Different applications of AI are applicable when there is a huge scope for solving a scientific or practical question. The growing number and size of different databases give more and more data to be evaluated for application in different biotechnological solutions. Omics data add another dimension to the analysis of solution space.

 

That leads to the application of different sub-fields of artificial intelligence as machine learning, deep learning and neural networks to help to acquire valuable information from big data. Our main interest is in the application of artificial intelligence in the selection of the most appropriate metabolic engineering design for particular substrate-product pairs including the selection of the most appropriate organism and necessary modifications to reach a good compromise in different sustainability aspects.

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