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The human brain is one of the most complex living structures in the knownUniverse. It consists of billions of neurons and synapses. Due to its intrinsiccomplexity, it can be a formidable task to accurately depict brain's structureand functionality. In the past, numerous studies have been conducted onmodeling brain disease, structure, and functionality. Some of these studieshave employed Agent-based approaches including multiagent-based simulationmodels as well as brain complex networks. While these models have all beendeveloped using agent-based computing, however, to our best knowledge, none ofthem have employed the use of Agent-Oriented Software Engineering (AOSE)methodologies in developing the brain or disease model. This is a problembecause without due process, developed models can miss out on importantrequirements. AOSE has the unique capability of merging concepts frommultiagent systems, agent-based modeling, artificial intelligence, besidesconcepts from distributed systems. AOSE involves the various tested softwareengineering principles in various phases of the model development ranging fromanalysis, design, implementation, and testing phases. In this paper, we employthe use of three different AOSE methodologies for modeling the MultipleSclerosis brain disease namely GAIA, TROPOS, and MASE. After developing themodels, we further employ the use of Exploratory Agent-based Modeling (EABM) todevelop an actual model replicating previous results as a proof of concept. Thekey objective of this study is to demonstrate and explore the viability andeffectiveness of AOSE methodologies in the development of complex brainstructure and cognitive process models. Our key finding include demonstrationthat AOSE methodologies can be considerably helpful in modeling various livingcomplex systems, in general, and the human brain, in particular.

Modeling the Multiple Sclerosis Brain Disease Using Agents: What Works and What Doesn't?