Progress in molecular biology and development of new techniques (DNA sequencing, ESTs, microarrays and advanced mass spectrometry) have provided the biosciences with reasonably complete descriptions of biological systems at the genomic and transcriptomic levels of organisation, and further "omic" levels are well underway. This provides a description of biological systems at an unprecedented level of detail. At the same time, the challenge arises to scientifically understand the molecular levels of biological organisation in a principle-based way, such that generic properties of biological systems can be distinguished from irrelevant details, and ultimately, prediction and rational design of biological systems becomes possible
We are interested in the principles underlying morphogenesis and spatial organisation in biological systems, and in the evolution of such systems. While we use plants as model systems, our long-term objective is to identify generic principles.
For modelling regulatory networks, we are developing the transsys language. transsys enables the description of regulatory networks in a compact, object-oriented format, facilitates computational simulation of gene expression dynamics, and provides a set of tools for generating networks and for analysing simulations. The image on the left shows an example of a transsys network.
Spatial structure is accounted for by embedding instances of transsys networks within components of a spatially extended or growing (i.e. extending) system. The L-transsys simulator has thus been realised by embedding transsys within the formal framework of parametric Lindenmayer systems. By replacing the generic (and unstructured) parameter lists with transsys instances, a simulator in which both the rules of spatial growth (captured by the Lindenmayer system) and the regulatory network (captured by the transsys network) can be represented and analysed integratively. The image on the right shows a simple Arabidopsis flower model. Its morphogenesis is organised by the network shown on the left.
Determining regulatory networks by "reversely engineering" of gene expression measurement is currently a major challenge in Systems Biology. Advances in this field require a framework which can provide simulated gene expression measurement data from a known regulatory network, so that the performance of network reconstruction can be assessed by comparing the reconstructed network to the real one. During recent years, transsys has been used to build such a framework, which uniquely combines flexibility of network modelling with extensive potentials of integrating further levels of biological organisation, as exemplified by L-transsys above.
Research Team: Dr. Jan Kim
