A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

Abstract

BackgroundConstraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.ResultsWe present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization.ConclusionSignificant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database.

The enduring utility of continuous culturing in experimental evolution

“…However, we would argue that this simplicity is exactly the point: by modeling adaptive evolution using the simplest possible selective regimen, we greatly increase our chances of ever being able to understand and explain it completely. Notably, continuous culture is an excellent context for modeling, particularly since the steady state assumptions of most metabolic models are actually met [71]. …”

A model‐based framework for parallel scale‐down fed‐batch cultivations in mini‐bioreactors for accelerated phenotyping

“…The use of a single set of model parameters for pulse-size calculation for all cultivations helps to establish a baseline, which allows the comparison of parallel cultivations.The model calculations and robotic executions also helped to establish and trace cause-and-effect relationships between process conditions and physiological responses, which are important for the interpretation of the results. Although a fully integrated genome-scale model of the strain(Weaver, Keseler, Mackie, Paulsen, & Karp, 2014) coupled with a hydrodynamic model of the larger scale(Haringa et al, 2018) may give more accurate results, the simplified mechanistic model used here was adequate for the purpose of calculating the concentration gradients, without the constraints of higher computational burden and issues of parameter identifiability in larger models, as discussed by. The further development of the system to simultaneously carry out 21 parallel cultivations ensured the generation of a large amount of data within the shortest possible time and eliminated batch-to-batch variability in inoculum and media components.…”

Metabolic network modeling with model organisms

“…A review of most E. coli GSMNMs have recently been provided [13] and will not be repeated here. An example from the previous review [14] and the most recent reconstruction [15] are included in Table 1.…”