Multi-objective optimisation of polymerase chain reaction continuous flow systems

A surrogate-enabled multi-objective optimisation methodology for a continuous flow Polymerase Chain Reaction (CFPCR) systems is presented, which enables the effect of the applied PCR protocol and the channel width in the extension zone on four practical objectives of interest, to be explored. High fidelity, conjugate heat transfer (CHT) simulations are combined with Machine Learning to create accurate surrogate models of DNA amplification efficiency, total residence time, total substrate volume and pressure drop throughout the design space for a practical CFPCR device with sigmoid-shape microfluidic channels. A series of single objective optimisations are carried out which demonstrate that DNA concentration, pressure drop, total residence time and total substrate volume within a single unitcell can be improved by up to similar to 5.7%, similar to 80.5%, similar to 17.8% and similar to 43.2% respectively, for the practical cases considered. The methodology is then extended to a multi-objective problem, where a scientifically-rigorous procedure is needed to allow designers to strike appropriate compromises between the competing objectives. A series of multi-objective optimisation results are presented in the form of a Pareto surface, which show for example how manufacturing and operating cost reductions from device miniaturisation and reduced power consumption can be achieved with minimal impact on DNA amplification efficiency. DNA amplification has been found to be strongly related to the residence time in the extension zone, but not related to the residence times in denaturation and annealing zones.

Automated summary

A surrogate-enabled multi-objective optimization methodology is proposed for continuous flow Polymerase Chain Reaction (CFPCR) systems, allowing the exploration of the effect of PCR protocol and channel width on practical objectives. Accurate surrogate models of DNA amplification efficiency, total residence time, total substrate volume, and pressure drop are created using high fidelity conjugate heat transfer simulations and Machine Learning. Single objective optimizations demonstrate improvements in DNA concentration, pressure drop, residence time, and substrate volume, while multi-objective optimizations provide a Pareto surface that shows how cost reductions and DNA amplification efficiency can be achieved simultaneously.