Genome Biology and Biotechnology The next frontier: Systems biology Prof. M. Zabeau Department of Plant Systems Biology Flanders Interuniversity Institute for Biotechnology (VIB) University of Gent International course 2005

Genomics

.. Functional Genomics

Systems Biology

gene pathway network Molecular Biology 60s to mid 80s Molecular Genetics since mid 80s Systems Biology since mid 90s From genes to networks

The large-scale organisation of metabolic networks ¤Study of the design principles underlying the structure of biological systems –Dissection of integrated “pathway-genome” databases providing complex connectivity maps Jeong et al (2000) Nature 407: 651

Case study ¤Analyses of core cellular metabolisms as –described in the `Intermediate metabolism and bioenergetics' portions of the WIT database ¤Prediction of metabolic pathways in organisms –on the basis of its annotated genome (presence of presumed open reading frame for enzymes that catalyse a given metabolic reaction) –in combination with firmly established data from the biochemical literature. ¤6 archaea, 32 bacteria and 5 eukaryotes Reprinted from: Jeong et al (2000) Nature 407: 651

Nodes are substrates Links are metabolic reactions (with EC enzyme numbers) Graph theoretic representation

Reprinted from: Jeong et al (2000) Nature 407: 651 Probability that a node has k links random uniform scale-free heterogeneous The World Wide Web and social networks have a scale-free structure Theoretical Network Architectures

Reprinted from: Jeong et al (2000) Nature 407: 651 Metabolic networks are scale-free as shown by the distribution of incoming and outgoing links for each substrate. This is a general rule applying to all organisms studied. Archaeglobus fulgidus E. coli C. elegans All 43 Connectivity distribution

Reprinted from: Jeong et al (2000) Nature 407: 651 Definition: the shortest “pathway”averaged over all pairs of substrates Biochemical pathway length in E. coli Average path length (43) Archae Bacteria Eukarya incoming linksoutgoing links Unexpectedly, network diameter does not increase with complexity. Therefore interconnectivity grows with the addition of substrates. Network diameter

Reprinted from: Jeong et al (2000) Nature 407: 651 A few hubs dominate the overall connectivity The sequential (“mutations”) removal of the most connected hubs dramatically increases the network diameter until disintegration the metabolic networks seem highly robust in computer simulations (cf. lethal mutation rate observed in vivo) Hub properties

Conclusions ¤The structure of biological networks are far from random –Their contemporary topology reflects a long evolutionary process –They show a robust response towards internal defects ¤Contrary to other scale-free networks, –metabolic ones do not grow in diameter with increasing complexity –which may be represent an additional (necessary?) survival and growth advantage Reprinted from: Jeong et al (2000) Nature 407: 651

Extension of the concept ¤Protein-protein interaction networks are also scale- free –yeast Y2H data ¤The probability for a gene to be essential –increases with the connectedness of the encoded protein –93% of proteins have 5 links or less 21% of their genes are essential –7% of have more than 15 links 62 % of their genes are essential Jeong et al (2001) Nature 411: 41

Reprinted from: Jeong et al (2001) Nature 411: 41

need more data! A long way to go… ¤List of biological components –cells, genes, proteins, metabolites ¤Description of local relationships –expression cluster –protein-protein interaction –molecule trafficking –cell-cell crosstalk ¤Whole system architecture ¤Dynamic regulatory mechanisms ¤System behaviour prediction ¤System manipulation, de novo design

Recommended reading ¤Large-scale organisation of biological networks Jeong et al (2000) Nature 407: 651 Oltvai and Barabasi (2002) Science 298: 763 ¤ Modelling at different levels Ideker and Lauffenburger (2003) TIB 21, 255 ¤Synthetic biology Elowitz and Leibner (2000) Nature 403: 335

Further reading ¤Large-scale organisation of biological networks Jeong et al (2001) Nature 411: 41 Han et al (2004) Nature 430: 88 Oltvai and Barabasi (2002) Science 298: 763 ¤Modelling at different levels Maere et al (2005) Bioinformatics 21: 3448 Vercruysse and Kuiper (2005) Bioinformatics 21: 269 ¤Synthetic biology Guet et al. (2002) Science 296: 1466