Greene Jennifer L, Wäechter Andreas, Tyo Keith E J, Broadbelt Linda J
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois.
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois.
Biophys J. 2017 Sep 5;113(5):1150-1162. doi: 10.1016/j.bpj.2017.07.018.
Developing reliable, predictive kinetic models of metabolism is a difficult, yet necessary, priority toward understanding and deliberately altering cellular behavior. Constraint-based modeling has enabled the fields of metabolic engineering and systems biology to make great strides in interrogating cellular metabolism but does not provide sufficient insight into regulation or kinetic limitations of metabolic pathways. Moreover, the growth-optimized assumptions that constraint-based models often rely on do not hold when studying stationary or persistor cell populations. However, developing kinetic models provides many unique challenges, as many of the kinetic parameters and rate laws governing individual enzymes are unknown. Ensemble modeling (EM) was developed to circumnavigate this challenge and effectively sample the large kinetic parameter solution space using consistent experimental datasets. Unfortunately, EM, in its base form, requires long solve times to complete and often leads to unstable kinetic model predictions. Furthermore, these limitations scale prohibitively with increasing model size. As larger metabolic models are developed with increasing genetic information and experimental validation, the demand to incorporate kinetic information increases. Therefore, in this work, we have begun to tackle the challenges of EM by introducing additional steps to the existing method framework specifically through reducing computation time and optimizing parameter sampling. We first reduce the structural complexity of the network by removing dependent species, and second, we sample locally stable parameter sets to reflect realistic biological states of cells. Lastly, we presort the screening data to eliminate the most incorrect predictions in the earliest screening stages, saving further calculations in later stages. Our complementary improvements to this EM framework are easily incorporated into concurrent EM efforts and broaden the application opportunities and accessibility of kinetic modeling across the field.
开发可靠的、具有预测性的代谢动力学模型是理解和有意改变细胞行为的一项困难但必要的优先任务。基于约束的建模使代谢工程和系统生物学领域在研究细胞代谢方面取得了巨大进展，但并未充分洞察代谢途径的调控或动力学限制。此外，基于约束的模型通常所依赖的生长优化假设在研究静止或持续存在的细胞群体时并不成立。然而，开发动力学模型面临许多独特的挑战，因为许多控制单个酶的动力学参数和速率定律是未知的。整体建模（EM）的开发旨在规避这一挑战，并使用一致的实验数据集有效地对庞大的动力学参数解空间进行采样。不幸的是，基础形式的EM需要很长的求解时间才能完成，并且常常导致动力学模型预测不稳定。此外，随着模型规模的增大，这些限制会急剧增加。随着利用越来越多的遗传信息和实验验证开发出更大的代谢模型，纳入动力学信息的需求也在增加。因此，在这项工作中，我们开始通过在现有方法框架中引入额外步骤来应对EM的挑战，具体而言是通过减少计算时间和优化参数采样。我们首先通过去除相关物种来降低网络的结构复杂性，其次，我们对局部稳定的参数集进行采样以反映细胞的实际生物学状态。最后，我们对筛选数据进行预排序，以在最早的筛选阶段消除最不正确的预测，从而在后期阶段节省进一步的计算。我们对这个EM框架的补充改进很容易整合到同步的EM工作中，并拓宽了整个领域动力学建模的应用机会和可及性。
