Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli .
Yuki KuriyaMasahiro MurataMasaki YamamotoNaoki WatanabeMichihiro ArakiPublished in: Bioengineering (Basel, Switzerland) (2023)
Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O 2 , CO 2 , and NH 4 + , were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of 13 C-MFA using CO 2 emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested.
Keyphrases
- escherichia coli
- systematic review
- stem cells
- oxidative stress
- type diabetes
- blood pressure
- metabolic syndrome
- electronic health record
- gene expression
- functional connectivity
- cell therapy
- quantum dots
- weight loss
- artificial intelligence
- cystic fibrosis
- skeletal muscle
- multidrug resistant
- resting state
- anti inflammatory
- glycemic control