Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
| Autor: | Michael Krogh Jensen, Jens Nielsen, Jie Zhang, Eduardo Abeliuk, Zak Costello, Andrés Ramirez, Andrés Pérez-Manríquez, Benjamin Sanchez, Søren D. Petersen, Jay D. Keasling, Hector Garcia Martin, Michael J. Fero, Tijana Radivojevic, Yu Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
0301 basic medicine Genotype Biochemical Phenomena Computer science Science education General Physics and Astronomy Bioengineering Biosensing Techniques Saccharomyces cerevisiae Machine learning computer.software_genre Models Biological 01 natural sciences Article General Biochemistry Genetics and Molecular Biology Applied microbiology Machine Learning Metabolic engineering 03 medical and health sciences Models 010608 biotechnology Amino Acids lcsh:Science Synthetic biology Multidisciplinary business.industry Tryptophan General Chemistry Tryptophan Metabolism Biological Living systems Phenotype ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Metabolic Engineering lcsh:Q Forward engineering Artificial intelligence business computer Algorithms Metabolic Networks and Pathways |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020) Nature Communications Nature communications, vol 11, iss 1 Zhang, J, Petersen, S D, Radivojevic, T, Ramirez, A, Pérez-Manríquez, A, Abeliuk, E, Sánchez, B J, Costello, Z, Chen, Y, Fero, M J, Martin, H G, Nielsen, J, Keasling, J D & Jensen, M K 2020, ' Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism ', Nature Communications, vol. 11, no. 1, 4880 . https://doi.org/10.1038/s41467-020-17910-1 |
| ISSN: | 2041-1723 |
| Popis: | Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts. In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker’s yeast. |
| Databáze: | OpenAIRE |
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