Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.