Optimal Design of Aqueous Two-Phase Systems for Biomolecule Partitioning

Aqueous two-phase systems (ATPS) have exhibited superior performance in many biotechnological applications. To promote the implementation of these powerful platforms by industry in the downstream processing, an optimal design method is developed to tailor high-performance ATPS for partitioning biomolecules in this work. In this design method, two machine learning (ML) models that combine the artificial neural network (ANN) algorithm and group contribution (GC) method are respectively employed to predict the phase equilibrium composition of polymer-electrolyte ATPS and the partition of biomolecules in these aqueous systems. By integrating these two ANN-GC models into the computer-aided design technique, the optimal ATPS is identified by solving an optimization-based mixed-integer non-linear programming (MINLP) problem. As a proof of concept, results of partitioning cefazolin and β-amylase are presented. In the case of cefazolin, the partitioning performance of our tailored ATPS (PPG600 + KNaSO4 + H2O) is nearly 7 times greater than that of the reported ATPS (PEG6000 + Na3C6H5O7 + H2O). Meanwhile, the ATPS of PPG600 + KNaSO4 + H2O gives a cefazolin recovery of 95.0 wt % and an agent input of 0.154 kg/kg aqueous solution, and for the ATPS of PEG6000 + Na3C6H5O7 + H2O, these values are 90.6 and 0.233, respectively. For the second case, the partition coefficient of β-amylase in our proposed ATPS (PPG400 + KNaHPO4 + H2O) is about 13.5 times higher than that of the reported ATPS (PEG10000 + KH2PO4 + H2O). In addition, the ATPS of PPG600 + KNaSO4 + H2O gives an β-amylase recovery of 97.3 wt % at a cost of 0.387 kg agent input/kg aqueous solution, and for the ATPS of PEG6000 + Na3C6H5O7 + H2O, they are 66.3 and 0.252, respectively.