In recent years, constraint-based metabolic models have emerged as an important tool for metabolic engineering; a number of computational algorithms have been developed for identifying metabolic engineering strategies where the production of the desired chemical is coupled with the growth of the organism.
To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression.
In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets.
Engineering investigation to obtain data necessary for identifying criteria and constraints and to test design ideas.
The framework has been developed by combining different process systems engineering methodologies – the knowledge-based approach for identifying the root cause of waste generation, the hierarchical design method for generating alternative designs, sustainability metrics, and multi-objective optimization – into one coherent simulation-optimization framework.
However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.
Unfortunately, there is no theoretical approach for identifying these values and engineering judgment is often used.
A key advantage of representing biological networks with mathematical models lies in their amenability for identifying network properties using engineering analysis approaches.
Convenient methods for identifying gates in protein structures are essential prerequisites for their engineering.
We also intend to implement additional features, including capabilities for identifying target sites for ZFNs made by other publicly available engineering methods such as modular assembly.
