4th round table

November 09, 2012

Rational RNA regulator design
Sven Findeiß, Bioinformatics and Computational Biology research group (BCB), University of Vienna

Designing the optimal ethanol producing E. Coli
Christian Jungreuthmayer, Metabolism modelling group, ACIB GmbH

 

Rational RNA regulator design
Sven Findeiß, Bioinformatics and Computational Biology research group (BCB), University of Vienna

Gene expression is usually regulated either by proteins or RNA elements. The latter mechanism is realized by independent RNA transcripts, called small or non-coding RNAs, and host gene encoded structural elements such as riboswitches and thermosensors.

Riboswitches are typically encoded in bacterial genomes and are located up- or down-stream of a protein coding sequence. Depending on their structural state riboswitches enhance or repress the protein's expression. As riboswitches are generally encoded on the same transcript as their target they represent a direct and efficient regulation mechanism on RNA level.

My talk will give a brief introduction to regulatory RNA elements with emphasis on riboswitches. On the basis of natural occurring riboswitches and their observed regulatory mechanisms I will present our recent work on the engineering of an artificial riboswitch. Here in silico methods and their limitations will be in the main focus.

  

Designing the optimal ethanol producing E. Coli
Christian Jungreuthmayer, Metabolism modelling group, ACIB GmbH

Fossil fuels are a restricted resource that will ultimately run out. In addition, they increase the amount of CO2 in the atmosphere. Biofuels such as ethanol and biodiesel are a promising alternative to fossil fuels. Additionally, biofuels have a positive effect on global warming, as the only produce CO2 that was absorbed by their source plants in the first place. However, biofuels are often made from agricultural products. Hence, biofuel production competes with food production. Consequently, the production of biofuels may result in increasing food prices. Therefore, the production efficiency of biofuels needs to be increased.

We address this important issue by designing microorganisms (e.g. E. Coli) that produce bioethanol as efficient as possible. This is achieved by genetic intervention. The genes to be knocked out to create an optimal producer are numerically identified. This identification process involves the computation of elementary flux modes and determining the minimal set of genes that need to be knocked out.

In my talk I will introduce the concept of elementary flux modes and minimal knockout sets. Furthermore, I will address the difficulties in using these methods and explain two novel approaches we have recently developed to speed up and simplify the design process of optimal producers.