Abstract
The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL: www.kimosys.org
Highlights
Sharing experimental data has major advantages for the scientific community
The option to search the data and model elements by standard cross-references ChEBI, KEGG or UniProt IDs is available in KiMoSys 2.0
KiMoSys 2.0 is a significant update to the original version
Summary
Sharing experimental data has major advantages for the scientific community. With the amount of biological data produced increasing each year, structured databases are a crucial tool to store, share and maintain data, improving quality and reproducibility (1). The estimation/identification of unknown parameters is a critical task of model building, and when fitting a kinetic model from data or evaluating model predictions (e.g. in Chassagnole et al (3), Khodayari et al (4) and Mannan et al (5)), a curated repository with metabolomics, fluxomics and proteomics data will be essential (6). Curated databases such as BRENDA (7) and SABIO-RK (8) provide relevant kinetic information (rate equations and experimentally derived kinetic parameters) available in the literature, which can support the model construction or extension, while repositories like BioModels (9) and JWS Online (10) stored Systems Biology Markup Language (SBML) (11) models and their properties. To complement data/model annotations and information, specific database identifiers (e.g. KEGG, ChEBI, UniProt and NCBI) associated with each biochemical entity are important
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