Estimation of biomass composition from genomic and transcriptomic information.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Genome-scale metabolic network reconstructions are crucial for understanding microorganisms.
• Biomass equations are essential components of metabolic models and often used as objective functions in Flux Balance Analysis (FBA).
• Accurate biomass composition data is vital for developing reliable metabolic models, but experimental determination can be resource-intensive.

Purpose of the Study:

• To develop a bioinformatics tool that estimates microbial biomass composition, specifically amino acids and nucleotides.
• To provide a user-friendly and rapid method for obtaining biomass composition data from genomic and transcriptomic information.
• To offer a computational alternative to experimental measurements for biomass equation parameterization.

Main Methods:

• A Java-based computational tool was developed.
• The tool utilizes genome sequences (FASTA format) and transcriptomic data (CSV format) as input.
• It estimates the composition of microbial biomass in terms of amino acids and nucleotides.

Main Results:

• The developed tool can rapidly estimate microbial biomass composition.
• Results obtained from the tool show good agreement with experimental data.
• The tool is designed to be user-friendly, catering to users with limited bioinformatics expertise.

Conclusions:

• The computational estimation of amino acid and nucleotide compositions from genomic and transcriptomic data is a viable alternative to experimental methods.
• This tool facilitates the acceleration of knowledge in the field of microbial metabolic modeling.
• The application provides a valuable resource for researchers needing to construct or refine genome-scale metabolic models.