Heumann-Hötzel model for aging revisited

Ng, de Medeiros; Rn, Onody

doi:10.1103/physreve.64.041915

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…The latter can be considered as the 'Reynolds number' [414]. For large values of L the system is turbulent and is sometimes considered as a test problem for theories on turbulence [415,416]. For moderate values of L the solution is still very complex and can possess strange attractors [401,[417][418][419].…”

Section: Advances In Physics 593mentioning

confidence: 99%

Dissipative solitons

Purwins¹,

Bödeker²,

Amiranashvili³

2010

Advances in Physics

215

193

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Section: Advances In Physics 593mentioning

confidence: 99%

Dissipative solitons

Purwins¹,

Bödeker²,

Amiranashvili³

2010

Advances in Physics

215

193

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“…The Penna model of 1995 is the most widespread computer simulation of aging by mutation accumulation, since it gave from the beginning in a simple way the roughly exponential increase of mortality with age for adult humans. Had the alternative of Medeiros and Onody (2001) been published immediately after the 1993/5 models of Partridge and Barton, Jan, Dasgupta, Heumann and Hötzel on which it is based, research might have taken a different path.…”

Section: Resultsmentioning

confidence: 99%

The Penna Model of Biological Aging

Stauffer

2007

Bioinform Biol Insights

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“…In this model, each individual carries a "chronological genome" of size x max , with a survival probability per time step G(x) at age x, which can be modified according to some mutations. The model has been modified to account for its weaknesses, namely its incapacity to treat populations with many age intervals and its ability to handle mutations exclusively deleterious (de Medeiros and Onody, 2001), where the catastrophic sensescence and the Gompertz law have been demonstrated. Optimization models of the disposable soma theory have been developed by several groups (Cichon and Kozlowski, 2000;Mangel and Munch, 2005;Abrams and Ludwig, 1995;Vaupel et al, 2004;Baudisch, 2008;Drenos and Kirkwood, 2005), which describe how the optimal investment in maintenance is affected by varying the parameters that specify the schedules of reproduction and mortality.…”

Section: Computational Modelling For Biological Agingmentioning

confidence: 99%

Evolution of the rate of biological aging using a phenotype based computational model

Kittas

2010

Journal of Theoretical Biology

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.Evolution of the rate of biological aging using a phenotype based computational model Aristotelis KittasTo cite this version: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Evolution of the rate of biological aging using a phenotype based computational model Aristotelis KittasDepartment of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece AbstractIn this work I introduce a simple model to study how natural selection acts upon aging, which focuses on the viability of each individual. It is able to reproduce the Gompertz law of mortality and can make predictions about the relation between the level of mutation rates (beneficial/deleterious/neutral), age at reproductive maturity and the degree of biological aging. With no mutations, a population with low age at reproductive maturity R stabilizes at higher density values, while with mutations it reaches its maximum density, because even for large pre-reproductive periods each individual evolves to survive to maturity. Species with very short pre-reproductive periods can only tolerate a small number of detrimental mutations. The probabilities of detrimental (P d ) or beneficial (P b ) mutations are demonstrated to greatly affect the process. High absolute values produce peaks in the viability of the population over time. Mutations combined with low selection pressure move the system towards weaker phenotypes. For low values in the ratio P d /P b , the speed at which aging occurs is almost independent of R, while higher values favor significantly species with high R. The value of R is critical to whether the population survives or dies out. The aging rate is controlled by P d and P b and the amount the viability of each individual is modified, with neutral mutations allowing the system more "room" to evolve. The process of aging in this simple model is revealed to be fairly complex, yielding a rich variety of results.

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Heumann-Hötzel model for aging revisited

Cited by 16 publications

References 17 publications

Dissipative solitons

Dissipative solitons

The Penna Model of Biological Aging

Evolution of the rate of biological aging using a phenotype based computational model

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