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• W2558715734 abstract "How can we boost Escherichia coli (E. coli) growth (i.e., production) without modifying genes themselves? This remains a challenging and fruitful goal that would facilitate the mass production of biofuels, biomedicine, and engineered genomes in synthetic biology. In this paper, we focus on rear-ranging gene order within an operon to optimize gene expression. Optimizing more than five genes remains laborious without predictive modeling as the number of gene orders increases factorially — a five-gene operon possesses 120 gene orders, but a ten-gene operon possesses 3,628,800 gene orders. To handle a ten-gene operon, we propose consultation algorithms utilizing LCS to analyze the relationship between gene order and growth rate, and then verify predicted gene orders with high growth rates using wet-lab experiments. “Consultation” refers to optimizing gene orders in different machine learning algorithms and choosing gene orders with high growth rates in each algorithm to avoid over-fitting. We address the following research questions: (RQ1) How can we predict E. coli growth according to gene orders? (RQ2) Can definite rules easily understood by biologists be extracted? (RQ3) Can new E. coli strains surpass the highest growth rate of the dataset? Our first computational approach shows that consultation algorithms utilizing LCS can identify gene orders that significantly control E. coli growth and create novel E. coli strains with high growth rates using these operon construction rules." @default.
• W2558715734 created "2016-12-08" @default.
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• W2558715734 date "2016-07-01" @default.
• W2558715734 modified "2023-10-03" @default.
• W2558715734 title "Optimization of artificial operon construction by consultation algorithms utilizing LCS" @default.
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• W2558715734 doi "https://doi.org/10.1109/cec.2016.7744333" @default.
• W2558715734 hasPublicationYear "2016" @default.
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