title: Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways.
abstract: BACKGROUND
  Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.
  
  
  RESULTS
  We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.
  
  
  CONCLUSIONS
  The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.
authors.affiliation: Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. fzhang005@e.ntu.edu.sg.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. chen0783@e.ntu.edu.sg.|Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. chen0783@e.ntu.edu.sg.
authors.forename: Haoting
authors.initials: H
authors.lastname: Chen
authors.affiliation: Bioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, Singapore. zhao0139@e.ntu.edu.sg.
authors.forename: Li Na
authors.initials: LN
authors.lastname: Zhao
authors.affiliation: Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuhui@ntu.edu.sg.|Lab of Information Management, Changzhou University, Changzhou, Jiangsu 213164, China. liuhui@ntu.edu.sg.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: National Center for Biotechnology Information, NLM/NIH, Bethesda, MD 20894, USA. przytyck@ncbi.nlm.nih.gov.
authors.forename: Teresa M
authors.initials: TM
authors.lastname: Przytycka
authors.affiliation: Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. zhengjie@ntu.edu.sg.|Complexity Institute, Nanyang Technological University, Singapore 637723, Singapore. zhengjie@ntu.edu.sg.|Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore. zhengjie@ntu.edu.sg.
authors.forename: Jie
authors.initials: J
authors.lastname: Zheng
mesh_terms: D002450:Cell Communication; D003198:Computer Simulation; D008954:Models, Biological; D010766:Phosphorylation; D011506:Proteins / Q000378:metabolism*; D015398:Signal Transduction
chemical_list: D011506:Proteins
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