Multi‐Scale Network Model Supported by Proteomics for Analysis of Combined Gemcitabine and Birinapant Effects in Pancreatic Cancer Cells

Abstract

Gemcitabine combined with birinapant, an inhibitor of apoptosis protein antagonist, acts synergistically to reduce pancreatic cancer cell proliferation. A large‐scale proteomics dataset provided rich time‐series data on proteome‐level changes that reflect the underlying biological system and mechanisms of action of these drugs. A multiscale network model was developed to link the signaling pathways of cell cycle regulation, DNA damage response, DNA repair, apoptosis, nuclear factor‐kappa β (NF‐κβ), and mitogen‐activated protein kinase (MAPK)‐p38 to cell cycle progression, proliferation, and death. After validating the network model under different conditions, the Sobol Sensitivity Analysis was applied to identify promising targets to enhance gemcitabine efficacy. The effects of p53 silencing and combining curcumin with gemcitabine were also tested with the developed model. Merging proteomics analysis with systems modeling facilitates the characterization of quantitative relations among relevant signaling pathways in drug action and resistance, and such multiscale network models could be applied for prediction of combination efficacy and target selection.