A Stochastic Model of Cell Replicative Senescence Based on Telomere Shortening, Oxidative Stress, and Somatic Mutations in Nuclear and Mitochondrial DNA

Sozou, Peter D.; Kirkwood, Thomas B. L.

doi:10.1006/jtbi.2001.2432

Cited by 94 publications

(46 citation statements)

References 24 publications

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“…Simulations using ordinary differential equations suggested that chronic increase in cortisol levels leads to faster decline in hippocampal output than acute bursts, but could be treated more efficiently. Sozou & Kirkwood (2001) modelled cell senescence based on telomere shortening and oxidative stress. The same group also described the influence of chaperones and accumulation of misfolded proteins on aging (Proctor et al ., 2005).…”

Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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“…Vondraček [31], Tan [34], Olofsson [23], Sozou and Kirkwood [33], op den Buijs et al [28], Dyson et al [9], and Portugal et al [29].…”

Section: P Olofssonmentioning

confidence: 99%

Can telomere shortening explain sigmoidal growth curves?

Olofsson

2010

Journal of Biological Dynamics

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“…telomere shortening (Olovnikov, 1973(Olovnikov, , 1996Rubin, 2002), stem cell senescence (Sharpless & DePinho, 2004), and oxidative damage (Harman, 1981;Finkel & Holbrook, 2000). These variables have been observed to correlate with aging or cellular senescence in some species and cell lines, but the causality is still widely discussed (Hopkin, 2001;Sozou & Kirkwood, 2001;Rubin, 2002;Weinert & Timiras, 2003;Cristofalo et al, 2004;de Magalhaes, 2005). The specific variables observed to correlate with aging are often either species-or individual specific and this has led to a search for more inclusive system-based models of aging (see e.g.…”

Section: Variables Correlated With Biological Agingmentioning

confidence: 99%

Aging mechanism as the “down side” of adaptation: A network approach

Borup¹,

Trusina²,

Andersson³

2008

Journal of Theoretical Biology

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Many diverse hypotheses on aging are in play. All from "aging genes" over decreasing telomere length to increased level of gene mutations has been suggested to determine an organism's lifespan, but no one unifying theory exists. As part of a growing interest towards more integrative approaches in the field we propose a simplistic model based on the "useit-or-lose-it" concept: we hypothesize that biological aging is a systemic property and the down side of adaptation in complex biological networks at various levels of organization: from brain over the immune system to specialized tissues or organs. The simple dynamical model undergoes three phases during its lifetime: 1. General plasticity (childhood), 2. Optimization/adaptation to given conditions (youth and adolescence) and 3. Steady state associated with high rigidity (aging). Furthermore, our model mimics recent data on the dynamics of the immune system during aging and, although simplistic, thus captures some essential characteristics of the aging process. Finally, we discuss the abstract model in relation to current knowledge on aging and propose experimental set-ups for testing some of the theoretical predictions.

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A Stochastic Model of Cell Replicative Senescence Based on Telomere Shortening, Oxidative Stress, and Somatic Mutations in Nuclear and Mitochondrial DNA

Cited by 94 publications

References 24 publications

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Can telomere shortening explain sigmoidal growth curves?

Aging mechanism as the “down side” of adaptation: A network approach

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