Insights into yeast response to chemotherapeutic agent through time series genome‐scale metabolic models

Organism‐specific genome‐scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time‐series transcriptomics...

Full description

Saved in:
Bibliographic Details
Published in:Biotechnology and bioengineering 2024-10, Vol.121 (10), p.3351-3359
Main Author: Karabekmez, Muhammed E.
Format: Article
Language:English
Subjects:
Bicarbonates
Biology
Biotechnology
Cell culture
Cluster analysis
Doxorubicin
Ergosterol
Fluctuations
Functional analysis
genome scale metabolic models
Genomes
Machine learning
Metabolic engineering
Metabolism
Molecular modelling
Reagents
Solution space
Spermidine
Time series
time series transcriptomics
Transcriptomics
Yeast
Yeasts
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Organism‐specific genome‐scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time‐series transcriptomics in GSMM simulations. Yeast is an easy‐to‐manipulate model organism for tumor research. Here, a novel approach (TS‐GSMM) was proposed to integrate time‐series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time‐series flux samples. The flux samples were clustered using machine learning techniques, and the clusters' functional analysis was performed using reaction set enrichment analysis. A time series transcriptomics response of Yeast cells to a chemotherapeutic reagent—doxorubicin—was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured. Integrating time‐series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics‐only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.
ISSN:0006-3592
1097-0290
1097-0290
DOI:10.1002/bit.28833