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In Cancer science

In this study, a new mathematical model was established and validated to forecast and define sensitive targets in the Kynurenine Pathway (Kynp) in the pancreatic adenocarcinoma (PDAC). Using the Panc-1 cell line, genetic profiles of Kynp molecules were tested. qPCR data were implemented in the algorithm programming (fmincon and lsqnonlin function) to estimate 35 parameters of Kynp variables by Matlab 2017b. All tested parameters were defined as non-negative and bounded. Then, based on experimental data, the function of fmincon equation was employed to estimate the approximate range of each parameter. These calculations were confirmed by qPCR and western blot. The correlation coefficient (R) between model simulation and experimental data (72 h interval of 6 h) of every variable was > 0.988. The analyzing of reliability and predictive accuracy depending on the qPCR and western blot data showed high predictive accuracy of the model, R was >0.988. Using the model calculations, kynurenine (x3, a6), GPR35 (x4, a8), NF-kβp105 (x7, a16) and NF-kβp65 (x8, a18) were recognized as sensitive targets in the Kynp. These predicted targets were confirmed by testing genes and proteins expression responses. Therefore, this study provides new interdisciplinary evidence for Kynp sensitive targets in the treatment of PDAC.

Alahdal Murad, Sun Deshun, Duan Li, Ouyang Hongwei, Wang Manyi, Xiong Jianyi, Wang Daping


Kynurenine Pathway, Mathematical Modeling, Matlab2017b, Pancreatic Adenocarcinoma, Sensitive targets