School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA.
Biotechnol Bioeng. 2010 Jun 1;106(2):271-84. doi: 10.1002/bit.22692.
Motivated by the need for a quick quantitative assessment of metabolic function without extensive data, we present an adaptation of the cybernetic framework, denoted as the lumped hybrid cybernetic model (L-HCM), which combines the attributes of the classical lumped cybernetic model (LCM) and the recently developed HCM. The basic tenet of L-HCM and HCM is the same, that is, they both view the uptake flux as being split among diverse pathways in an optimal way as a result of cellular regulation such that some chosen metabolic objective is realized. The L-HCM, however, portrays this flux distribution to occur in a hierarchical way, that is, first among lumped pathways, and next among individual elementary modes (EM) in each lumped pathway. Both splits are described by the cybernetic control laws using operational and structural return-on-investments, respectively. That is, the distribution of uptake flux at the first split is dynamically regulated according to environmental conditions, while the subsequent split is based purely on the stoichiometry of EMs. The resulting model is conveniently represented in terms of lumped pathways which are fully identified with respect to yield coefficients of all products unlike classical LCMs based on instinctive lumping. These characteristics enable the model to account for the complete set of EMs for arbitrarily large metabolic networks despite containing only a small number of parameters which can be identified using minimal data. However, the inherent conflict of questing for quantification of larger networks with smaller number of parameters cannot be resolved without a mechanism for parameter tuning of an empirical nature. In this work, this is accomplished by manipulating the relative importance of EMs by tuning the cybernetic control of mode-averaged enzyme activity with an empirical parameter. In a case study involving aerobic batch growth of Saccharomyces cerevisiae, L-HCM is compared with LCM. The former provides a much more satisfactory prediction than the latter when parameters are identified from a few primary metabolites. On the other hand, the classical model is more accurate than L-HCM when sufficient datasets are involved in parameter identification. In applying the two models to a chemostat scenario, L-HCM shows a reasonable prediction on metabolic shift from respiration to fermentation due to the Crabtree effect, which LCM predicts unsatisfactorily. While L-HCM appears amenable to expeditious estimates of metabolic function with minimal data, the more detailed dynamic models [such as HCM or those of Young et al. (Young et al., Biotechnol Bioeng, 2008; 100: 542-559)] are best suited for accurate treatment of metabolism when the potential of modern omic technology is fully realized. However, in view of the monumental effort surrounding the development of detailed models from extensive omic measurements, the preliminary insight into the behavior of a genotype and metabolic engineering directives that can come from L-HCM is indeed valuable.
受限于对代谢功能进行快速定量评估的需要，我们提出了一种对控制论框架的改进，称为集总混合控制论模型（L-HCM），它结合了经典集总控制论模型（LCM）和最近开发的 HCM 的属性。L-HCM 和 HCM 的基本原理是相同的，即它们都认为摄取通量由于细胞调节而以最优的方式分配到不同的途径中，从而实现某些选定的代谢目标。然而，L-HCM 以分层的方式描述这种通量分布，即首先在集总途径之间，然后在每个集总途径中的各个基本模式 (EM) 之间。这两种分裂都分别使用操作和结构投资回报率的控制论控制律来描述。也就是说，第一个分裂处的摄取通量分配是根据环境条件动态调节的，而后续的分裂则完全基于 EM 的计量关系。该模型的表示形式非常方便，因为它涉及到集总途径，这些集总途径与所有产物的产率系数完全相关，这与基于本能集总的经典 LCM 不同。这些特性使得该模型能够针对任意大的代谢网络的完整 EM 集进行建模，尽管它只包含可以使用最少数据识别的少量参数。然而，如果没有一种经验性质的参数调整机制，那么在寻求用较少的参数对更大的网络进行量化时，就无法解决内在的冲突。在这项工作中，通过使用经验参数调整模式平均酶活性的控制论控制，实现了对 EM 相对重要性的调整，从而完成了这一目标。在涉及酿酒酵母有氧批式生长的案例研究中，将 L-HCM 与 LCM 进行了比较。当从几种初级代谢物中识别参数时，前者比后者提供了更令人满意的预测。另一方面，当涉及足够多的数据集进行参数识别时，经典模型比 L-HCM 更准确。在将两种模型应用于恒化器场景时，由于 Crabtree 效应，L-HCM 显示出对代谢从呼吸到发酵转变的合理预测，而 LCM 的预测则不理想。虽然 L-HCM 似乎适合于使用最少的数据进行代谢功能的快速估计，但当充分利用现代组学技术的潜力时，更详细的动态模型[如 HCM 或 Young 等人的模型（Young 等人，Biotechnol Bioeng，2008 年；100:542-559）]最适合于准确处理代谢问题。然而，鉴于从广泛的组学测量中开发详细模型所需要的巨大努力，从 L-HCM 中获得的对基因型的初步了解和代谢工程指令确实是有价值的。
