Optimal synthesis of multistep protein purification processes

There has been an increasing interest in the development of systematic methods for the synthesis of purification steps for fermentation products, which are often the most difficult and costly stages in a biochemical process. There are several techniques of separation and purification of protein mixtures and the most important set includes chromatographic operations. Purification is attained after several steps and in each of them the mixture is split into two streams, one that contains the target protein and the other that is discarded. One of the main challenges in the synthesis of downstream purification stages is the appropriate selection and sequencing of chromatographic steps. The objective of this work is to develop methodologies for the synthesis of protein purification processes, which rely on mathematical models based on mixed integer programming. First, an optimization model is proposed that uses physicochemical data on a protein mixture, which contains the desired product, to calculate the minimum number of steps from a set of candidate chromatographic steps that must achieve a specified purity level. Since several sequences may attain this target protein specification, a second model is generated that uses the total number of steps found in the first model to select the operations and their sequence that maximizes the purity of product. Also, models are generated for the special case in which the purification process is sequence independent; in other words, only the selection of steps must be performed. The methodology is tested in examples with experimental data, containing up to 9 components and a set of 22 candidate chromatographic steps. The optimal solutions are verified experimentally and compared to the ones obtained by expert systems.