Toward sustainable resource development, bioprocess technology using useful organisms such as yeast (Saccharomyces cerevisiae) is expected to be developed. Microorganisms in large-scale fermenters and high-concentration cultures on an industrial scale are sometimes exposed to hypoxic conditions. Hypoxic environments are not particularly uncommon for environmental microorganisms. Uncovering the biological responses under such hypoxic conditions is a critical issue of understanding the degree of microbial adaptation and scale-up of microbial production. To find time-series trends of gene expression patterns in yeast under hypoxic conditions, this study applied the dynamic topic model, which is a method of latent semantic analysis used in the field of natural language processing, to statistically analyze the “trends of gene expressions.” This approach visualized rank changes in time-series gene expression. Especially, several cell wall related genes were dramatically changed ranks under hypoxic conditions. This method extracts trends in the time-series expression profiles of yeast under hypoxic conditions, and the rank variation in the trends reveals temporal shifts in gene expression among hypoxia-responsive gene sets. This approach could provide new insights into the gene regulatory systems under hypoxic conditions in yeast.
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28 September 2023
11TH INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES
5–8 September 2022
Belgrade, Serbia
Research Article|
September 28 2023
Latent semantic analysis reveals time-series gene expression trends in yeast under hypoxic conditions
Koji Ishiya;
a)Corresponding author: [email protected]
AIP Conf. Proc. 2872, 020003 (2023)
Citation
Koji Ishiya, Takeaki Taniguchi; Latent semantic analysis reveals time-series gene expression trends in yeast under hypoxic conditions. AIP Conf. Proc. 28 September 2023; 2872 (1): 020003. https://doi.org/10.1063/5.0163319
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