A systemic approach for modeling biological evolution using Parallel DEVS

Can Aging Develop as an Adaptation to Optimize Natural Selection? (Application of Computer Modeling for Searching Conditions When the "Fable of Hares" Can Explain the Evolution of Aging)

There are two points of view on the evolution of aging. The classical theory of aging suggests that natural selection does not efficiently eliminate mutations or alleles that are harmful to organisms at later age. Another hypothesis is that the genetic program of aging has evolved as an adaptation that contributes to the optimization of the evolutionary process. Academician V. P. Skulachev advocates the latter hypothesis, which he has illustrated with the "Fable of hares". In this paper, we have used computer simulation to search for conditions when, according to the "Fable", aging develops as an adaptation required for the evolution of useful traits. The simulation has shown that the evolutionary mechanism presented in the "Fable of hares" is only partially functional. We have found that under certain conditions, programmed deterioration of some organismal functions makes it possible to increase the efficiency of natural selection of other functions. However, we have not identified mechanisms that would ensure the distribution and support of genes of aging within the population.

Biosystem models, generated from a complex rule/reaction/influence network and from two functionality prototypes

In this work we have further developed the Direct Computer Mapping (DCM) based modelling and simulation methodology. A unified, transition-based representation of complex rule, reaction and influence networks has been introduced and two prototypes (one general state- and another general transition-prototype) have been developed for the unified functional modelling of the state and transition nodes. Starting from the network and from the functional prototypes, an automatic generation method of the graphically editable and extensible GraphML description of biosystem models has been elaborated. The new developments have been implemented in the improved kernel of DCM models. The applied knowledge representation makes possible the unified generation and execution of the balance-based quantitative and influence- or rule-based qualitative, as well as optionally time-driven, multiscale biosystem models. Application of the developed methodology has been illustrated by the improved implementation of the formerly studied and upgraded example biosystem model for combining the detailed, quantitative p53/miR34a signalling system with the pathological model through an extended rule-based coupling model.