Stochastic Modeling of Patterning and Growth Control in the Development of Multicellular Organisms

Project: Research

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A morphogen is a signaling molecule governing the pattern of tissue development.Morphogens spread from a localized source and form a concentration gradient across adeveloping tissue, and induce specific cellular responses depending on its localconcentration. A morphogen gradient is not only responsible for tissue patterning, butalso contributes to the growth of tissue. Most theoretical studies have focused onmorphogen-mediated growth and patterning in an “ideal” environment. However, themost important focus should be on how a precise and robust morphogen-mediateddevelopment of multicellular organisms can be maintained under genetic mutations andenvironmental changes. In this project, we aim to understand how the regulation ingrowth process plays an important role on the robustness of morphogen-mediatedpatterning in a noisy environment, such as in an in-vivo biological system.In order to study the correlation between the regulated growth process and therobustness of patterning, we propose to build a spatial stochastic model for morphogensystems with multiple growth mechanisms, which are suggested by the experimentaldata from the system of the Drosophila wing disc. The model will show whatcombination of mechanisms can achieve multiple performance objectives for a robustand precise patterning. Aside from modeling aspect, we will develop a spatially hybridstochastic algorithm for improving efficiency and accuracy in our computational studies.The method combines the advantages of stochastic partial differential equationsapproach, improving the efficiency, and reaction-diffusion stochastic simulationalgorithm, guaranteeing the accuracy. The long-term aim of our study is to indicate thefactors that relate to various abnormal developments of embryos. Furthermore, after theresults are validated by clinical and experimental data, we will advance ourunderstanding of prognosis and treatment for the abnormal growth of human beings.


Project number9048043
Grant typeECS
Effective start/end date1/12/1520/10/20

    Research areas

  • stochastic modeling ,numerical stochastic methods,systems biology,stochastic PDEs,