An agent-based model of the IL-1 stimulated nuclear factor-kappa B signalling pathway

The transcription factor NF-κB is a biological component that is central to the regulation of genes involved in the innate immune system. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. We believe that computational modelling and simulation of the NF-κB signalling pathway will complement wet-lab experimental approaches, and will facilitate a more comprehensive understanding of this example of a complex biological system. In this study, we have developed an agent-based model of the IL-1 stimulated NF-κB signalling pathway, which has been calibrated to wet- lab data at the single-cell level. Through rigorous software engineering, which followed a principled approach to design and development by adherence to the CoSMoS process, we believe our model provides an abstracted view of the underlying real-world system, and can be used in a predictive capacity through in silico experimentation. A novel approach to domain modelling has been presented, which uses linear and multivariate statistical techniques to complement the Unified Modelling Language. Furthermore, in silico experimentation with the newly developed agent-based model, has confirmed the robust yet fragile nature of the signalling pathway. We have discovered that the pathway is robust to perturbations of cell membrane receptor component number, intermediate component number, and the temporal lag between cell membrane receptor activation and subsequent activation of IKK. Conversely however, in silico experimentation predicts that the pathway is sensitive to changes in the ratio of free IκBα to NF-κB, and fragile to basal dissociation of NF-κB-IκBα outside of a narrow range of probabilities.

Name dc:type.qualificationname

Ph.D.

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of York

Year dc:date.issued

2014

Authors dc:creator

• Williams, Richard
• Timmis, Jon
• Qwarnstrom, Eva E.