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Published on: March 11, 2020
A potential surrogate model for efficient inference of stochastic GLUT4 translocation
Brock D Sherlock1, Christopher Drovandi2, Marko A A Boon3
1School of Mathematics & Statistics, University of New South Wales, Sydney, 2052, NSW, Australia; Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, Eindhoven, 5600 MB, the Netherlands.
Developing a deterministic surrogate model significantly reduces computational costs for stochastic queuing network models. This enables more efficient parameter inference and hypothesis testing in biological systems like glucose transporter translocation.
Area of Science:
- Computational biology
- Mathematical modeling
Background:
- Stochastic models, like queuing networks, are valuable for simulating biological processes but are computationally expensive.
- This expense limits the exploration of parameters and hypotheses, hindering biological insights.
Purpose of the Study:
- To develop a computationally efficient deterministic surrogate model for a previously established glucose transporter (GLUT4) queuing network.
- To approximate the blocking mechanisms within the queuing network using differential equations with feedback terms.
Main Methods:
- A deterministic surrogate model was formulated using a system of differential equations.
- Feedback terms were incorporated to mimic the blocking mechanisms of the original queuing network.
- Sensitivity analysis was conducted to assess the surrogate model's behavior and its correspondence with the queuing network.
Main Results:
- The deterministic surrogate model successfully approximates the blocking mechanisms of the queuing network.
- Sensitivity analysis confirmed the surrogate model's performance and its correlation with the original model.
- The surrogate model offers a significant reduction in computational cost compared to the stochastic queuing network.
Conclusions:
- The developed deterministic surrogate model provides a computationally efficient alternative for simulating GLUT4 intracellular translocation.
- This approach facilitates more extensive parameter inference and hypothesis testing, advancing our understanding of insulin signaling.
- The surrogate model can be a valuable tool for recalibrating complex biological models at a lower computational expense.
