School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor, Malaysia.
BMC Bioinformatics. 2010 Feb 9;11:83. doi: 10.1186/1471-2105-11-83.
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).
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.
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.
萜类生物合成的动态模型的开发和模拟提供了一个系统的视角，使我们对该生化途径的结构如何影响化合物合成有了新的认识。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生产的反应。在这项研究中，基于混合功能 Petri 网 (HFPN) 技术构建了萜类生物合成途径的动态模型。该技术是三种其他扩展 Petri 网技术（即混合 Petri 网 (HPN)、动态 Petri 网 (HDN) 和功能 Petri 网 (FPN)）的融合。
构建萜类代谢模型所需的生物数据来自文献和生物数据库。这些数据被用作构建 HFPNe 模型和生成控制模型全局行为的参数的构建块。对动态模型进行了模拟，并针对从广泛的文献搜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。通过引入抑制剂可以观察到影响萜类产生的中间产物之间的相互作用，这些抑制剂在途径内和途径之间建立了反馈环和串扰。
尽管这个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，并应该导致对萜类生物合成有更全面的理解。
