A agent based modelling framework for dynamic biological systems and applications to cancer cells, G protein coupled receptors and G proteins

Benkwitz-Bedford, Sam Robin Edward (2022). A agent based modelling framework for dynamic biological systems and applications to cancer cells, G protein coupled receptors and G proteins. University of Birmingham. Ph.D.

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Abstract

Dynamic real-world biological systems are quite difficult to study because it requires us to understand the interactions within, without being able to isolate them. This hidden complexity means, when designing representative models, problems often arise in appropriate representation, abstraction and applicable comparison with real-world phenomena.

The way distinct systems evolve over time by interaction between population members and their environment can generate emergent patterns of behaviour, here we observed it indirectly via visualisation of movement. This work centres on improving our understanding of real-world complex dynamic spatial biological systems, looking at two example populations: Cancer cells and G-protein-coupled receptors (GPCRs), for support of biological exploration and hypothesis development.

A framework was developed to take sets of population tracks and digitise them in a unifying representation for observation that could also be used to design representative models. Representative visual patterns were found and replicated: strand-like movement patterns from Cancer cells and movement hot-zones in GPCR and G protein sets. We isolated and visualised movement choices in relation to position and time. Artificial neural nets (ANN) were also applied to image classification; generating similarity measures between model and biological systems. Populations could also be split with ANNs on individual track morphology to assess specific pattern subsets. We successfully developed and applied our framework by applying generalised analysis and modelling tools to gain insight into our chosen biological systems.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):