Understanding Metabolic Flux Behaviour in Whole-Cell Model Output.

ABSTRACT: Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that the

SUBMITTER: Landon S 

PROVIDER: S-EPMC8718694 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature