Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Mathematical Analysis of HIV-1 Dynamics in Vivo
SIAM Review
Dynamics of complex systems
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
CAFISS: a complex adaptive framework for immune system simulation
Proceedings of the 2005 ACM symposium on Applied computing
A Hybrid Agent-Based Model of Chemotaxis
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Simulating antigenic drift and shift in influenza A
Proceedings of the 2009 ACM symposium on Applied Computing
The swarming body: simulating the decentralized defenses of immunity
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Multi-agent model of hepatitis C virus infection
Artificial Intelligence in Medicine
Hi-index | 0.00 |
Researchers of HIV-1 are today, still unable to determine exactly the biological mechanisms that cause AIDS. Various mechanisms have been hypothesized and their existences have been experimentally verified, but whether they are sufficient to account for the observed disease progression is still in question. To better understand the phenomena, HIV-1 researchers turn to scientific models for hypothesis verification. Modeling methods which rely on differential calculus to describe population dynamics, can be inconvenient for predicting nonuniform interactions on a spatial dimension. Multi-Agent (or MA) modeling approaches, on the other hand, views the immune system as a hierarchical structure of cooperating and competing agents, operating with highly coupled behaviours to exhibit emergent complexity. We adopt the latter approach to simulate the pathogenesis of HIV-1. We show the model design and the emergent results for four well-known hypotheses: Direct Effect on CD4+ cells, Rapid Viral Mutation, Syncytium Formation, and Filling of CD4+ Receptor sites under the influence of a null model for an adaptive response to HIV-1. We give the logical basis for our methodology and clarify the semantics for 'model accuracy'. Preliminary simulation results indicate that AIDS is more likely to be caused by either Rapid Viral Mutation or Syncytium Formation.