Modeling the interaction of the immune system with HIV
Mathematical and statistical approaches to AIDS epidemiology
Cell Modeling Using Agent-Based Formalisms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Sufficiency verification of HIV-1 pathogenesis based on multi-agent simulation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Guest Editor's Introduction: On the Evolution of Applying Agent Technology to Healthcare
IEEE Intelligent Systems
Multiagent systems for cardiac pacing simulation and control
AI Communications - Agents Applied in Health Care
Exploring the vast parameter space of multi-agent based simulation
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
A massively multi-agent system for discovering HIV-Immune interaction dynamics
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
An agent-oriented conceptual framework for systems biology
Transactions on Computational Systems Biology III
An enhanced massively multi-agent system for discovering HIV population dynamics
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Editorial: Software agents in health care
Artificial Intelligence in Medicine
Anthropic agency: a multiagent system for physiological processes
Artificial Intelligence in Medicine
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Objectives: The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice. Methods and materials: In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared. Results: Important advantages of the proposed method are noted and demonstrated: these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%. Conclusions: The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections.