Deciphering signal transduction networks in the liver by mechanistic mathematical modelling


In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.


DOI: 10.1042/BCJ20210548

Projects: LiSyM network, SMART-NAFLD

Publication type: Journal

Journal: Biochemical Journal

Citation: Biochemical Journal 479(12):1361-1374

Date Published: 30th Jun 2022

Registered Mode: by DOI

Authors: Lorenza A. D’Alessandro, Ursula Klingmüller, Marcel Schilling

help Submitter
D’Alessandro, L. A., Klingmüller, U., & Schilling, M. (2022). Deciphering signal transduction networks in the liver by mechanistic mathematical modelling. In Biochemical Journal (Vol. 479, Issue 12, pp. 1361–1374). Portland Press Ltd.

Views: 339

Created: 27th Jun 2022 at 11:28

Last updated: 26th Sep 2022 at 09:17

help Tags

This item has not yet been tagged.

help Attributions


Related items

Powered by
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH

LiSyM Seek uses essential cookies to keep your session open and to save if you accept this banner.

By continuing to use this site you agree to the use of cookies