Nature is a great designer and problem solver. The theories posit by Darwin, Mendel and all those that contributed for the modern synthesis, based on molecular biology, explained us how this could happen. Some decades ago, computer scientists start proposing computational models, called evolutionary algorithms, based on some of the processes used by nature, in order to solve problems that either do not have an analytical solution or are to costly if we apply exact methods. Along time, many complex problems were satisfactory solved by those algorithms, even if those nature-inspired heuristic methods are very simplistic, and based on a basic separation between the genotype and the phenotype. In recent years, the biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory model, first proposed by Wolfgang Banzhaf. In this talk, we will present a modification of this model, aimed at solving some of its limitations, and show experimentally that it is effective in solving a set of benchmark problems. We will also discuss some future developments of the model.