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Published on: October 6, 2019
Multi-scale genetic dynamic modelling II: application to synthetic biology: an algorithmic Markov chain based
Markus Kirkilionis1, Ulrich Janus, Luca Sbano
1Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK. mak@maths.warwick.ac.uk
This study details a synthetic genetic clock model in Escherichia coli, demonstrating how the "average dynamics" framework simplifies modeling complex genetic systems and integrates molecular details for experimental comparison.
Area of Science:
- Synthetic biology
- Computational biology
- Systems biology
Background:
- Engineered synthetic genetic clocks provide model systems for understanding biological regulation.
- Previous theoretical frameworks for genetic circuits have limitations in representing molecular details.
- The 'average dynamics' framework offers a novel approach to model biological systems.
Purpose of the Study:
- To present a detailed model of a synthetic genetic clock engineered in Escherichia coli.
- To illustrate the utility of the 'average dynamics' modeling framework for synthetic genetic systems.
- To demonstrate the potential for algorithmic automation in biological modeling processes.
Main Methods:
- Utilized the 'average dynamics' modeling framework for theoretical description.
- Modeled molecular interactions of known genetic components.
- Developed model variants representing genetic modules at different levels of detail.
Main Results:
- A dynamic mathematical model of the synthetic genetic clock was developed.
- The model allows for detailed representation of molecular players and their functions.
- The framework facilitates comparison of model predictions with experimental data across scales.
Conclusions:
- The 'average dynamics' framework enhances the representation of genetic components and their regulatory behavior.
- This approach facilitates the integration of mathematical modeling with bioinformatics tools.
- The framework is applicable to investigating complex genetic systems, including regulation and feedback mechanisms.
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