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Modelling and mathematical analysis of the M$_{2}$ receptor-dependent joint signalling and secondary messenger network in CHO cells.
Mathematical Medicine and Biology ( IF 1.1 ) Pub Date : 2017-05-16 , DOI: 10.1093/imammb/dqx003 Benjamin Engelhardt 1 , Janine Holze 2 , Christina Elliott 3 , George S Baillie 4 , Maik Kschischo 5 , Holger Fröhlich 6
Mathematical Medicine and Biology ( IF 1.1 ) Pub Date : 2017-05-16 , DOI: 10.1093/imammb/dqx003 Benjamin Engelhardt 1 , Janine Holze 2 , Christina Elliott 3 , George S Baillie 4 , Maik Kschischo 5 , Holger Fröhlich 6
Affiliation
The muscarinic M$_{2}$ receptor is a prominent member of the GPCR family and strongly involved in heart diseases. Recently published experimental work explored the cellular response to iperoxo-induced M$_{2}$ receptor stimulation in Chinese hamster ovary (CHO) cells. To better understand these responses, we modelled and analysed the muscarinic M$_{2}$ receptor-dependent signalling pathway combined with relevant secondary messenger molecules using mass action. In our literature-based joint signalling and secondary messenger model, all binding and phosphorylation events are explicitly taken into account in order to enable subsequent stoichiometric matrix analysis. We propose constraint flux sampling (CFS) as a method to characterize the expected shift of the steady state reaction flux distribution due to the known amount of cAMP production and PDE4 activation. CFS correctly predicts an experimentally observable influence on the cytoskeleton structure (marked by actin and tubulin) and in consequence a change of the optical density of cells. In a second step, we use CFS to simulate the effect of knock-out experiments within our biological system, and thus to rank the influence of individual molecules on the observed change of the optical cell density. In particular, we confirm the relevance of the protein RGS14, which is supported by current literature. A combination of CFS with Elementary Flux Mode analysis enabled us to determine the possible underlying mechanism. Our analysis suggests that mathematical tools developed for metabolic network analysis can also be applied to mixed secondary messenger and signalling models. This could be very helpful to perform model checking with little effort and to generate hypotheses for further research if parameters are not known.
更新日期：2019-11-01
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