AI Article Synopsis
- Many production processes in the chemical industry can be more efficiently performed by living cells, but they face metabolic limitations.
- To enhance these cells' capabilities, metabolic modeling can identify optimal genetic changes, with Elementary Flux Mode Analysis (EFMA) being a useful tool that doesn’t need a defined cellular goal.
- The authors propose two EFMA-based methods: one for small to medium metabolic networks, and an advanced dual system approach for genome-scale models.
Article Abstract
Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis (EFMA) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two EFMA-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.
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| http://dx.doi.org/10.1007/978-1-4939-7528-0_17 | DOI Listing |
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