Escherichia coli is considered to be the best-known microorganism given the large number
of published studies detailing its genes, its genome and the biochemical functions of its …

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction
associations for entire metabolic genes in an organism, and can be simulated to predict …

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 …

Genome‐scale metabolic models (GEM s) are widely used to calculate metabolic
phenotypes. They rely on defining a set of constraints, the most common of which is that the …

Engineering cell metabolism for bioproduction not only consumes building blocks and
energy molecules (eg, ATP) but also triggers energetic inefficiency inside the cell. The …

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have
been under development since the first whole-genome sequences appeared in the mid …

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 …

Abstract Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are
widely used for genome-scale modeling of metabolic networks in both prokaryotes and …

The turnover number k cat, a measure of enzyme efficiency, is central to understanding
cellular physiology and resource allocation. As experimental k cat estimates are unavailable …

The prediction of cellular function from a genotype is a fundamental goal in biology. For
metabolism, constraint-based modelling methods systematize biochemical, genetic and …