Techniques of Model Reductions in Biochemical Cell Signaling Pathways

TL;DR

This paper reviews and develops techniques for reducing complex biochemical cell signaling pathway models, enabling better understanding and efficient analysis of their dynamics with minimal loss of accuracy.

Contribution

It introduces new approaches and applies existing reduction methods to various signaling pathways, improving model simplicity and interpretability.

Findings

Good agreement between original and reduced models

Reduction techniques effectively simplify complex pathways

Enhanced understanding of signaling dynamics

Abstract

There are many mathematical models of biochemical cell signaling pathways that contain a large number of elements (species and reactions). This is sometimes a big issue for identifying critical model elements and describing the model dynamics. Thus, techniques of model reduction can be used as a mathematical tool in order to minimize the number of variables and parameters. In this thesis, we review some well-known methods of model reduction for cell signaling pathways. We have also developed some approaches that provide us a great step forward in model reduction. The techniques are quasi steady state approximation (QSSA), quasi equilibrium approximation (QEA), lumping of species and entropy production analysis. They are applied on protein translation pathways with microRNA mechanisms, chemical reaction networks, extracellular signal regulated kinase (ERK) pathways, NFkB signal transduction pathways, elongation factors EFTu and EFTs signaling pathways and Dihydrofolate reductase (DHFR) pathways. The main aim of this thesis is to reduce the complex cell signaling pathway models. This provides one a better understanding of the dynamics of such models and gives an accurate approximate solution. Results show that there is a good agreement between the original models and the simplified models.