Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.
The self-renewal and multipotent potentials in neural stem cells(NSCs)maintain the normal physiological functions of central nervous system(CNS).The ab-normal differentiation of NSCs would lead to CNS disorders.However,the mechanisms of how NSCs differentiate into astrocytes,oligodendrocytes(OLs)and neurons are still unclear,which is mainly due to the complexity of differentiation processes and the limita-tion of the cell separation method.In this study,we modeled the dynamics of neural cell interactions in a systemic approach by mining the high-throughput genomic and proteomic data,and identified 8615 genes that are involved in various biological processes and functions with significant changes during the differentiation processes.A total of 1559 genes are specifically expressed in neural cells,in which 242 genes are NSC specific,215 are astrocyte specific,551 are OL specific,and 563 are neuron specific.In addition,we proposed 57 transcriptional regulators specifically expressed in NSCs may play essential roles in the development courses.These findings provide more comprehensive analysis for better understanding the endogenous mechanisms of NSC fate determination.
Kai WangHaifeng WangJiao WangYuqiong XieJun ChenHuang YanZengrong LiuTieqiao Wen
