Within the Penna model, we assume a modification with a reproduction rate B that depends on the health state of the individual defined by the number of bad mutations. The idea is that biologically weaker individuals has got less chance to produce offsprings. The results obtained from simulations and for typical set of model parameters show that then the mortality rate q(a) is increasing faster with age a. The maximum age is also limited from about 50 percent of the biological maximum lifespan for the standard Penna model, to about 40 percent. We also conclude that bad mutation distribution r(a) is altered so that younger individuals may accommodate significantly more bad mutations, up to 25 percent, as compared with the standard model (5 percent). In summary, it makes sense to force less productivity for older and weaker individuals.

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There are some analytical solutions of the Penna model of biological aging; here, we discuss the approach by Coe et al. (Phys. Rev. Lett. 89, 288103, 2002), based on the concept of self-consistent solution of a master equation representing the Penna model. The equation describes transition of the population distribution at time t to next time step (t + 1). For the steady state, the population n(a, l, t) at age a and for given genome length l becomes time-independent. In this paper we discuss the stability of the analytical solution at various ranges of the model parameters--the birth rate b or mutation rate m. The map for the transition from n(a, l, t) to the next time step population distribution n(a + 1, l, t + 1) is constructed. Then the fix point (the steady state solution) brings recovery of Coe et al. results. From the analysis of the stability matrix, the Lyapunov coefficients, indicative of the stability of the solutions, are extracted. The results lead to phase diagram of the stable solutions in the space of model parameters (b, m, h), where h is the hunt rate. With increasing birth rate b, we observe critical b (0) below which population is extinct, followed by non-zero stable single solution. Further increase in b leads to typical series of bifurcations with the cycle doubling until the chaos is reached at some b (c). Limiting cases such as those leading to the logistic model are also discussed.

Abstract. The vertical disease spreading from parent to offspring and/or horizontal transmission through infection is discussed, using cellular automata approach implemented on a N ×N lattice. We concentrate on age distribution of the population, resulting from different scenario, such as whether newborns are placed in close vicinity of parents or separated from them. We also include migration aspect in context of disease spreading. Main conclusions drawn are that the vertical version is resistant to manipulations of parameters which control migration. Horizontal version represents self-recovering population unless migration of grown-ups is introduced for the case of offsprings located in vicinity of parents. Then the migration seems to be beneficial for highly infectious and lethally diseases, while it brings more deaths for milder infections.

In this paper we present results of numerical calculation of the Penna bit-string model of biological aging, modified for the case of a -dependent mutation rate m(a), where a is the parent's age. The mutation rate m(a) is the probability per bit of an extra bad mutation introduced in offspring inherited genome. We assume that m(a) increases with age a. As compared with the reference case of the standard Penna model based on a constant mutation rate m , the dynamics of the population growth shows distinct changes in age distribution of the population. Here we concentrate on mortality q(a), a fraction of items eliminated from the population when we go from age (a) to (a+1) in simulated transition from time (t) to next time (t+1). The experimentally observed q(a) dependence essentially follows the Gompertz exponential law for a above the minimum reproduction age. Deviation from the Gompertz law is however observed for the very old items, close to the maximal age. This effect may also result from an increase in mutation rate m with age a discussed in this paper. The numerical calculations are based on analytical solution of the Penna model, presented in a series of papers by Coe et al. [J. B. Coe, Y. Mao, and M. E. Cates, Phys. Rev. Lett. 89, 288103 (2002)]. Results of the numerical calculations are supported by the data obtained from computer simulation based on the solution by Coe et al.