On Computational Complexity of Pathway-Inspired Networking

抄録

To model the signaling pathway network, to explain what cause the dynamics of signal transduction is important. The research presented here is to investigate the computational complexity of the reconstruction algorithms for dynamic networks inspired by signaling pathway in the cell, based on the biochemical representation of cell communication. An algorithm for pathway-inspired networking based on the pathways of GEFs/GAPs and the pathways of kinases/phosphatases is proposed in this paper. Through the control of the enzyme-like nodes, the efficiency of the networking process can be increased. The topological structure of the dynamic networks and the signals of network dynamics are used as the references for analysis of the coupling relation between the symbolic logical model and the nonlinear dynamics model. Based on the GTP/GDP switches and the kinase/phosphatase switches, the above-mentioned pathway-inspired networking algorithm is applied to explain the feedback mechanism of switching process for mitosis/meiosis in fission yeast.