A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models

Biosystems. 2014 Oct:124:26-38. doi: 10.1016/j.biosystems.2014.07.002. Epub 2014 Jul 23.


Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.

Keywords: Gene regulation; Hepatic metabolism; Insulin and glucagon signalling; Kinetic models; Logical networks; Model integration.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Calibration
  • Gene Regulatory Networks*
  • Kinetics
  • Models, Biological*
  • Signal Transduction*