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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
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It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology.
Niklas Korsbo1,2, Henrik Jönsson1,2,3
1The Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom.
Plos Computational Biology
|June 30, 2020
Summary
Simplifying biological pathway models requires careful assumptions. A new approach assumes homogeneous information propagation rates, outperforming truncated models for both linear and non-linear systems.
Area of Science:
- Systems biology
- Computational modeling
- Biochemical pathway analysis
Background:
- Systems biology models require simplification for tractability.
- Poor assumptions can lead to overly complex or inaccurate models.
- Truncating pathway steps is a common but potentially problematic simplification.
Purpose of the Study:
- Investigate the impact of simplifying assumptions in systems biology models.
- Identify failure points and signatures of truncated pathway models.
- Propose and evaluate an alternative simplification strategy.
Main Methods:
- Computational investigation of sequential multi-step pathway models.
- Comparison of truncated pathway models with an alternative assumption.
- Analysis of linear pathways with first-order kinetics and non-linear pathways.
Main Results:
- Truncated pathway models can fail to reproduce experimental data.
- Pathway truncation introduces detectable signatures in model dynamics and parameters.
- The proposed assumption of homogeneous propagation rates yields superior models.
Conclusions:
- Informed simplification is key for effective systems biology modeling.
- Assuming homogeneous information propagation rates offers a robust alternative to pathway truncation.
- This approach enables the development of terse yet accurate models for biological systems.
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