Mathematical Modeling and Thermodynamic Integration for Precision PCR Optimization

The objective of this study was to develop a predictive modeling framework for optimizing MgCl₂ concentration and melting temperature (Tm) in PCR conditions.

The study developed predictive models using multivariate Taylor series expansion and thermodynamic functions integrated with 120 PCR primers across various species. Ridge, Lasso, and Elastic Net multiple regression analyses were applied to fine-tune these models.

The models demonstrated excellent predictive capabilities, achieving an R² =0.9942 for MgCl₂ concentration and 0.9600 for Tm. These findings underscore the efficacy of the proposed optimization strategy.

The research provides a tight framework for optimizing PCR conditions and enhancing specificity and sensitivity in DNA amplification. More importantly, it is based on an approach that combines theoretical modeling with empirical validation, thus offering valuable insight into the advancement of PCR-based methodologies.