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Our data suggest basal AKT phosphorylation and the degree of EGF-induced activation of AKT and ERK as molecular determinants of erlotinib efficiency in PC cells.

Interferon-γ (IFNγ)-mediated signal transduction via upregulation of signal transducer and activator of transcription (STAT) 1 leads to the expression of the mucin (MUC) 4 gene in pancreatic cancer cells. Upregulation of STAT1 may also implicate STAT1 tyrosine- or serine-phosphorylation. Experimental data indicate that reaction steps involved in IFN-γ induced serine-phosphorylation of STAT1 vary between cell types in contrast to conserved IFN-γ induced tyrosine-phosphorylation of STAT1. The above observations raise the following two questions: (i) How does IFNγ stimulation regulates serine-phosphorylation of STAT1 in the pancreatic cancer cell line CD18/HPAF? (ii) Which type of STAT1 acts as a transcription factor of MUC4? Our objective is to address these two questions by data-driven mathematical modelling. Simulation results of the parameterised ordinary differential equation models show that serine-phosphorylation of unphosphorylated STAT1 occurs in the cytoplasm. In contrast, serine-phosphorylation of tyrosine-phosphorylated STAT1 can take place in the cytoplasm or in the nucleus. In addition, our results propose that unphosphorylated or serine-phosphorylated STAT1 can act as transcription factors of MUC4, either alone by progressive binding to different sites in the promoter or both together.

Despite temporal changes in the quantities of molecules, the functioning of cells also depends on their distribution within cells and in their extracellular environment. The dynamics of molecules are often governed by diffusion in heterogeneous environments consisting of dynamically changing impenetrable barriers (excluded volumes). This provides a challenge for efficient simulations of cellular processes with large numbers of molecules. To model the diffusion of molecular mass in consideration of excluded volumes, we here present an explicit numerical scheme that approximates the diffusion equation by using cellular automata. Because this approach represents molecular diffusion at the macroscopic scale, it is more amenable for efficient simulations than comparable microscopic approaches that treat diffusing molecules individually. In contrast to implicit numerical schemes (macroscopic approach), our approach is capable of accounting for excluded volumes, even if those exhibit a dynamic of their own, without increasing computational costs. The presented approach can easily be integrated into certain types of spatio-temporal multiscale models, as demonstrated by an existing model investigating cancer progression. Thereby, it allows to take the spatial effects of a heterogeneous environment on diffusing molecules into account.