a dynamic analysis of irs-pkr signaling in liver cells a discrete modeling approachirs-pkr信号的动态分析肝细胞离散建模方法.pdfVIP

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach Ming Wu1., Xuerui Yang2., Christina Chan1,2,3* 1 Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America, 2 Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, Michigan, United States of America, 3 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America Abstract A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA- dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic b