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利用计算机模拟策略将生物乙醇生产与蓝藻生长相结合。

In silico strategies to couple production of bioethanol with growth in cyanobacteria.

机构信息

Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca, Argentina.

出版信息

Biotechnol Bioeng. 2019 Aug;116(8):2061-2073. doi: 10.1002/bit.26998. Epub 2019 May 17.

Abstract

Cyanobacteria have been considered as promising candidates for sustainable bioproduction from inexpensive raw materials, as they grow on light, carbon dioxide, and minimal inorganic nutrients. In this study, we present a genome-scale metabolic network model for Synechocystis sp. PCC 6803 and study the optimal design of the strain for ethanol production by using a mixed integer linear problem reformulation of a bilevel programming problem that identifies gene knockouts which lead to coupling between growth and product synthesis. Five mutants were found, where the in silico model predicts coupling between biomass growth and ethanol production in photoautotrophic conditions. The best mutant gives an in silico ethanol production of 1.054 mmol·gDW ·h .

摘要

蓝藻被认为是从廉价原料中可持续生物生产的有前途的候选者,因为它们在光照、二氧化碳和最少的无机营养物质上生长。在这项研究中,我们提出了一种 Synechocystis sp. PCC 6803 的基因组规模代谢网络模型,并通过将双层规划问题转化为混合整数线性问题,来研究该菌株用于乙醇生产的最佳设计,从而确定导致生长和产物合成偶联的基因敲除。发现了五个突变体,其中的计算机模型预测在光自养条件下,生物量生长和乙醇生产之间存在偶联。最好的突变体在计算机上预测的乙醇产量为 1.054mmol·gDW·h 。

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