Bioinformatics. 2017 Apr 1;33(7):1057-1063. doi: 10.1093/bioinformatics/btw772.
Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations.
TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA.
TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir .
Supplementary data are available at Bioinformatics online.
不同生物网络和数据类型的整合一直是系统生物学的主要挑战。本研究介绍了转录调控通量平衡分析（TRFBA）算法，该算法使用一组表达数据来整合转录调控和代谢模型。
TRFBA 将基因的表达水平视为一个新的连续变量，并引入了两个新的线性约束。第一个约束使用一个常数参数（C）限制代谢基因支持的反应速率，该参数将表达水平转换为反应的上限。考虑到基于约束的建模的概念，第二组约束将每个靶基因的表达水平与其调节基因的表达水平相关联。还添加了一组约束和二进制变量，以防止第二组约束重叠。TRFBA 被应用于大肠杆菌和酿酒酵母模型，以估计各种环境和遗传扰动下的生长速率。评估了算法参数的误差灵敏度，以找到 C 的最佳值。结果表明，与之前提出的算法相比，该算法在定量预测生长方面有了显著的改进。测试了算法对表达数据和调控网络变化的稳健性，以评估噪声和不完整数据的影响。此外，使用添加的约束来对没有基因表达谱的扰动进行改进，表明这些约束可用于提高 FBA 的生长预测。
TRFBA 在 Matlab 软件中实现，需要 COBRA 工具箱。源代码可在 http://sbme.modares.ac.ir 免费获取。
补充数据可在生物信息学在线获得。
