Systems analysis of eukaryotic translation initiation

The information contained in the genes of living cells has to be converted into cellular components that form structures and enable biochemical reactions to take place. This process is called gene expression and it is vital to all life. Gene expression comprises two main steps, called transcription and translation. In transcription, the information in the DNA sequences of the genes is converted into equivalent sequences in so-called messenger RNA (mRNA) molecules. In translation, the mRNA molecules are 'read' by a large molecular structure called the ribosome, which uses the information to dictate the synthesis of proteins. One of the major goals of science today is to place our knowledge of biology on a quantitative footing. This is the only way in which we will achieve a full understanding of how living systems work. In this project, we will apply this principle to the process of translation, determining numerical answers to questions about how the components of the system interact with each other and also control how fast protein synthesis takes place. This will lead to the development of a computer-based model that can be used to predict the behaviour of protein synthesis in vivo, thus opening up new opportunities to make the study of this fascinating process more rigorous and exact. Moreover, this type of systems analysis will help us determine how best to influence the rate of protein synthesis using drugs such as antibiotics. Since the targeting of such drugs to protein synthesis in bacterial and fungal organisms is potentially of great significance to human health, this work may accordingly contribute to the development of novel therapeutic strategies in the healthcare sector.

Systems analysis will be applied to eukaryotic translation initiation. Our understanding of rate control in eukaryotic translation so far is almost exclusively qualitative.We will therefore use an in vivo experimental system, combined with complementary in vitro systems to determine the rate control coefficients for all the factors involved in the initiation pathway. In vivo, expression of the genes encoding translation factors will be placed under the control of the TetO7 operator so that the intracellular abundance of the eIFs can be varied as a function of doxycycline concentration. Both cell-free translation extracts and a (partially) reconstituted initiation pathway will be used to study the influence of eIF levels on flux through the system in vitro. The use of these in vitro systems will serve to generate comparative data that supplement the measurements made in vivo. Additionally, we will define the affinities between the components of the translation initiation machinery (defining a 'quantitative interactome'). The data from the above will be inputted into an in silico model that will be refined to the point that it is capable of generating accurate quantitative predictions of the behaviour of the pathway in vivo. The resulting model will constitute a valuable framework for future research in the field.