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Simulation of a Petri net-based Model of the Terpenoid Biosynthesis Pathway

机译:基于Petri网的萜类生物合成途径模型的仿真

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Background The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN). Results The biological data needed to construct the terpenoid metabolic model were gathered from the literature and from biological databases. These data were used as building blocks to create an HFPNe model and to generate parameters that govern the global behaviour of the model. The dynamic model was simulated and validated against known experimental data obtained from extensive literature searches. The model successfully simulated metabolite concentration changes over time (pt) and the observations correlated with known data. Interactions between the intermediates that affect the production of terpenes could be observed through the introduction of inhibitors that established feedback loops within and crosstalk between the pathways. Conclusions Although this metabolic model is only preliminary, it will provide a platform for analysing various high-throughput data, and it should lead to a more holistic understanding of terpenoid biosynthesis.
机译:背景技术萜类生物合成动力学模型的开发和仿真产生了系统观点,该观点为该生化途径的结构如何影响化合物合成提供了新见解。这些见解最终可能有助于确定可以通过实验操作来放大萜类化合物产生的反应。在这项研究中,建立了基于混合功能陪替氏网(HFPN)技术的萜类生物合成途径的动力学模型。该技术是其他三个扩展Petri网技术的融合,即混合Petri网(HPN),动态Petri网(HDN)和功能Petri网(FPN)。结果从文献和生物学数据库中收集了构建萜类代谢模型所需的生物学数据。这些数据被用作构建HFPNe模型并生成控制模型全局行为的参数的基础。对动态模型进行了仿真,并根据从大量文献检索中获得的已知实验数据进行了验证。该模型成功模拟了代谢物浓度随时间(pt)的变化,并且观察结果与已知数据相关。可以通过引入抑制剂来观察影响萜烯产生的中间体之间的相互作用,所述抑制剂建立了通路内的反馈环并在通路之间产生串扰。结论尽管这种代谢模型只是初步的,但它将为分析各种高通量数据提供一个平台,并且应该导致对萜类生物合成的更全面的了解。

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