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Amanda Mackie

Macquarie University

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Ian T. Paulsen

Australian Genome Research Facility

Peter D. Karp

SRI International

Suzanne Paley

SRI International

Ingrid M. Keseler

Stanford University

Julio Collado‐Vides

Universidad Nacional Autónoma de México

Luis Muñiz-Rascado

Universidad Nacional Autónoma de México

Alberto Santos-Zavaleta

Universidad Nacional Autónoma de México

César Bonavides-Martínez

Universidad Nacional Autónoma de México

Socorro Gama‐Castro

Universidad Nacional Autónoma de México

Markus Krummenacker

SRI International

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SRI International

Stanford University

Universidad Nacional Autónoma de México

Menlo School

Cornell University

Carnegie Department of Plant Biology

Scripps Institution of Oceanography

Human Computer Interaction (Switzerland)

Carnegie Mellon University

University of Mumbai

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Revisiting the y-ome of Escherichia coli

Nucleic Acids Research (2024)

Lisa R. Moore Ron Caspi Dana Boyd Mehmet Berkmen Amanda Mackie

The model organism Escherichia coli K-12 has one of the most extensively annotated genomes in terms of functional characterization, yet a significant number of genes, ∼35%, are still considered poorly characterized. Initially genes without known functional understanding were given 'y' gene names. However, due to inconsistency in changing 'y' names to non-'y' names over the years, gene name alone does not provide sufficient information as to the characterization level of genes. Attempts to characterize y-ome genes, i.e. those that lack experimental evidence for function, are ongoing, and recent categorization based on the level of experimental evidence has helped clarify those genes that are well characterized versus uncharacterized. EcoCyc, the most comprehensive, curated genome database for E. coli K-12 substr. MG1655, has updated this approach by expanding the categories to include Partially characterized genes using a set of computational rules that includes keywords, experimental evidence codes and Gene Ontology terms. Approximately half of the previously categorized y-ome genes are now categorized as Partially characterized, leaving 15.5% (738) as Uncharacterized genes in EcoCyc. This new categorization scheme is searchable in the EcoCyc database, will be updated as new experimental evidence is curated and provides important information for research decisions.

Gene nomenclature

10.1093/nar/gkae857

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Citations (2)

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

BMC Systems Biology (2014)

Daniel Weaver Ingrid M. Keseler Amanda Mackie Ian T. Paulsen Peter D. Karp

Constraint-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. We 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. Significant 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.

Flux Balance Analysis

Chemostat

Metabolic flux analysis

Model Organism

Synthetic Biology

10.1186/1752-0509-8-79

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Citations (52)

Dead End Metabolites - Defining the Known Unknowns of the E. coli Metabolic Network

PLoS ONE (2013)

Amanda Mackie Ingrid M. Keseler Laura M. Nolan Peter D. Karp Ian T. Paulsen

The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database – these are metabolites which lack the requisite reactions (either metabolic or transport) that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12 – these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.

Metabolic network

Metabolic pathway

Dead end

10.1371/journal.pone.0075210

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Citations (28)

The EcoCyc database: reflecting new knowledge aboutEscherichia coliK-12

Nucleic Acids Research (2016)

Ingrid M. Keseler Amanda Mackie Alberto Santos-Zavaleta Richard Billington César Bonavides-Martínez

EcoCyc (EcoCyc.org) is a freely accessible, comprehensive database that collects and summarizes experimental data for Escherichia coli K-12, the best-studied bacterial model organism. New experimental discoveries about gene products, their function and regulation, new metabolic pathways, enzymes and cofactors are regularly added to EcoCyc. New SmartTable tools allow users to browse collections of related EcoCyc content. SmartTables can also serve as repositories for user- or curator-generated lists. EcoCyc now supports running and modifying E. coli metabolic models directly on the EcoCyc website.

Model Organism

10.1093/nar/gkw1003

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Citations (540)

Verticillium Wilt of Cotton: Genetic Markers for Disease Resistance

Zhu You-yong Mona Akbari Raja Kota Amanda Mackie B. R. Lyon

Verticillium wilt

Wilt disease

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The EcoCyc Database

EcoSal Plus (2018)

Peter D. Karp Wai Kit Ong Suzanne Paley Richard Billington Ron Caspi

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of

10.1128/ecosalplus.esp-0006-2018

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Citations (104)

The EcoCyc Database (2023)

EcoSal Plus (2023)

Peter D. Karp Suzanne Paley Ron Caspi Anamika Kothari Markus Krummenacker

EcoCyc is a bioinformatics database available online at http://www.EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli .

10.1128/ecosalplus.esp-0002-2023

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Citations (40)

The EcoCyc Database (2023).

Peter D Karp Suzanne Paley Ron Caspi Anamika Kothari Markus Krummenacker

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of

10.1128/ecosalplus.esp-0002-2023

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Citations (18)

The EcoCyc Database

EcoSal Plus (2014)

Peter D. Karp Daniel Weaver Suzanne Paley Carol A. Fulcher Aya Kubo

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review provides a detailed description of the data content of EcoCyc and of the procedures by which this content is generated.

Metabolic flux analysis

10.1128/ecosalplus.esp-0009-2013

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Citations (152)

Biolog Phenotype MicroArrays for Phenotypic Characterization of Microbial Cells

Methods in molecular biology (2014)

Amanda Mackie Karl A. Hassan Ian T. Paulsen Sasha G. Tetu

10.1007/978-1-62703-712-9_10

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Citations (53)