Towards universal systems for recombinant gene expression

Recombinant gene expression is among the most important techniques used both in molecular and medical research and in industrial settings. Today, two recombinant expression systems are particularly well represented in the literature reporting on recombinant expression of specific genes. According to searches in the PubMed citation database, during the last 15 years 80% of all recombinant genes reported on in the literature were expressed in either the enterobacterium Escherichia coli or the methylotropic yeast Pichia pastoris. Nevertheless, some eukaryotic proteins are misfolded or inadequately posttranslationally modified in these expression systems. This situation demands identification of other recombinant expression systems that enable the proper expression of the remaining eukaryotic genes. As of now, a single universal system allowing expression of all target genes is still a distant goal. In this light, thorough experimental screening for systems that can yield satisfying quantity and quality of target protein is required. In recent years, a number of new expression systems have been described and used for protein production. Two systems, namely Drosophila melanogaster S2 insect cells and human embryonic kidney 293 (HEK293) cells stably expressing the EBNA-1 gene, show exceptional promise. The time has come to identify a few well-performing systems that will allow us to express, purify, and characterize entire eukaryotic genomes

Soluble expression of recombinant proteins in the cytoplasm of Escherichia coli

Pure, soluble and functional proteins are of high demand in modern biotechnology. Natural protein sources rarely meet the requirements for quantity, ease of isolation or price and hence recombinant technology is often the method of choice. Recombinant cell factories are constantly employed for the production of protein preparations bound for downstream purification and processing. Eschericia coli is a frequently used host, since it facilitates protein expression by its relative simplicity, its inexpensive and fast high density cultivation, the well known genetics and the large number of compatible molecular tools available. In spite of all these qualities, expression of recombinant proteins with E. coli as the host often results in insoluble and/or nonfunctional proteins. Here we review new approaches to overcome these obstacles by strategies that focus on either controlled expression of target protein in an unmodified form or by applying modifications using expressivity and solubility tags

A gradient boosting approach for optimal selection of bidding strategies in reservoir hydro

Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market, and they are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either continuously be registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques (gradient boosting and neural networks). A wide range of variables accessible to the models prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance