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Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines

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Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines. / Williams, Richard Alun; Timmis, Jon; Qwarnstrom, Eva E.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling and Theory. IEEE, 2017. p. 249-256 33.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Williams, RA, Timmis, J & Qwarnstrom, EE 2017, Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines. in Proceedings of the 2017 IEEE Congress on Evolutionary Computation: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling and Theory., 33, IEEE, pp. 249-256. https://doi.org/10.1109/CEC.2017.7969320

APA

Williams, R. A., Timmis, J., & Qwarnstrom, E. E. (2017). Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling and Theory (pp. 249-256). Article 33 IEEE. https://doi.org/10.1109/CEC.2017.7969320

Vancouver

Williams RA, Timmis J, Qwarnstrom EE. Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling and Theory. IEEE. 2017. p. 249-256. 33 doi: 10.1109/CEC.2017.7969320

Author

Williams, Richard Alun ; Timmis, Jon ; Qwarnstrom, Eva E. / Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines. Proceedings of the 2017 IEEE Congress on Evolutionary Computation: Special Session on Artificial Immune Systems: Algorithms, Simulation, Modelling and Theory. IEEE, 2017. pp. 249-256

Bibtex

@inproceedings{58add17790314faa93846651e116e9ca,
title = "Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines",
abstract = "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 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, 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. In this study, we have focused on the dynamics of the IKK complex and its activation of NF-κB. Our agent-based model suggests that the pathway is sensitive to: variations in the binding probability of IKK to the inhibited NF-κB-IκBα complex; and variations in the temporal rebinding delay of IKK.",
author = "Williams, {Richard Alun} and Jon Timmis and Qwarnstrom, {Eva E.}",
year = "2017",
month = jun,
day = "5",
doi = "10.1109/CEC.2017.7969320",
language = "English",
isbn = "9781509046027",
pages = "249--256",
booktitle = "Proceedings of the 2017 IEEE Congress on Evolutionary Computation",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Investigating IKK Dynamics in the NF-κB Signalling Pathway using X-Machines

AU - Williams, Richard Alun

AU - Timmis, Jon

AU - Qwarnstrom, Eva E.

PY - 2017/6/5

Y1 - 2017/6/5

N2 - 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 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, 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. In this study, we have focused on the dynamics of the IKK complex and its activation of NF-κB. Our agent-based model suggests that the pathway is sensitive to: variations in the binding probability of IKK to the inhibited NF-κB-IκBα complex; and variations in the temporal rebinding delay of IKK.

AB - 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 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, 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. In this study, we have focused on the dynamics of the IKK complex and its activation of NF-κB. Our agent-based model suggests that the pathway is sensitive to: variations in the binding probability of IKK to the inhibited NF-κB-IκBα complex; and variations in the temporal rebinding delay of IKK.

U2 - 10.1109/CEC.2017.7969320

DO - 10.1109/CEC.2017.7969320

M3 - Conference contribution/Paper

SN - 9781509046027

SP - 249

EP - 256

BT - Proceedings of the 2017 IEEE Congress on Evolutionary Computation

PB - IEEE

ER -