Analysis of optimality in natural and perturbed metabolic networks
Analysis of optimality in natural and perturbed metabolic networks
复制标题
DOI：
10.1073/pnas.232349399
复制
发表时间：
2002-11-12
影响因子：
11.1
通讯作者：
Church, GM
中科院分区：
文献类型：
--
作者：
Segrè, D;Vitkup, D;Church, GM
全细胞计算建模的一个重要目标是将详细的生化信息与生物直觉相结合，以产生可测试的预测。通量平衡分析（FBA）基于大肠杆菌等原核生物在沿着进化过程中生长性能最大化的假设，利用线性规划方法预测稳态时的代谢通量分布。证实早期的结果，我们表明，最近的细胞内流量数据野生型E。coli JM101的结果与FBA的预测结果吻合较好。虽然野生型细菌的最优性假设是合理的，但同样的论点可能对基因工程敲除或其他没有受到长期进化压力的细菌菌株无效。我们通过引入代谢调节最小化（MOMA）的方法来解决这一点，由此我们测试了敲除代谢通量相对于野生型的通量配置进行最小重新分配的假设。MOMA采用二次规划，以确定通量空间中的一个点，这是最接近的野生型点，兼容的基因删除约束。比较MOMA和FBA的预测与E.大肠杆菌丙酮酸激酶突变体PB25，我们发现MOMA显示出显着更高的相关性比FBA。我们的方法得到了E.大肠杆菌敲除生长速率。因此，它可以用于预测扰动代谢网络的行为，其增长性能一般是次优的。MOMA及其未来可能的扩展可能有助于理解新陈代谢的进化优化。
An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.