In a real scenario, metabolic pathway design is performed through an iterative process. Initial prototypes are designed based on the available pathway knowledge and associated models. However, those initial prototypes are generally not optimized because of the lack of precise understanding of every effect coming from our design choices. Therefore, an initial pilot screening is essential as a way of identifying the most promising designs. However, we should expect low titers and poor performance from this initial screening. A second optimization round is at least necessary. Here, we will discuss pathway redesign and optimization techniques that should help in order to bring up titers and the overall performance in the second iteration, allowing in that way the selection of streamlined designs. Two main approaches are possible: model-based and data-driven. The former will be used in order to redesign the chassis and the enzyme sequences, whereas the latter will be employed for experimental redesign. In order to address these challenges a promising approach is the use of machine learning, an efficient model-free approach that can assist us through the entire redesign process.
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- The site https://www.scipy-lectures.org/ provides a good introduction to many topics related to scientific computation using Python. The sections about statistics and machine learning are a useful reference material for the methods discussed in this chapter.
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