Comparison of reaction networks of insulin signaling

TL;DR

This paper compares insulin signaling networks in healthy and diabetic cells using chemical reaction network theory, revealing structural bifurcations, stability differences, and new analytical methods that deepen understanding of insulin resistance.

Contribution

It introduces network translation analysis to compare insulin signaling networks, uncovering stability properties and structural bifurcations not previously identified.

Findings

Presence of a structural bifurcation in signaling processes.

Both networks tend to be monostationary with high propensity.

INRES exhibits higher stability beyond monostationarity.

Abstract

Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends these analyses using various methods which give further insights regarding insulin signaling. Using embedded networks, we discuss evidence of the presence of a structural "bifurcation" in the signaling process between INSMS and INRES. Concordance profiles of INSMS and INRES show that both have a high propensity to remain monostationary. Moreover, the concordance properties allow us to present heuristic evidence that INRES has a higher level of stability beyond its monostationarity. Finally, we discuss a new way of analyzing reaction networks through network translation. This method gives rise to three new insights: (i) each stoichiometric class of INSMS and INRES contains a unique positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially stable and is a global attractor in its stoichiometric class; and (iii) any positive equilibrium of INRES is locally asymptotically stable. These results open up opportunities for collaboration with experimental biologists to understand insulin signaling better.