Journal
ADVANCED THEORY AND SIMULATIONS
Volume 4, Issue 7, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adts.202100005
Keywords
cell-fate decision; multiscale computations; positional information; self-organization; zebra stripe
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Funding
- Zhejiang University
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation [2019M662027]
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The exact self-organization mechanism underlying complex multi-physics coupling developmental processes has been studied using multiscale computations based on first principles. It was found that cell cycles can self-organize into periodic stripes in E. coli populations, relying on a moving graded nutrient concentration profile to provide positional information for cells. The research also showed that the statistical cell cycle distribution can collapse to a universal function and exhibit scale invariance, with transitions between gene patterns depending on the radial distribution mode of genetic oscillations in cell populations.
A big challenge in current biology is to understand the exact self-organization mechanism underlying complex multi-physics coupling developmental processes. Using multiscale computations of subcellular gene expressions and cell population dynamics that are based on first principles, it is shown that cell cycles can self-organize into periodic stripes in the development of E. coli populations from one single cell, relying on the moving graded nutrient concentration profile, which provides directing positional information for cells to keep their cycle phases in place. Resultantly, the statistical cell cycle distribution within the population is observed to collapse to a universal function and shows a scale invariance. Depending on the radial distribution mode of genetic oscillations in cell populations, a transition between gene patterns is achieved. When an inhibitor-inhibitor gene network is subsequently activated by a gene-oscillatory network, cell populations with zebra stripes can be established, with the positioning precision of cell-fate-specific domains influenced by cells' speed of free motions. Such information may provide important implications for understanding relevant dynamic processes of multicellular systems, such as, biological development.
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