Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Area of Science:

• Microbial Physiology
• Systems Biology
• Metabolic Engineering

Background:

• Genome-Scale Models (GEMs) aim to predict microbial phenotypes from genotypes under specific conditions.
• This review examines the prediction of dynamic microbial phenotypes, including growth, metabolism, and stress responses.
• Current GEMs often rely on outdated biokinetic frameworks, limiting their predictive accuracy.

Purpose of the Study:

• To critically evaluate the limitations of current GEMs in predicting dynamic microbial phenotypes.
• To identify deficiencies in GEMs regarding environmental conditions, cellular composition, growth rate, and stress response.
• To propose improvements for enhancing the performance and applicability of GEMs.

Main Methods:

• Review of constraint-based metabolic reconstructions and their historical development.
• Analysis of deficiencies in existing GEMs, including inadequate environmental accounting and failure to simulate adaptive changes.
• Identification of outdated biokinetic frameworks and their impact on model performance.

Main Results:

• GEMs exhibit deficiencies in accounting for environmental fluctuations and simulating adaptive changes in MacroMolecular Cell Composition (MMCC).
• Misinterpretation of Specific Growth Rate (SGR) and neglect of stress resistance limit GEM accuracy.
• Inefficient experimental verification against simple growth data further compromises model performance.

Conclusions:

• Outdated biokinetic frameworks in GEMs hinder accurate prediction of dynamic microbial phenotypes.
• Improvements are needed in environmental condition accounting, MMCC adaptation simulation, and stress resistance integration.
• Replacing the Monod equation with the Synthetic Chemostat Model (SCM) can enhance GEMs' predictive power.