Signal propagation in sensing and reciprocating cellular systems with spatial and structural heterogeneity

Arran Hodgkinson (Lead / Corresponding author), Giles Uzé, Ovidiu Radulescu, Dumitru Trucu

Research output: Contribution to journalArticle

1 Citation (Scopus)
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Abstract

Sensing and reciprocating cellular systems (SARs) are important for the operation of many biological systems. Production in interferon (IFN) SARs is achieved through activation of the Jak-Stat pathway, and downstream upregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the role that high and low affinity IFNs play in this process remains unclear. We present a comparative between a minimal spatio-temporal partial differential equation (PDE) model and a novel spatio-structural-temporal (SST) model for the consideration of receptor, binding, and metabolic aspects of SAR behaviour. Using the SST framework, we simulate single- and multicluster paradigms of IFN communication. Simulations reveal a cyclic process between the binding of IFN to the receptor, and the consequent increase in metabolism, decreasing the propensity for binding due to the internal feedback mechanism. One observes the effect of heterogeneity between cellular clusters, allowing them to individualise and increase local production, and within clusters, where we observe ‘subpopular quiescence’; a process whereby intra-cluster subpopulations reduce their binding and metabolism such that other such subpopulations may augment their production. Finally, we observe the ability for low affinity IFN to communicate a long range signal, where high affinity cannot, and the breakdown of this relationship through the introduction of cell motility. Biological systems may utilise cell motility where environments are unrestrictive and may use fixed system, with low affinity communication, where a localised response is desirable.
Original languageEnglish
Pages (from-to)1900-1936
Number of pages37
JournalBulletin of Mathematical Biology
Volume80
Issue number7
Early online date2 May 2018
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Population dynamics
  • structured models
  • interferon signalling
  • Structured models
  • Interferon signalling

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