The organization of life
Cells are the building blocks of life, and they come in many forms and shapes, from self-sustained bacteria to highly specialized neurons in the human brain. At a glance, cells might come across as small bags filled with proteins and DNA. Still, a closer look reveals a high degree of spatial and temporal organization. Proteins associate in large assemblies or complexes which seemingly reorganize in a context-dependent manner, and the cell division is strictly controlled temporally so that each step in the cycle is carried out in order. Understanding this organization will allow us to gain insight into diseases at the molecular level. Understanding disease at the molecular level will be fundamentally important when developing tomorrow's therapeutics.
The study of the organization of cells sometimes called systems biology, and as the name suggest, one approach biology from a systemic point of view. My research group is approaching this from two different angles. The first is to model protein abundance regulation by accurately measuring protein abundance on a systemic level. The second is to understand how proteins assemble into complexes.
Check out the systems biology page for more information.
Proteins carry out a vast number of functions in cells, from providing scaffolds and necessary structures to catalyzing chemical reactions. Mass spectrometry is one of the best-suited tools to study proteins as it is fast and sensitive and can be used to identify proteins and modifications (either biological (post-translational modifications) or synthetic (such as chemical cross-links)) and quantify both between different conditions or absolutely by using references. Measuring the difference in protein abundance between samples can be informative in multiple ways. For example, differences in protein expression between a healthy and diseased state can provide clues about what is causing the disease and how the cells respond to various stimuli. My research attempts to find ways to improve label-free quantification technology in which the samples have not been isotopically labeled. The advantage of this approach is that it is possible to compare more samples (labeled approaches are limited to the number of labels the technique of choice supports), and one avoids problems that might arise from the labels influencing the fragmentation of the ions. The drawback is that the data analysis can become difficult because each condition is measured at different times. In addition, we attempt to find post-translational modifications and cross-links, which can provide information about protein function and spatial organization.
Check out the mass spectrometry page for more information.
Protein structure prediction
Proteins are in the lowest energy conformation, and the process in which they assume this conformation from an extended, unfolded state is called protein folding. It is possible to simulate this event using computers and hence to predict a protein's lowest energy conformation (native state) using protein structure prediction technologies. Integrating mass spectrometry (MS) data with de novo protein structure prediction technology can elucidate protein structures fast and cheap with higher accuracy than using only in silico approaches.
Check out the structure prediction page for more information.
The amount of biological information produced each year is growing exponentially. This increases the need for tools that can organize and analyze this wealth of information as well as tools that can "learn" from well-studied areas of biology and predict features of areas where little or nothing is known.
Check out the information management page for more information.
High-performance computing requires careful design and implementation of hardware and software solutions. I have built two medium-sized computer systems (at the time) in the past, and we have been putting together a heterogeneous computing environment strategy here at the ETH in the past year.
Check out the computer infrastructure page for more information.