Upon elevation of plasma glucose concentration, pancreatic β-cells generate bursts of action potentials to induce cyclic changes in [Ca2+]i and regulate pulsatile insulin release. This glucose-dependent insulin secretion is synergistically enhanced by an incretin hormone, gucagon-like peptide-1 (GLP-1). To date, it has been well established that GLP-1 activates adenylate cyclase through binding to its G-protein-coupled receptor and increases [cAMP], the key signal underlying the insulinotropic effects. The increase in [cAMP] subsequently activates protein kinase A (PKA) and cAMP-regulated guanine nucleotide exchange factor (cAMP-GEF or Epac), modulating the activities of ion channels at the plasma membrane and ER, which in turn modify the pattern of burst as well as Ca2+ transients. However, because of complex interactions between multiple cellular factors and ion channels or transporters involved in the GLP-1 effects, quantitative aspects of the molecular mechanisms have not yet been determined. In order to overcome this difficulty, we adopted a strategy of modeling analysis; simulation study and mathematical analysis. First, our β-cells model [1] was updated and GLP-1 receptor signal transduction model [2] was incorporated. The modulatory effects of PKA and Epac on ion channels, voltage-gated Ca2+ channels (VGCC), delayed rectifying K+ channels (KDR), ATP-sensitive K+ channel (KATP) and non-selective cation channels (NSCC), were then reconstructed based on experimental observations in electrophysiological and pharmacological studies. Finally, lead potential (VL) analysis [1] were applied to quantitatively determine the functional role of each ion channel in GLP-1 induced increase in membrane excitability (Fig. 1). The analysis revealed that activation of NSCC strongly contributes to shorten burst interval, whereas deactivation of KATP causes insufficient contribution to hyperpolarization during plateau leading to prolongation of burst duration during GLP-1 stimulation. Simulation studies by constructing a comprehensive model based on experimental records and mathematical analyses of the model behavior provide a deeper and more objective insight into cellular functions.