Combining machine learning and metabolic modelling for yeast bioprocessing

Project: Other

Description

Recent research has shown that metabolic modelling utilising gene expression profiling combined with genetic information is more predictive for target metabolites than the latter alone [Zampieri et al., 2017]. This new field of research has gained notable attention [Phys.org, 2017] and is an extremely promising area, where machine learning techniques can aid in discerning genetic and media composition to maximise bioprocess yield.

Our objective is to propose and combine a novel machine learning 'kernel trick', based on a newly-defined metabolic distance with a poly-omic analysis on yeast cells. The resulting poly-omic machine-learning method will identify whether target cells have optimal conditions for a targeted protein growth, and predict genetic and media modifications that will increase the yield. We will train the method using gene expression data and the most up-to-date model of Saccharomyces cerevisiae metabolism.
StatusFinished
Effective start/end date22/05/1830/11/18

Funding

  • BBSRC