This App uses an input metabolic model to simulate growth in a set of media conditions, with a specified set of gene knockouts, and with specified media supplements. A metabolic model can be curated using phenotypic data such as Biolog growth data or gene essentiality data generated on a specific set of media conditions. The App reports differences between growth predictions and experimentally measured growth rates and can be applied to test the accuracy of a model in replicating experimental observations, as well as exploring the set of metabolites that an organism can utilize as nutrient sources. The Simulate Growth on Phenotype Data App carries out flux balance analysis (FBA) for each medium and knockout in the phenotype dataset and displays the output (growth/no growth) as a side-by-side comparison of model predictions and experimental results. To begin, the user uploads a table of phenotype data (e.g., Biolog or gene essentiality data) into the App. The user also either uploads a metabolic model or selects a model already present in KBase. KBase uses the selected model to simulate the uploaded phenotypes, presenting simulation results in a detailed exportable report. It also conducts some reconciliation of models with phenotype data.

For information about how to upload your own phenotype data, see the Data Upload and Download Guide.

The columns in the phenotype files are as follows:

• Media: lists the ID of the media condition loaded in KBase where the phenotype was observed.
• Mediaws: lists the workspace where the media for the phenotype data was loaded into KBase.
• Growth: indicates whether or not the organism grew in the specified media with the specified knockouts. 1 means growth; 0 means no growth.
• Geneko: list the genes knocked out in the phenotype; use none for wild-type phenotypes.
• AddtlCpd: lists additional media components added along with the primary media formulation.

For more information about growth simulation see the Tutorial for Simulate Growth on Phenotype Data Method.

Team members who developed & deployed algorithm in KBase: Chris Henry, Janaka Edirisinghe, Sam Seaver, and Neal Conrad. For questions, contact us.