见木见树：代谢建模中优化和组学网络集成方法的森林
Seeing the wood for the trees: a forest of methods for optimisation and omic-network integration in metabolic modelling
摘要：
代谢建模已经进入了一个成熟的阶段，有几十种方法和软件可供实践者和理论家使用。对于一个建模者来说，能够只见树木不见森林是不容易的。在这种类比的驱动下，我们在这里提出了一个“森林”的主要方法用于基于约束的建模在系统生物学。这为未来的建模者提供了一个基于树形结构的方法视图，也可以在http://modellingmetabolism.net上以交互式版本获得，在本手稿出版后，将不断更新新方法。我们对现有方法和工具的最新分类突出了不同分支中最有前途的方法和工具，目的是对现有方法如何杂交并变得更加复杂进行展望。然后，我们提供了第一个动手教程的多目标优化代谢模型在R。最后，我们讨论了多视图机器学习方法在多体集成中的实现。在这项工作中，我们展示了多个代谢目标之间的权衡优化，重点是通过机器学习进行组学数据集成。我们预计，结合调查，多视图机器学习的观点，以及一步一步的R教程应该对初学者和高级用户都感兴趣。
英文摘要：
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a "forest" of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridise and become more complex. We then provide the first hands-on tutorial for multi-objective optimisation of metabolic models in R. We finally discuss the implementation of multi-view machine-learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimisation of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning, and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
