Motivation
The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway.
Results
We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway.
Availability and implementation
We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance.
Contact
zhangle06@scu.edu.cn or lnchen@sibs.ac.cn
Supplementary information
Supplementary data are available at Bioinformatics online
Abstract.In the past few years, systems biology methods have been widely used to explore the mechanism of cancer. Various signaling pathways play the important role in cancer. And a large number of computational methods are used to model the signaling pathway. Here, we reviewed many existing mechanisms and modeling techniques, which are used to model different signaling pathways. Then we introduced the systematic workflow of a system biology approach and some fundamental analysis methods. Finally we discuss some known challenges in the process of modeling signaling pathways and what we should handle in our future work.
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