Computer Modeling in the Study of Aging

Kirkwood, Tbl; Boys, Richard J.; Gillespie, Colin S.; Proctor, CJ; Shanley, Daryl P.; Wilkinson, D. J.

doi:10.1016/b978-012088387-5/50015-7

Cited by 3 publications

(1 citation statement)

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“…Muitos cientistas ainda entendem os modelos computacionais como essencialmente descritivos e, portanto, sem importância, pois, neste raciocínio, as respostas científicas fundamentais só poderiam ser obtidas em estudos experimentais. Outra crença extremamente arraigadaé que, assim que um modelo necessite de 2 ou 3 parâmetros, ele pode "explicar qualquer coisa", resultando em uma descrença de sua utilidade real(Kirkwood et al, 2006). O diálogo nosúltimos anos entre especialistas em modelagem e especialistas em biologia experimental, no entanto, tem levado a uma diminuição destes preconceitos.…”

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Ensaios sobre o surgimento, evolução e manutenção dos mecanismos de senescência em animais

Monaco¹

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Monaco TO. Essays on the emergence, evolution and maintenance od senescence mechanisms in animals [thesis]. São Paulo: Faculty of Medicine, University of Sao Paulo; SP (Brazil); 2011. 180p. The evolutionary basis of senescence is a long standing biological problem. Senescence, in the sense of progressive deterioration of an organism, is distinguished from aging, or the mere passage of time. Because, at least in mammals, aging and senescence occur simultaneously, the terms are wrongly taken as synonyms. Senescence seems explained by the wear of the organism, leading to a paradox facing the mechanisms of natural selection, because, being the biological machinery bearer of self-repair mechanisms, we would expect their improvement, with gradual elimination of the deterioration associated with aging. Historically, there were attempts to resolve this paradox by supposing that senescence confers an adaptive advantage, but this argument would require that older individuals were previously distinct from younger ones and is, therefore, circular. From mid-twentieth century, three hypotheses have prevailed. The mutation accumulation, proposed by Medawar (1951), proposes that the gradual decay of the force of selection with age favors the accumulation of deleterious genes expressed in advanced ages. The antagonistic pleiotropy, proposed by Williams (1957), argues that genes linked to beneficial and deleterious traits could, under certain conditions, be favorably selected. Finally, the disposable soma theory, proposed by Kirkwood (1975), suggests that organisms are positively selected when they invest in reproduction even at the expense of somatic maintenance. The dynamic description of the phenomena involved in each mechanism and the relative contribution of each one is still debated. The present work aims to develop stochastic computer models that mimic the conditions for each. We started by historical order, testing the mechanism proposed by Medawar, which is the subject of this thesis. The theory of mutation accumulation found critics who suggest that this mechanism would cause deleterious effects to synchronize in very advanced ages, causing senescence to be a sudden phenomenon limited to these ages. This is a contradiction with the experimental observation, because senescence is a gradual process and even wild animals exhibit senescent phenomena detectable in young ages. To keep the model consistent with the current knowledge on population genetics, this model included the effects of selection, genetic drift and different mutation rates. As predicted, in our simulations the mode of the age of onset of deleterious genes stabilized only in very advanced ages, near the end of the age distribution. Also as expected, these distributions ended in earlier ages in scenarios of higher extrinsic mortality. However, in all our simulations, there was more or less wide distribution of ages of onset around the mode. The distribution is enlarged with increased probabilities of mutation, suggesting that the ages of onset may spread thro...

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Ensaios sobre o surgimento, evolução e manutenção dos mecanismos de senescência em animais

Monaco¹

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Stochastic modelling for quantitative description of heterogeneous biological systems

2009

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Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level. Second, sophisticated statistical methods and algorithms are being used to fit both deterministic and stochastic models to time course and other experimental data. Both frameworks are needed to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.

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BASIS: an internet resource for network modelling

Gillespie

Wilkinson

Shanley

et al. 2007

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Computer Modeling in the Study of Aging

Cited by 3 publications

References 97 publications

Ensaios sobre o surgimento, evolução e manutenção dos mecanismos de senescência em animais

Ensaios sobre o surgimento, evolução e manutenção dos mecanismos de senescência em animais

Stochastic modelling for quantitative description of heterogeneous biological systems

BASIS: an internet resource for network modelling

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