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한빛사논문

조회778

우현재Hyunjae Woo

건국대학교

Mol. Syst. Biol., Jan 30 2024, | https://doi.org/10.1038/s44320-024-00017-w Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources

Hyunjae Woo, Youngshin Kim, Dohyeon Kim & Sung Ho Yoon

Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Republic of Korea.

Corresponding author : Sung Ho Yoon

Abstract

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

논문정보

형식Research article
게재일2024년 01월 (BRIC 등록일 2024-02-02)
연구진국내 연구진
분야 생명과학 > 미생물학

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