The Computational Systems Biology (CSB) group studies the complex networks underlying cellular functions in order to elucidate the principles of their design and operation. Our research focuses on the development of concepts and tools for network inference, for system modelling and analysis, and for large-scale experimental design in different biological systems.
The recent increase in the amount of experimental data available (e.g. sequenced genomes, high-throughput transcriptomics and proteomics) has shifted the focus in biology towards how networks, which are more than the mere sum of their parts, establish biological functions. However, current computational methods cannot efficiently use the available biological knowledge and thus do not allow for the large-scale analysis of complex systems in sufficient detail. For example, concepts and tools for network inference, for system modelling and analysis, and for large-scale experimental design are not available. The CSB group focuses on the development of such bioinformatics methods. All projects involve close collaborations with experimental biologists and computer or systems scientists. Our particular interests lie in the determination of the design principles of the integration of cellular regulation with metabolism, signal transduction, and gene regulation. The approaches to the simplification of cellular complexity define the three main project areas detailed below.
Projects and Services
Structural network analysis
Network structures (e.g. reaction stoichiometries in metabolic networks) are usually better characterized than the functions of individual nodes. Structural network analysis attempts to elucidate functional features from the network topologies alone. This type of analysis is important for any functional modelling of (bio)chemical systems because it investigates the general constraints on network behaviour. The group develops efficient algorithms for the computation of pathways in metabolic networks and new methods for their analysis. Novel algorithmic concepts lead to major improvements in performance and scalability. With these tools, we can address questions such as the complexity and control of metabolic networks to reveal new drug targets.
System dynamics of cellular regulation
The aim of this project area is to develop methods and models for the detailed, dynamic analysis of networks in cellular regulation. However, a major obstacle for network inference and modelling is the high level of uncertainty resulting from the limited availability of experimental data and knowledge on many cellular networks. The group develops and applies new modelling and analysis approaches (e.g. ensemble modelling) that take account of structural model diversity. Analysis of features such as robustness is aimed at uncovering more general design principles. In particular, we study several biological examples such as TOR signalling, cell cycle and ribosome biogenesis, and circadian oscillators.
Synthetic biology is the synthesis of complex, biology-based (or biology-inspired) systems which display functions that do not exist in nature. This engineering perspective will enable the design of "biological systems" in a rational and systematic way. Our activities in this nascent field are concerned with the bioinformatics and computational biology aspects of the rational design process. For instance, model-based design has enabled the development of a time-delay circuit analogous to engineered systems. The CSB group is interested in principles of robust systems design and in the use of artificial genetic circuits in future medical applications.
Websites for Further Information
Computational Systems Biology (csb) Group http://www.csb.ethz.ch/