Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal ...Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.展开更多
The collective motion of rounded squares with different comer-roundness ζ is studied by molecular dynamlcs （MD） simulation in this work. Three types of translational collective motion pattern are observed, includin...The collective motion of rounded squares with different comer-roundness ζ is studied by molecular dynamlcs （MD） simulation in this work. Three types of translational collective motion pattern are observed, including＇, gliding, hopping and a mixture of gliding and hopping. Quantitatively, the dynamics of each observed ordered phase is characterized by both mean square displacement and van Hove functions for both translation and rotation. The effect of corner-roundness on the dynamics is further studied by comparing the dynamics of the rhombic crystal phases folmed by different comer-.rounded particles at a same surface fraction. The results show that as ζ increases from 0.286 to 0.667, the translational collective motion of particles changes from a gliding-dominant pattern to a hopping-dominant patte;n, whereas the rotational motion pattern is hopping-like and does not change in its type, but the rotational hopping becomes much more frequent as increases （i.e., as particles become more rounded）. A simple geometrical model is proposed to explain the trend of gliding motion observed in MD simulations.展开更多
A minimal cellular automaton model is introduced to describe the collective motion of self-propelled particles on two-dimensional square lattice. The model features discretization of directional and positional spaces ...A minimal cellular automaton model is introduced to describe the collective motion of self-propelled particles on two-dimensional square lattice. The model features discretization of directional and positional spaces and single-particle occupation on one lattice site. Contrary to the Vicsek model and its variants, our model exhibits the nonvanishing optimal noise. When the particle density increases, the collective motion is promoted with optimal noise strength and reduced with noise strength out of optimal region. In addition, when the square lattice undergoes edge percolation process, no abrupt change of alignment behaviors is observed at the critical point of percolation.展开更多
In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein inte...In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the ErdSs and Rényi （ER） model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12090052,U24A2014,and 12325405).
文摘Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.21573159 and 21621004)
文摘The collective motion of rounded squares with different comer-roundness ζ is studied by molecular dynamlcs （MD） simulation in this work. Three types of translational collective motion pattern are observed, including＇, gliding, hopping and a mixture of gliding and hopping. Quantitatively, the dynamics of each observed ordered phase is characterized by both mean square displacement and van Hove functions for both translation and rotation. The effect of corner-roundness on the dynamics is further studied by comparing the dynamics of the rhombic crystal phases folmed by different comer-.rounded particles at a same surface fraction. The results show that as ζ increases from 0.286 to 0.667, the translational collective motion of particles changes from a gliding-dominant pattern to a hopping-dominant patte;n, whereas the rotational motion pattern is hopping-like and does not change in its type, but the rotational hopping becomes much more frequent as increases （i.e., as particles become more rounded）. A simple geometrical model is proposed to explain the trend of gliding motion observed in MD simulations.
基金Project supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB33000000)the National Natural Science Foundation of China(Grant No.12090054)。
文摘A minimal cellular automaton model is introduced to describe the collective motion of self-propelled particles on two-dimensional square lattice. The model features discretization of directional and positional spaces and single-particle occupation on one lattice site. Contrary to the Vicsek model and its variants, our model exhibits the nonvanishing optimal noise. When the particle density increases, the collective motion is promoted with optimal noise strength and reduced with noise strength out of optimal region. In addition, when the square lattice undergoes edge percolation process, no abrupt change of alignment behaviors is observed at the critical point of percolation.
基金Acknowledgements The authors thank Bin Zhou and Changping Yang for helpful discussions. This work was supported by the Chinese Academy of Sciences, the Open Foundation of the State Key Laboratory of Theoretical Physics （Grant No. Y3KF321CJ1）, and the National Natural Science Foundation of China （Grant No. 10835005）.
文摘In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the ErdSs and Rényi （ER） model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.
