Community standards to facilitate development and address challenges in metabolic modeling

Abstract

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remain challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of constraint‐based metabolic modeling by conducting a community‐wide survey. We used this feedback to (i) outline the major challenges that our field faces and to propose solutions and (ii) identify a set of features that defines what a “gold standard” metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community‐driven outline will help the long‐term development of community‐inspired resources as well as produce high‐quality, accessible models within our field. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure the continued development of both practical, usable standards and reproducible, knowledge‐rich models.

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

“…Model checks were done with MEMOTE, and refinements were performed with MetaNetX 4.2, BiGG, ChEBI, MetaCyc, and PubChem databases ( Caspi et al, 2014 ; Hastings et al, 2016 ; Norsigian et al, 2020 ; Kim et al, 2021 ; Moretti et al, 2021 ). The new GEM contains 4,660 genes, 3,614 reactions, and 4,052 metabolites and conforms to the minimum standardised content for a newly published GEM based on recently published community standards ( Carey et al, 2020 ); 100% of the metabolites in ( i HsaEC21) have a human-readable descriptive name, 100% have an inchi key, 100% of metabolite annotation conformity with the BiGG database and in MetaNetX, Kyoto Encyclopedia of Genes and Genomes (KEGG), ChEBI, ModelSEED, HMDb, or MetaCyc ( Caspi et al, 2014 ; Hastings et al, 2016 ; Wishart et al, 2018 ; Norsigian et al, 2020 ; Kanehisa et al, 2021 ; Kim et al, 2021 ; Moretti et al, 2021 ; Seaver et al, 2021 ); 100% of the metabolites have a charge and chemical formula with a charge balance of 75.3% ( ). In addition, 97% of the reactions have a human-readable descriptive name, 100% of reactions conform with the BiGG database and as well as in MetaNetX, KEGG, ChEBI, ModelSEED, HMDb, or MetaCyc.…”

“…We have built on these approaches by developing an integrated epithelial cell/SARS-CoV-2 metabolic model and used a combination of structural and dynamical analyses to assess the model and make predictions. We have applied the recently released community standards to facilitate our development of a standardised model for the systems biology international community and used the MEMOTE quality control software to assess and compare our model with previously developed GEMS ( Carey et al, 2020 , Lieven et al, 2020 ). We have designed a novel computational method and developed a software tool ( findCPcli ) to carry out such analyses and to predict drug targets.…”

dfba: Software for efficient simulation of dynamic flux-balance analysis models in Python

“…Increasingly, researchers engaged in metabolic modeling prefer open source software. Python is quickly becoming their platform of choice (Carey, Dräger, Beber, Papin, & Yurkovich, 2020). Among other packages, open source resources for building and simulating FBA models using Python can be found in the COBRApy (Ebrahim, Lerman, Palsson, & Hyduke, 2013) package which is part of the openCOBRA organization (openCOBRA, n.d.).…”

Mechanistic models of microbial community metabolism

“…Therefore, quality assessments are not the end point for the model development process, but rather a feedback mechanism to inform further curation. 27 …”