Modeling of immune life history and body growth: the role of antigen burden

In this paper, a recently developed mathematical model of age related changes in population of peripheral T cells (Romanyukha, Yashin, 2003) is used to describe ontogenetic changes of the immune system. The treatise is based on the assumption of linear dependence of antigen load from basal metabolic rate, which, in turn, depends on body mass following the allometric relationship – 3/4 power scaling law (Kleiber, 1932; West, Brown, 2005). Energy cost of antigen burden, i.e. the energy needed to produce and maintain immune cells plus the energy loss due to infectious diseases, is estimated and used as a measure of the immune system effectiveness. The dependence of optimal resource allocation from the parameters of antigen load is studied.

Modeling of Immunosenescence and Risk of Death from Respiratory Infections: Evaluation of the Role of Antigenic Load and Population Heterogeneity

It is well known that efficacy of immune functions declines with age. It results in an increase of severity and duration of respiratory infections and also in dramatic growth of risk of death due to these diseases after age 65. The goal of this work is to describe and investigate the mechanism underlying the age pattern of the mortality rate caused by infectious diseases and to determine the cause-specific hazard rate as a function of immune system characteristics. For these purposes we develop a three-compartment model explaining observed risk-of-death. The model incorporates up-to-date knowledge about cellular mechanisms of aging, disease dynamics, population heterogeneity in resistance to infections, and intrinsic aging rate. The results of modeling show that the age-trajectory of mortality caused by respiratory infections may be explained by the value of antigenic load, frequency of infections and the rate of aging of the stem cell population (i.e. the population of T-lymphocyte progenitor cells). The deceleration of infection-induced mortality at advanced age can be explained by selection of individuals with a slower rate of stem cell aging. Parameter estimates derived from fitting mortality data indicate that infection burden was monotonically decreasing during the twentieth century, and changes in total antigenic load were gender-specific: it experienced periodic fluctuations in males and increased approximately two-fold in females

Individual fecundity dynamically predicts remaining life expectancy in medflies

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Senescence can play an essential role in modelling and estimation of vector based epidemiological indicators: demographical approach

In the paper basic epidemiological indicators, produced by an aging population of vectors, are calculated. In the study we follow two lines: calculations for demographically structured population and individual life-history approach. We discuss the advantages and limitations of these approaches and compare the results of our calculations with epidemiological indicators obtained for non-aging population of vectors.Gibraltar, age effect, disease control, gerontology

A frailty model for (interval) censored family survival data, applied to the age at onset of non-physical problems

Family survival data can be used to estimate the degree of genetic and environmental contributions to the age at onset of a disease or of a specific event in life. The data can be modeled with a correlated frailty model in which the frailty variable accounts for the degree of kinship within the family. The heritability (degree of heredity) of the age at a specific event in life (or the onset of a disease) is usually defined as the proportion of variance of the survival age that is associated with genetic effects. If the survival age is (interval) censored, heritability as usually defined cannot be estimated. Instead, it is defined as the proportion of variance of the frailty associated with genetic effects. In this paper we describe a correlated frailty model to estimate the heritability and the degree of environmental effects on the age at which individuals contact a social worker for the first time and to test whether there is a difference between the survival functions of this age for twins and non-twins. © 2009 The Author(s)