Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

Metabolic engineering encompasses several widely-used strategies, which currently hold a
high seat in the field of biotechnology when its potential is manifesting through a plethora of …

In this proof-of-concept work, we evaluate the performance of multiple machine-learning
methods as surrogate models for use in the analysis of agent-based models (ABMs) …

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the
metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a …

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …

Yeasts have been widely used for production of bread, beer and wine, as well as for
production of bioethanol, but they have also been designed as cell factories to produce …

In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore,
our capacity to control it has an enormous potential to help us move towards a bio-based …

Data are the most important elements of bioinformatics: Computational analysis of
bioinformatics data, in fact, can help researchers infer new knowledge about biology …

Highlights•DBTL for cell factory development faces involution without breakthrough.•
Machine learning can assist DBTL from genetic optimizations to fermentation controls.•The …

Machine learning has become a powerful tool for systems biologists, from diagnosing
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …