The era of big data: Genome-scale modelling meets machine learning

Computational and Structural Biotechnology Journal

卷:18

Cleo Kontoravdi¹  Pavlos Kotidis²  Athanasios Antonakoudis²  Rodrigo Barbosa² 

[1] Corresponding author.;

[2] Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom;

关键词: Flux balance analysis;    Cell metabolism;    Strain optimisation;    Chinese hamster ovary cells;    Hybrid modelling;    Principal component analysis;   

DOI  :  

来源: DOAJ

【 摘 要 】

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

【 授权许可】

Unknown