Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Jan-Mar;8(1):206-16. doi: 10.1109/TCBB.2009.55.
A standard approach to estimate intracellular fluxes on a genome-wide scale is flux-balance analysis (FBA), which optimizes an objective function subject to constraints on (relations between) fluxes. The performance of FBA models heavily depends on the relevance of the formulated objective function and the completeness of the defined constraints. Previous studies indicated that FBA predictions can be improved by adding regulatory on/off constraints. These constraints were imposed based on either absolute or relative gene expression values. We provide a new algorithm that directly uses regulatory up/down constraints based on gene expression data in FBA optimization (tFBA). Our assumption is that if the activity of a gene drastically changes from one condition to the other, the flux through the reaction controlled by that gene will change accordingly. We allow these constraints to be violated, to account for posttranscriptional control and noise in the data. These up/down constraints are less stringent than the on/off constraints as previously proposed. Nevertheless, we obtain promising predictions, since many up/down constraints can be enforced. The potential of the proposed method, tFBA, is demonstrated through the analysis of fluxes in yeast under nine different cultivation conditions, between which approximately 5,000 regulatory up/down constraints can be defined. We show that changes in gene expression are predictive for changes in fluxes. Additionally, we illustrate that flux distributions obtained with tFBA better fit transcriptomics data than previous methods. Finally, we compare tFBA and FBA predictions to show that our approach yields more biologically relevant results.
一种估计全基因组范围内细胞内通量的标准方法是通量平衡分析（FBA），它通过在通量（通量之间的关系）的约束下优化目标函数来实现。FBA 模型的性能在很大程度上取决于所制定的目标函数的相关性和定义的约束的完整性。先前的研究表明，通过添加调控开关约束可以提高 FBA 预测的准确性。这些约束是基于基因表达值的绝对值或相对值来施加的。我们提供了一种新的算法，该算法在 FBA 优化（tFBA）中直接使用基于基因表达数据的调控上调/下调约束。我们的假设是，如果一个基因的活性从一种条件急剧变化到另一种条件，那么由该基因控制的反应的通量将相应地变化。我们允许这些约束被违反，以考虑转录后控制和数据中的噪声。这些上调/下调约束比之前提出的开关约束更宽松。然而，我们得到了有希望的预测，因为可以强制执行许多上调/下调约束。通过在九种不同的培养条件下对酵母中的通量进行分析，证明了所提出的方法 tFBA 的潜力，在这九种条件之间，可以定义大约 5000 个调控上调/下调约束。我们表明，基因表达的变化可以预测通量的变化。此外，我们说明了使用 tFBA 获得的通量分布比以前的方法更能拟合转录组学数据。最后，我们将 tFBA 和 FBA 的预测进行比较，以表明我们的方法产生了更具生物学相关性的结果。
