  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.
