Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA.
Bioinformatics. 2018 Nov 15;34(22):3882-3888. doi: 10.1093/bioinformatics/bty445.
Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.
In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.
Code is available in the Supplementary data available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
转录组学和蛋白质组学数据已被整合到基于约束的模型中，以影响通量预测。然而，最近有报道称，对于大肠杆菌和酿酒酵母，简约通量平衡分析（pFBA）的模型预测，不使用表达数据，与将转录组学或蛋白质组学数据整合到基于约束的模型中的各种算法的预测一样好或更好。
在本文中，我们描述了一种新的基于约束的方法，称为线性边界通量平衡分析（LBFBA），它使用表达数据（转录组学或蛋白质组学）来预测代谢通量。该方法使用表达数据对个体通量施加软约束，这些约束可以被违反。在用于预测其他条件下的表达数据通量之前，软约束中的参数首先根据训练表达和通量数据集进行估计。我们将 LBFBA 应用于大肠杆菌和酿酒酵母数据集，发现 LBFBA 的预测比 pFBA 的预测更准确，平均归一化误差大约是 pFBA 的一半。我们首次证明了一种将表达数据整合到基于约束的模型中的计算方法，并且比 pFBA 更能提高定量通量预测的准确性。
代码可在 Bioinformatics 在线提供的补充数据中获得。
补充数据可在 Bioinformatics 在线获得。
