title: Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects.
abstract: Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model.
authors.affiliation: Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Data Analytic Department, Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore, Singapore.
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.affiliation: Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore.
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.affiliation: Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore.|Complexity Institute, Nanyang Technological University, 637723, Singapore, Singapore.|Genome Institute of Singapore, Agency for Science, Technology and Research, 138672, Singapore, Singapore.
authors.forename: Jie
authors.initials: J
authors.lastname: Zheng
mesh_terms: D002454:Cell Differentiation; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism / Q000473:pathology*; D017711:Nonlinear Dynamics; D010750:Phosphoproteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction
chemical_list: D010750:Phosphoproteins