Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources

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 non-essential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosy

SUBMITTER: Mr Hyunjae Woo 

PROVIDER: S-SCDT-10_1038-S44320-024-00017-W | biostudies-other |

REPOSITORIES: biostudies-other