Figure 2.

Genetic algoritm scheme. Flow chart of the estimate procedure using the genetic algorithm (GA). Every unknown model parameter is called a " gene", while the whole set of parameters to be estimated is defined as the " genome". Every genome is contained within an " individual", the computational entity able to " evolve". An ensemble of genomes corresponds to a "population". The GA procedure begins with an initial random guess of the parameters values used to run a simulation of the model network. This first step is iterated for all the individuals belonging to different populations. For each individual, the simulated time course of the concentrations for specific proteins are compared with the experimental measures and the distances between the functions are calculated. Every individual is thus related to a fitness index, measuring the degree of compatibility of the genome with the experimental constraints. A small number of individuals are selected based on their fitness but also on probabilistic rules: they will have the genomes randomly mutated by genetic operators, giving birth to a new offspring that enters the next generation. At each round the plot describing the evolution of the best fitness computed until then is updated: when it clearly saturates the algorithm stops and the genome corresponding to that fitness is the solution of the algorithm.

Arisi et al. BMC Neuroscience 2006 7(Suppl 1):S6   doi:10.1186/1471-2202-7-S1-S6