MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae
Artikel i vetenskaplig tidskrift, 2018

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using beta-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric platebased, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories.

Combinatorial optimization

YeastFab

Machine learning

Microbial cell factory

Författare

Yikang Zhou

Tsinghua University

Gang Li

Chalmers, Biologi och bioteknik, Systembiologi

Junkai Dong

Tsinghua University

Xin-hui Xing

Tsinghua University

Junbiao Dai

Chinese Academy of Sciences

Tsinghua University

Chong Zhang

Tsinghua University

Metabolic Engineering

1096-7176 (ISSN) 1096-7184 (eISSN)

Vol. 47 294-302

Ämneskategorier

Mikrobiologi

Bioinformatik (beräkningsbiologi)

Datavetenskap (datalogi)

DOI

10.1016/j.ymben.2018.03.020

PubMed

29627507

Mer information

Senast uppdaterat

2018-06-19