Predicting DNA kinetics with a truncated continuous-time Markov chain method.

Area of Science:

• Biotechnology
• Computational Biology
• Biophysics

Background:

• Predicting nucleic acid reaction kinetics is crucial for biology and biotechnology.
• Current methods using continuous-time Markov chains can be computationally expensive for rare events and require repeated simulations for parameter calibration.

Purpose of the Study:

• To introduce a novel method, pathway elaboration, for efficient and accurate prediction of nucleic acid reaction kinetics.
• To address the computational limitations of existing trajectory-based methods, especially for rare events and model calibration.

Main Methods:

• Pathway elaboration constructs a truncated continuous-time Markov chain using biased and unbiased sampling.
• This approach results in a moderate state space, enabling efficient computation of reaction rates using matrix methods.
• The method allows for easy adaptation of transition rates when model or environmental parameters are changed.

Main Results:

• Pathway elaboration accurately approximates trajectory-based predictions for toehold-mediated strand displacement reactions.
• The method demonstrates superior performance compared to alternative truncation-based approaches for mean first passage time estimation.
• Optimized parameters using pathway elaboration significantly improved reaction rate predictions in a kinetic model simulation.

Conclusions:

• Pathway elaboration offers an efficient and adaptable framework for predicting nucleic acid reaction kinetics.
• The method is particularly valuable for rare events, multiple prediction scenarios, and model calibration.
• This approach enhances the feasibility of computational studies in nucleic acid-based biotechnology and synthetic biology.