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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
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Modelling compartmentalization towards elucidation and engineering of spatial organization in biochemical pathways
Govind Menon1, Chinedu Okeke1, J Krishnan2,3
1Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, UK.
Scientific Reports
|September 23, 2017
Summary
Compartmentalization is key in biology and synthetic biology. This study clarifies when simplified compartmental models accurately represent complex biochemical pathways, guiding better system design.
Area of Science:
- Biochemistry
- Systems Biology
- Synthetic Biology
Background:
- Compartmentalization is crucial for biological systems, from cellular processes to evolution.
- Understanding and modeling these compartments is vital for systems and synthetic biology.
- Current modeling approaches require rigorous validation for complex pathways.
Purpose of the Study:
- To systematically analyze the applicability of compartmental Ordinary Differential Equation (ODE) models.
- To compare compartmental ODE models with detailed reaction-transport models.
- To establish conditions for reliable and predictive modeling of compartmentalized biochemical pathways.
Main Methods:
- Comparative analysis of compartmental ODE models and reaction-transport models.
- Systematic examination of building blocks in signaling and metabolic pathways.
- Investigation of complexities and engineering challenges in compartmentalized systems.
Main Results:
- Identified conditions under which compartmental models are valid and invalid.
- Explained the reasons behind the limitations of compartmental models.
- Proposed augmentations to enhance the reliability and predictability of these models.
Conclusions:
- Provides critical insights for modeling, elucidating, and engineering compartmentalized biochemical pathways.
- Highlights the importance of understanding model limitations in systems and synthetic biology.
- Offers guidance on selecting and refining modeling frameworks for biological systems.
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