IEEE/ACM Trans Comput Biol Bioinform. 2020 Jan-Feb;17(1):27-36. doi: 10.1109/TCBB.2018.2853724. Epub 2018 Jul 11.
The calculation of steady-state metabolite concentrations in metabolic reaction network models is the first step in the sensitivity analysis of a metabolic reaction system described by differential equations. However, this calculation becomes very difficult when the number of differential equations is more than 100. In the present study, therefore, we investigated a calculation procedure for obtaining true steady-state metabolite concentrations both efficiently and accurately even in large-scale network models. For convenience, a linear pathway model composed of a simple Michaelis-Menten rate law and two TCA cycle models were used as case studies. The calculation procedure is as follows: first solve the differential equations by a numerical method for solving initial-value problems until the upper several digits of the calculated values stabilize, and then use these values as initial guesses for a root-finding technique. An intensive investigation indicates that the S-system technique, finding roots in logarithmic space and providing a broader convergence region, is superior to the Newton-Raphson technique, and the algorithm using the S-system technique successfully provides true steady-state values with machine accuracy even with 1,500 differential equations. The complex-step method is also shown to contribute to shortening the calculation time and enhancing the accuracy. The program code has been deposited to https://github.com/BioprocessdesignLab/Steadystateconc.
在代谢反应系统的微分方程描述中,计算代谢反应网络模型中的稳态代谢物浓度是进行灵敏度分析的第一步。然而,当微分方程的数量超过 100 个时,这种计算就变得非常困难。因此,在本研究中,我们研究了一种即使在大规模网络模型中也能高效准确地获得真实稳态代谢物浓度的计算程序。为了方便起见,使用了由简单的米氏动力学定律和两个 TCA 循环模型组成的线性途径模型作为案例研究。计算程序如下:首先通过求解初值问题的数值方法求解微分方程,直到计算值的前几位稳定下来,然后将这些值用作求根技术的初始猜测。深入研究表明,在对数空间中寻找根并提供更广泛的收敛区域的 S 系统技术优于牛顿-拉普森技术,并且使用 S 系统技术的算法即使使用 1500 个微分方程也能成功地以机器精度提供真实的稳态值。复杂步长法也有助于缩短计算时间和提高精度。该程序代码已存储在 https://github.com/BioprocessdesignLab/Steadystateconc。