FB2026_02 , released June 18, 2026
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Liu, F., Yamamoto, E., Shirahama, K., Saitoh, T., Aoyama, S., Harada, Y., Murakami, R., Matsuno, H. (2022). Analysis of Pattern Formation by Colored Petri Nets With Quantitative Regulation of Gene Expression Level.  IEEE/ACM Trans. Comput. Biol. Bioinform. 19(1): 317--327.
FlyBase ID
FBrf0252554
Publication Type
Research paper
Abstract
Modeling and simulation are becoming indispensable tools for studying multicellular events such as pattern formation during embryonic development. In this paper, we propose a new approach for analyzing multicellular biological phenomena by combining colored hybrid Petri nets (ColHPNs) with newly devised biological experiments that can control level of a gene quantitatively. With this approach, we analyzed patterning of the boundary cells in the Drosophila large intestine, where one-cell-wide domain of boundary cells differentiate through Delta-Notch signaling. Biological experiments regulating the level of Delta resulted in six distinct patterns of boundary cells correlating with the level of Delta. All these patterns were successfully reproduced by simulation based on ColHPN modeling only by changing the parameter related to the level of Delta. By monitoring the concentration of the active form of Notch in each cell during simulation, it was revealed that these distinct modes of patterning correlate with the fluctuation range of active Notch. Combination of simulation and quantitative manipulation of a gene activity described here is a reliable and powerful approach for analyzing and understanding the patterning process regulated by Notch signaling. This approach can be easily adapted to address other similar pattern formation issues in the systems biology area.
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    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    IEEE/ACM Trans. Comput. Biol. Bioinform.
    Title
    IEEE/ACM transactions on computational biology and bioinformatics
    ISBN/ISSN
    1545-5963 1557-9964
    Data From Reference
    Genes (2)