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Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective.
Wheaton L Schroeder1,2, Patrick F Suthers1,2,3, Thomas C Willis1,2
1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Metabolites
|July 26, 2024
Summary
Genome-scale metabolic models (GEMs) predict cellular behavior but often overlook protein costs and limitations. Resource allocation models (RAMs) integrate these factors for more accurate predictions in metabolic engineering.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Stoichiometric genome-scale metabolic models (GEMs) are widely used but have limitations.
- GEMs do not account for protein cost, enzyme kinetics, or proteome limitations, potentially leading to inaccurate predictions.
- Previous attempts to address these limitations include flux balance analysis with molecular crowding.
Purpose of the Study:
- To provide a comprehensive review of resource allocation models (RAMs).
- To discuss the evolution from stoichiometric models to RAM frameworks.
- To compare different RAM frameworks based on their utility, data needs, strengths, and limitations.
Main Methods:
- Review of existing literature on metabolic modeling frameworks.
- Categorization of RAM frameworks into coarse-grained and fine-grained approaches.
- Analysis of mathematical frameworks to contrast different RAMs.
Main Results:
- RAMs incorporate proteome-related limitations into GEMs.
- RAM frameworks are divided into coarse-grained and fine-grained categories.
- The review details the utility, data requirements, and limitations of various RAMs.
Conclusions:
- RAMs offer a more mechanistic approach to metabolic modeling compared to traditional GEMs.
- Understanding the strengths and weaknesses of different RAMs is crucial for selecting appropriate models for research.
- Future applications of RAMs hold promise for advancing metabolic engineering and synthetic biology.
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