Agent-based and multicellular simulations
Instead of equations for average behaviour, agent-based models simulate every cell as an individual with rules for dividing, moving, dying and signalling, producing virtual tumours in which immune attack, drug delivery and evolution can be watched and tested.
Overview
Agent-based models represent thousands to millions of cells as discrete agents on a lattice or in continuous space, each following rules drawn from biology, with diffusing chemicals such as oxygen and drugs computed alongside. Open frameworks such as PhysiCell and CompuCell3D and the Anderson-Chaplain and Rejniak lineages have simulated tumour growth under hypoxia, immune infiltration, nanoparticle delivery and the emergence of invasive phenotypes. They are the natural tool for testing adaptive therapy schedules and spatial hypotheses, at the cost of many parameters and heavy computation.
How it works
Discrete cells with rule-based behaviour interact with each other and with continuous fields of nutrients and drugs; population behaviour emerges rather than being assumed.
- Captures spatial and cell-level heterogeneity
- Tests hypotheses no equation can express
- Open, reusable frameworks
- Many uncertain parameters
- Computationally heavy
- Validation against patient data is hard
Latest papers
topQuery for this technology: (TITLE:"Agent-based and multicellular simulations" OR ABSTRACT:"Agent-based and multicellular simulations") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Agent-based and multicellular simulations, not a curated reading list.
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