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Models of biochemical networks are usually presented as connected graphswhere vertices indicate proteins and edges are drawn to indicate activation orinhibition relationships. These diagrams are useful for drawing qualitativeconclusions from the identification of topological features, for examplepositive and negative feedback loops. These topological features are usuallyidentified under the presumption that activation and inhibition are inverserelationships. The conclusions are often drawn without quantitative analysis,instead relying on rules of thumb. We investigate the extent to which a modelneeds to prescribe inhibition and activation as true inverses before modelsbehave idiosyncratically; quantitatively dissimilar to networks with similartypologies formed by swapping inhibitors as the inverse of activators. Thepurpose of the study is to determine under what circumstances rudimentaryqualitative assessment of network structure can provide reliable conclusions asto the quantitative behaviour of the network and how appropriate is it to treatactivator and inhibitor relationships as opposite in nature.

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