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Introduction
In a biological context, the cells can be viewed as networks of molecules connected by chemical reactions. The development of massive data collection techniques, as cDNA microarrays and RNA-Seq, allows the simultaneous verification of cell"s components estate in multiples instances of time. Computational methods have been extensively used to analyze and to interpret this amount of generated data. In particular, gene regulatory networks (GRN) are used to indicate how the genes are regulated and consequently give insights about the activity of live organisms in molecular level. However, there is an important problem: how to validate the network identification results? This work presents an approach to validate such algorithms by considering the AGN model generation and simulation.
This project provides an Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data, which ca
