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 …

The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …

Omic data analysis is steadily growing as a driver of basic and applied molecular biology
research. Core to the interpretation of complex and heterogeneous biological phenotypes …

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 …

Introduction Metabolomics is a well-established tool in systems biology, especially in the top–
down approach. Metabolomics experiments often results in discovery studies that provide …

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful
systems biology approach, with applications ranging from basic understanding of genotype …

Machine learning uses experimental data to optimize clustering or classification of samples
or features, or to develop, augment or verify models that can be used to predict behavior or …

Kinetic models are critical to predict the dynamic behaviour of metabolic networks.
Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting …

Metabolic engineering aims to maximize the production of bio-economically important
substances (compounds, enzymes, or other proteins) through the optimization of the …

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme
levels through mechanistic relations, rendering them essential for understanding, predicting …