Development of a Heuristic Machine Analogy Method for Model Simplification With an Application to a Large‐Scale Model of Gi/Gs Signaling - Liang Yang; David Finlay; Michelle Glass; Stephen Duffull (CPT: Pharmacometrics & Systems Pharmacology)
ABSTRACT Model simplification is a process to simplify large‐scale mathematical models to enable easy applications such as simulation and parameter estimation. A novel heuristic machine analogy method of model simplification was developed and applied to a motivating example of a model for cAMP signaling switch induced by Gi/Gs pathway competition for the CB1 receptor (consisting of 31 species and 76 parameters) to enable its use in estimation. The method first acquired an understanding of the mechanism by full model simulation, and then the mechanism was abstracted to a machine analogy. The machine analogy included signal start, signal mode selector, signal size regulator, and final effector, representing functions of different parts of the full model. The simplified minimal model (consisting of 11 species and 13 estimated parameters) was used for parameter estimation for Gi/Gs signaling of six CB1 agonists. The results of the minimal model suggested that six CB1 agonists have similar ratios of Gi/Gs activation, indicating Gi/Gs preference was more of a system effect rather than a ligand‐specific effect. In conclusion, the novel machine analogy method can be used to heuristically simplify a larger‐scale model while maintaining the important mechanisms. In the example here, the full Gi/Gs model of CB1 was successfully simplified, and the results indicated Gi/Gs preference is a system‐dependent effect.