Emergence of system-level properties in biological networks from cellular automata evolution

The increasing number of novel theoretical and numerical tools developed in the field of systems biology requires more and more quantitative data and system-level knowledge. On the other hand, while biotechnologies have greatly evolved during the last decade, the time and cost required for experimental measurements, especially in the case of time-series data, are still rather high. In-silico models can overcome these drawbacks, provided they are realistic enough to produce valuable experimental data useful to test and validate reverse engineering algorithms. In the present work, a novel approach for the generation of random in-silico models of biological interaction systems is proposed. Interaction network models are automatically generated by means of cellular automata and properties common to real biological networks are reproduced as emergent properties of complex systems.