Analysis of Fn14-NF-κB Signaling Response Dynamics using a Mechanistic Model
Fn14 is a transmembrane receptor protein belonging to the tumor necrosis factor receptor (TNFR) superfamily. Many experimental reports have shown that crosslinking of the receptor by its extracellular ligand TWEAK induces prolonged activation of transcription factor NF-κB. This behavior is distinct from TNF-α receptor, which is a more well-characterized member of the TNFR family. TNF-α receptor, despite sharing many similar molecular interactions with Fn14, only transiently activates NF-κB in response to TNF-α stimulation. Here, we investigate molecular mechanisms that enable Fn14 to display such distinctive behavior. In particular, we focus on two specific features of the Fn14 pathway that potentially give rise to a positive feedback regulation and differentiate it from the TNF-α receptor signaling. By developing a mechanistic model, we analyze how these features may determine the dynamics of an Fn14-NF-κB response. Our analysis reveals that stimulation of Fn14 by TWEAK may generate highly non-linear dynamics, including stable limit cycles and bistable responses. The type of response depends both on the strength and duration of a TWEAK signal. Our predictions and analyses also show that the molecular interactions underlying the positive feedback explain the prolonged activation of NF-κB under certain parameter regimes. In light of the model predictions, we propose possible deregulations of Fn14 leading to its overexpression in solid tumors and tissue injuries.
J. Khetan and D. Barua, "Analysis of Fn14-NF-κB Signaling Response Dynamics using a Mechanistic Model," Journal of Theoretical Biology, vol. 480, pp. 34-42, Academic Press, Nov 2019.
The definitive version is available at https://doi.org/10.1016/j.jtbi.2019.07.016
Chemical and Biochemical Engineering
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Bifurcation analysis; Cell signaling; Computational modeling; Computational modeling; Glioblastoma; Rule-based modelling; Systems biology; TNFRSF12A
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Academic Press, All rights reserved.
01 Nov 2019