Kustikova, V., Mariño, I. P., Meyerov, I., Ullner, E., Franceschi, C., & Zaikin, A. (2020). The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging. Frontiers in Aging Neuroscience, 12, [136]. https://doi.org/10.3389/fnagi.2020.00136

Vancouver

Author

Whitwell, Harry J. ; Bacalini, Maria Giulia ; Blyuss, Oleg ; Chen, Shangbin ; Garagnani, Paolo ; Gordleeva, Susan Yu ; Jalan, Sarika ; Ivanchenko, Mikhail ; Kanakov, Oleg ; Kustikova, Valentina ; Mariño, Ines P. ; Meyerov, Iosif ; Ullner, Ekkehard ; Franceschi, Claudio ; Zaikin, Alexey. / The Human Body as a Super Network : Digital Methods to Analyze the Propagation of Aging. In: Frontiers in Aging Neuroscience. 2020 ; Vol. 12.

Bibtex

@article{95e60551a90a444fa17ec7819a1bc355,
title = "The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging",
abstract = "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.",
keywords = "aging, digital medicine, inflammaging, network analysis, propagation of aging",
author = "Whitwell, {Harry J.} and Bacalini, {Maria Giulia} and Oleg Blyuss and Shangbin Chen and Paolo Garagnani and Gordleeva, {Susan Yu} and Sarika Jalan and Mikhail Ivanchenko and Oleg Kanakov and Valentina Kustikova and Mari{\~n}o, {Ines P.} and Iosif Meyerov and Ekkehard Ullner and Claudio Franceschi and Alexey Zaikin",
note = "{\textcopyright} 2020 Whitwell, Bacalini, Blyuss, Chen, Garagnani, Gordleeva, Jalan, Ivanchenko, Kanakov, Kustikova, Mari{\~n}o, 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.",
year = "2020",
month = may,
day = "25",
doi = "10.3389/fnagi.2020.00136",
language = "English",
volume = "12",
journal = "Frontiers in Aging Neuroscience",
issn = "1663-4365",
publisher = "Frontiers Media S.A.",

}

RIS

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

SN - 1663-4365

M1 - 136

ER -