Reduction of a biochemical network mathematical model by means of approximating activators and inhibitors as perfect inverse relationships

TL;DR

This paper examines when qualitative topological features of biochemical networks can reliably predict their quantitative behavior, especially when approximating activators and inhibitors as perfect inverse relationships.

Contribution

It investigates the conditions under which simplifying assumptions about activation and inhibition as inverse relationships are valid for modeling biochemical networks.

Findings

Qualitative network features often do not accurately predict quantitative behavior.

Approximating activators and inhibitors as perfect inverses can lead to significant discrepancies.

The validity of inverse assumptions depends on specific network conditions.

Abstract

Models of biochemical networks are usually presented as connected graphs where vertices indicate proteins and edges are drawn to indicate activation or inhibition relationships. These diagrams are useful for drawing qualitative conclusions from the identification of topological features, for example positive and negative feedback loops. These topological features are usually identified under the presumption that activation and inhibition are inverse relationships. The conclusions are often drawn without quantitative analysis, instead relying on rules of thumb. We investigate the extent to which a model needs to prescribe inhibition and activation as true inverses before models behave idiosyncratically; quantitatively dissimilar to networks with similar typologies formed by swapping inhibitors as the inverse of activators. The purpose of the study is to determine under what circumstances rudimentary qualitative assessment of network structure can provide reliable conclusions as to the quantitative behaviour of the network and how appropriate is it to treat activator and inhibitor relationships as opposite in nature.