Optimal Design of Aqueous Two-Phase Systems for Biomolecule Partitioning

Aqueous two-phase systems (ATPS) have exhibited superiorperformancein many biotechnological applications. To promote the implementationof these powerful platforms by industry in the downstream processing,an optimal design method is developed to tailor high-performance ATPSfor partitioning biomolecules in this work. In this design method,two machine learning (ML) models that combine the artificial neuralnetwork (ANN) algorithm and group contribution (GC) method are respectivelyemployed to predict the phase equilibrium composition of polymer-electrolyteATPS and the partition of biomolecules in these aqueous systems. Byintegrating these two ANN-GC models into the computer-aided designtechnique, the optimal ATPS is identified by solving an optimization-basedmixed-integer non-linear programming (MINLP) problem. As a proof ofconcept, results of partitioning cefazolin and & beta;-amylase arepresented. In the case of cefazolin, the partitioning performanceof 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 0.154kg/kg aqueous solution, and for the ATPS of PEG6000 + Na3C6H5O7 + H2O, these valuesare 90.6 and 0.233, respectively. For the second case, the partitioncoefficient of & beta;-amylase in our proposed ATPS (PPG400 + KNaHPO4 + H2O) is about 13.5 times higher than that ofthe reported ATPS (PEG10000 + KH2PO4 + H2O). In addition, the ATPS of PPG600 + KNaSO4 +H2O gives an & beta;-amylase recovery of 97.3 wt % at acost of 0.387 kg agent input/kg aqueous solution, and for the ATPSof PEG6000 + Na3C6H5O7 + H2O, they are 66.3 and 0.252, respectively.