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纤维素酶对无定形纤维素解聚的建模、参数研究与优化。

Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation.

作者信息

Niu Hongxing, Shah Nilay, Kontoravdi Cleo

机构信息

Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, England, UK.

出版信息

Biochem Eng J. 2016 Jan 15;105(Pt B):455-472. doi: 10.1016/j.bej.2015.10.017.

Abstract

Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endoglucanase, exoglucanase, and β-glucosidase) upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model's predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation.

摘要

深入了解纤维素酶对异质纤维素的水解作用是优化基于酶催化的纤维素生物精炼厂的基础。本文建立了一个详细的机理模型,用于描述纤维素酶(内切葡聚糖酶、外切葡聚糖酶和β-葡萄糖苷酶)在无定形纤维素上的动态吸附/解吸以及协同链端断裂过程。该模型可以预测水解过程中不溶性纤维素聚合物链长的变化以及可溶性糖的生成。同时,基于准蒙特卡罗方法和全局敏感性分析构建了一个不确定性分析建模框架,该框架可以系统地识别关键参数,有助于完善模型并提高其可识别性。该模型最初包含27个参数,发现在通常操作条件下(低酶负载)存在结构和实际识别问题,属于参数过度设定。因此,参数估计问题在数学上是不适定的。该框架一方面使我们能够识别出13个关键参数的子集,利用给定的实验数据集对其中更准确的置信区间进行估计,另一方面能够克服识别问题。利用一组独立的实验数据对模型的预测能力进行了检验。最后,通过基于模型的优化方法,获得了用于酶水解以及同时糖化发酵过程的纤维素酶混合物的最佳组成。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/751d/4705870/d32aecca2b38/fx1.jpg

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