Integrating phosphoproteomic time series data into prior knowledge networks

Abstract : Traditional canonical signaling pathways help to understand overall signaling processes inside the cell. Large scale phosphoproteomic data provide insight into alterations among different proteins under different experimental settings. Our goal is to combine the traditional signaling networks with complex phosphoproteomic time-series data in order to unravel cell specific signaling networks. On the application side, we apply and improve a caspo time series method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM BreastCancer challenge. We infer a family of Boolean models from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. The obtained results are comparable to the top performing teams of the HPN-DREAM challenge. We also discovered that the similar models are clustered to getherin the solutions space. On the computational side, we improved the method to discover diverse solutions and improve the computational time.
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Misbah Razzaq. Integrating phosphoproteomic time series data into prior knowledge networks. Bioinformatics [q-bio.QM]. École centrale de Nantes, 2018. English. ⟨NNT : 2018ECDN0048⟩. ⟨tel-02021019⟩

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