Stanford University, Dept of Management Science and Engineering, Stanford, CA 94305, USA.
University of California, San Diego, Dept of Bioengineering, La Jolla, CA 92093, USA.
Sci Rep. 2017 Jan 18;7:40863. doi: 10.1038/srep40863.
Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.
约束重建与分析（COBRA）是目前唯一允许在基因组规模上对代谢和大分子表达（ME）进行综合建模的方法。线性优化计算 ME 模型的稳态通量解，但通量值跨越多个数量级。数据值的幅度也有很大差异。标准双精度求解器可能会返回不准确的解，或者报告不存在解。基于有理数算术的精确单纯形求解器在大型问题上（当前的 ME 模型有 70000 个约束和变量，并且会变得更大）需要近乎最优的预热开始才能实用。我们已经开发了我们的线性和非线性优化器 MINOS 的 quadruple-precision 版本，以及一种涉及 Double 和 Quad MINOS 的求解过程（DQQ），该过程可实现 ME 模型和此处测试的其他挑战性问题的可靠性和效率。DQQ 将使大规模线性和非线性模型在系统生物学和涉及多尺度数据的其他应用中得到广泛应用。
