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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Single-cell omics analysis with genome-scale metabolic modeling.

Yu Chen¹, Johan Gustafsson², Jingyu Yang¹

¹Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Current Opinion in Biotechnology

|February 15, 2024

Summary

This summary is machine-generated.

Single-cell technologies generate vast biological data. This review explores using genome-scale metabolic models (GEMs) to interpret this data, building upon bulk sample analysis methods.

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Area of Science:

Systems biology
Metabolic engineering
Genomics

Background:

Single-cell technologies produce extensive biological datasets requiring interpretation.
Genome-scale metabolic models (GEMs) integrate prior metabolic knowledge and gene-protein-reaction data.
GEMs are effective for analyzing omics data, particularly from bulk samples.

Purpose of the Study:

To review methods for leveraging bulk omics data with GEMs.
To discuss advancements in integrating single-cell omics data with GEMs.
To outline current challenges and future perspectives in the field.

Main Methods:

Review of existing literature on GEMs and omics data integration.
Discussion of strategies for applying GEMs to single-cell omics data.
Analysis of challenges and opportunities in single-cell metabolic modeling.

Main Results:

Two common approaches for using bulk omics data with GEMs are presented.
Advances in the integrative analysis of single-cell omics data with GEMs are discussed.
Key challenges and future research directions are identified.

Conclusions:

Integrating single-cell omics data with GEMs is a rapidly developing area.
Further development is needed to fully realize the potential of GEMs for single-cell analysis.
The field holds significant promise for advancing biological understanding and applications.