Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.

ABSTRACT: Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through s

SUBMITTER: Wu D 

PROVIDER: S-EPMC10793177 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature