通过整合微阵列数据快速重建紧凑的上下文特异性代谢网络
Fast reconstruction of compact context-specific metabolic networks via integration of microarray data
摘要：
最近，我们提出了一种用于快速重建紧凑上下文特定代谢网络（FASTCORE）的算法，该算法允许将重建时间降低到秒的时间量级（Vlavis et al.，2014年）。这种极低的计算需求为提高模型的质量开辟了新的可能性。重建高质量的模型需要几轮模型重建、根据真实实验数据测试模型的预测、输入模型和核心反应集的管理步骤以及交叉验证分析。这些半自动化的模型策展步骤在这种程度上是不可能的竞争算法，由于他们的高计算需求。为了适应FASTCORE的集成微阵列数据，因此，我们提出了一个新的工作流程：FASTCORMICS。FASTCORMICS需要输入微阵列数据和基因组规模的重建。FASTCORMICS没有启发式参数设置，并且具有低计算需求，总体构建时间大约为几分钟。FASTCORMICS用离散化工具Barcode预处理微阵列数据（Zillox等，2007）。条形码使用关于给定微阵列平台的每个探针组的强度分布的先验知识，以在表达的基因和非表达的基因之间分离。该预处理步骤允许规避设置启发式表达阈值的需要，这对于输出模型是关键的，因为响应于该阈值，可能包括或排除替代路径或子系统，从而严重改变模型的功能。一般来说，FASTCORMICS优于竞争算法，并允许以高吞吐量的方式获得高质量，鲁棒的模型。这将允许使用代谢建模作为用于分析微阵列数据的常规过程，例如在个性化医学领域。
英文摘要：
Recently we proposed an algorithm for the fast reconstruction of compact context-specific metabolic networks (FASTCORE) that allowed dropping the reconstruction time to the time order of seconds (Vlassis et al.,2014). This extremely low computational demand opens new possibilities for improving the quality of the models. Several rounds of model reconstruction, testing of the model's predictions against real experimental data, curation steps of the input model and the set of core reactions as well as cross-validations assays are required to reconstruct high-quality models. These semi-automated model curations steps are in such extend not possible with competing algorithms due to their high computational demands. To adapt FASTCORE for the integration of microarray data, we therefore propose a new workflow: FASTCORMICS. FASTCORMICS requires as input microarray data and a Genome-scale reconstruction. FASTCORMICS is devoid of heuristic parameter settings and has a low computational demand with overall building times in the order of a few minutes. FASTCORMICS preprocesses the microarrays data with the discretization tool Barcode (Zillox et al, 2007). Barcode uses prior knowledge on the intensity distribution of each probe set for a given microarray platform to segregate between expressed genes and non-expressed genes. This preprocessing step allows circumventing the need of setting a heuristic expression threshold, which is critical for the output models as in response to this threshold alternative pathways or subsystems might be included or excluded, thereby heavily changing the functionalities of the model. In general, FASTCORMICS outperforms competing algorithms and allows obtaining high-quality, robust models in a high-throughput manner. This will allow the use of metabolic modelling as routine process for the analysis of microarray data e.g. in the field of personalized medicine.
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