agent-based model
Definition
Agent-based models (ABMs) are computational simulations where autonomous entities (agents) interact according to defined rules within an environment, generating emergent system-level behaviors. In biological systems, agents can represent cells, molecules, organisms, or populations, each with individual properties and decision-making capabilities. ABMs excel at capturing spatial heterogeneity, stochastic processes, and non-linear dynamics that arise from local interactions. Unlike equation-based models that assume population homogeneity, ABMs track individual agents over time, making them particularly valuable for studying complex biological phenomena like immune responses, tumor growth, tissue development, and ecological dynamics where individual variability and spatial organization critically influence outcomes.
Visualize agent-based model in Nodes Bio
Researchers can visualize agent interaction networks in Nodes Bio by mapping agents as nodes and their communications or influences as edges. This reveals emergent interaction patterns, identifies critical agent types that drive system behavior, and highlights network motifs that govern collective dynamics. Network analysis can uncover which agent-agent connections are essential for specific outcomes, enabling researchers to identify intervention points in complex biological systems.
Visualization Ideas:
- Agent communication networks showing information flow between individual cells or molecules
- Temporal interaction networks capturing how agent relationships evolve during simulation time steps
- Spatial proximity networks mapping which agents physically interact based on location in tissue microenvironments
Example Use Case
An immunologist develops an agent-based model of tumor-immune interactions where T cells, tumor cells, and cytokines are individual agents. Each T cell agent follows rules for migration, activation, and cytotoxic activity based on local cytokine concentrations and tumor antigen recognition. The model reveals that spatial clustering of regulatory T cells creates immunosuppressive microenvironments that shield tumor regions. By visualizing the interaction network, researchers identify that disrupting specific cytokine-mediated communications could enhance anti-tumor immunity, guiding combination immunotherapy strategies.