Multi-objective optimisation of polymerase chain reaction continuous flow systems

Abstract

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 $$\sim$$ ∼ 5.7%, $$\sim$$ ∼ 80.5%, $$\sim$$ ∼ 17.8% and $$\sim$$ ∼ 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.

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“…In the first step, the dynamics of capillary-driven fluid are resolved to procure a macroscopic velocity field u, clarified by the fluid viscosity, porosity, permeability, and the geometrical boundaries of the fluid zone. Coupled with this velocity field, the macroscopic species concentration field C is utilized as follows (Elizalde et al 2015 ): where J is the species’ total mass flux and is derived from the linear superposition of advection, diffusion, and mechanical dispersion transport mechanisms, respectively, by the following equation (Schaumburg et al 2018 ; Zagklavara et al 2022 ): where is molecular diffusion coefficient and is the dispersivity constant, which demonstrates a characteristic dimension of the porous fiber network microstructure (Urteaga et al 2018 ).…”