Functional redundancy in the NF-\kappa B signalling pathway

TL;DR

This paper introduces an information-theoretic method to analyze the parameter relationships and redundancy in the NF- signaling pathway, revealing insights into network topology and parameter identifiability.

Contribution

It develops a novel approach to quantify parameter similarity and biological component orthogonality, aiding understanding of robustness and sloppiness in complex biological models.

Findings

Parameter similarity reflects network topology

Identified number of identifiable parameters in NF- pathway

Suggested experimental protocols for better parameter estimation

Abstract

The ability to represent intracellular biochemical dynamics via deterministic and stochastic modelling is one of the crucial components to move biological sciences in the observe-predict-control-design knowledge ladder. Compared to the engineering or physics problems, dynamical models in quantitative biology typically dependent on a relatively large number of parameters. Therefore, the relationship between model parameters and dynamics is often prohibitively difficult to determine. We developed a method to depict the input-output relationship for multi-parametric stochastic and deterministic models via information-theoretic quantification of similarity between model parameters and modules. Identification of most information-theoretically orthogonal biological components, provided mathematical language to precisely communicate and visualise compensation like phenomena such as biological robustness, sloppiness and statistical non-identifiability. A comprehensive analysis of the multi-parameter NF-$\kappa$B signalling pathway demonstrates that the information-theoretic similarity reflects a topological structure of the network. Examination of the currently available experimental data on this system reveals the number of identifiable parameters and suggests informative experimental protocols.