Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A multi-agent system for the quantitative simulation of biological networks
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Biomolecular swarms—an agent-based model of the lactose operon
Natural Computing: an international journal
Journal of Biomedical Informatics
The virtual reality applied to biology understanding: The in virtuo experimentation
Expert Systems with Applications: An International Journal
Self-organized middle-out abstraction
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
Hi-index | 0.00 |
We utilize an agent-based approach to model the MAPK signaling pathway, in which we capture both individual and group behaviour of the biological entities inside the system. In an effort to adaptively reduce complexity of interactions among the simulated agents, we propose a bottom-up approach to find and group similar agents into a single module which will result in a reduction in the complexity of the system. Our proposed adaptive method of grouping and ungrouping captures the dynamics of the system by identifying and breaking modules adaptively as the simulation proceeds. Experimental results on our simulated MAPK signaling pathway show that our proposed method can be used to identify modules in both stable and periodic systems.