Design centering enables robustness screening of pattern formation models.

Area of Science:

• Systems Biology
• Computational Biology
• Biochemistry

Background:

• Quantitative biological data enables complex reaction-diffusion models of intracellular processes.
• Increased model complexity leads to more unknown parameters and higher computational costs.
• Efficiently characterizing model behavior and robustness is a key challenge in systems biology.

Purpose of the Study:

• To propose a novel computational framework for efficient high-dimensional parameter space characterization of reaction-diffusion models.
• To enable efficient prediction of model characteristics like bistability and pattern formation.
• To quantify robustness for model selection in polynomial time.

Main Methods:

• Leveraging the Lp-Adaptation algorithm, an adaptive-proposal statistical method.
• Utilizing an oracle function to predict model specification fulfillment.
• Developing specific oracles for bistability, instability, spontaneous pattern formation, and pattern maintenance in Turing-type models.

Main Results:

• The framework enables global exploration of pattern-forming instabilities in reaction-diffusion models.
• Quantification of robustness for model selection is achieved in polynomial time with dimensionality.
• Application to pattern formation by Rab5 on the endosomal membrane reveals underlying molecular mechanisms.

Conclusions:

• The proposed framework offers an efficient solution for analyzing complex reaction-diffusion models in systems biology.
• The method facilitates robust model selection and understanding of biological pattern formation.
• Open-source code is available for broader application and research.