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Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

机译：通过白盒，灰盒和黑匣子建模方法识别代谢途径中的助焊途径

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Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.
机译：代谢途径建模在药物设计中起着越来越大的作用，允许更好地理解生物体代谢中的潜在的调节和控制网络。然而，尽管在该领域的快速进步，途径建模可以成为研究人员的真正噩梦，特别是当少数实验数据或者途径高度复杂时。在这里，开发了三种不同的方法以模拟E.组织olyTICA的糖酵解的第二部分作为应用示例，并且已经成功地预测了最终的途径通量：一个包括详细的动态信息（白盒），另一个具有添加的调整项（灰盒）和使用人工神经网络方法（黑匣子）的最后一个。之后，每个模型用于代谢控制分析和磁通控制系数确定。该途径的前两种酶被鉴定为在助焊剂控制中发挥作用的关键酶。本研究揭示了三种建立适当模型的方法的重要性，该方法调整到代谢途径建模领域的可用数据，并且对生物学家和建模者来说可能有用。

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