About ABM

Agent-based modelling (ABM) is a simulation technique where complex emergent behaviour can be predicted as a result of local interactions between individual virtual entities or “software agents” and their environment.  It relies on modelling the essential components of the system and how these components interact with each other. The interaction of these components together defines the behaviour of the whole system. This modelling approach highlights the low-level roles, which are critical contributors to system behaviour. The ABM approach has been successfully used in different fields such as ecology, economics and cell biology to explain and show how low-level interactions play an important role in the emergence of behaviour at the system level. In biology, at the macro-system level, ABM had shed light on ants foraging behaviour. At the cellular level, ABM was used to model T-cells activation in lymph nodes and epidermis wound-healing and self-organisation. The Epithelome project had illustrated the role (TGF-beta) in wound healing and illustrated that self-organisation of keratinocytes depended on cell-cell interaction and cell-substrate interaction.

References

ADRA, S., SUN, T., MACNEIL, S., HOLCOMBE, M. & SMALLWOOD, R. 2010. Development of a Three Dimensional Multiscale Computational Model of the Human Epidermis. PLoS ONE, 5, e8511.

BOGLE, G. & DUNBAR, P. R. 2009. Agent-based simulation of T-cell activation and proliferation within a lymph node. Immunol Cell Biol, 88, 172-179.

BOGLE, G. & DUNBAR, P. R. 2010. T cell responses in lymph nodes. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2, 107-116.

BOGLE, G. & DUNBAR, P. R. 2012. On-Lattice Simulation of T Cell Motility, Chemotaxis, and Trafficking in the Lymph Node Paracortex. PLoS ONE, 7, e45258.

COAKLEY S, SMALLWOOD R, HOLCOMBE M. Using x-machines as a formal basis for describing agents in agent-based modelling. SIMULATION SERIES. 2006;38(2):33.

HOLCOMBE M, ADRA S, BICAK M, CHIN S, COAKLEY S, GRAHAM AI, et al. Modelling complex biological systems using an agent-based approach. Integrative biology: quantitative biosciences from nano to macro. 2012;4(1):53-64.

JACKSON, D. E., HOLCOMBE, M. & RATNIEKS, F. L. W. 2004. Trail geometry gives polarity to ant foraging networks. Nature, 432, 907-909.

KAUL H, VENTIKOS Y. Investigating biocomplexity through the agent-based paradigm. Briefings in Bioinformatics. 2013.

ROBINSON, E. J. H., JACKSON, D. E., HOLCOMBE, M. & RATNIEKS, F. L. W. 2005. Insect communication: /`No entry/’ signal in ant foraging. Nature, 438, 442-442.

SUN, T., MCMINN, P., HOLCOMBE, M., SMALLWOOD, R. & MACNEIL, S. 2008. Agent Based Modelling Helps in Understanding the Rules by Which Fibroblasts Support Keratinocyte Colony Formation. PLoS ONE, 3, e2129.