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PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and
Anush Chiappino-Pepe1, Vassily Hatzimanikatis1
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.
STAR Protocols
|February 3, 2021
Summary
PhenoMapping integrates genome-scale metabolic models (GEMs) with omics and phenotypic data to identify cellular processes driving phenotypes. This approach aids in gene essentiality classification and metabolic model curation for bioengineering and medicine.
Area of Science:
- Systems biology
- Metabolic engineering
- Computational biology
Background:
- Genome-scale metabolic models (GEMs) are crucial for integrating omics data and understanding cellular physiology.
- Identifying cellular processes linked to specific phenotypes is vital for bioengineering and medical applications.
Purpose of the Study:
- To present PhenoMapping, a novel protocol for mapping cellular processes to observed phenotypes.
- To classify gene essentiality (conditional and unconditional) and guide the curation of GEMs.
Main Methods:
- Utilizes genome-scale metabolic models (GEMs).
- Integrates multi-omics data with phenotypic data.
- Employs computational analysis for process mapping and gene essentiality classification.
Main Results:
- PhenoMapping successfully maps cellular processes to phenotypes.
- The protocol facilitates the classification of genes as conditionally or unconditionally essential.
- Guides comprehensive curation of GEMs for improved accuracy and utility.
Conclusions:
- PhenoMapping provides a robust framework for linking genotype to phenotype through metabolic modeling.
- The protocol enhances the utility of GEMs in biological research and development.
- Facilitates targeted interventions in bioengineering and medicine by elucidating key cellular mechanisms.
