Genome Biology and Genome Biology and BiotechnologyBiotechnology
The next frontier: Systems biologyThe next frontier: Systems biology
Prof. M. ZabeauProf. M. ZabeauDepartment of Plant Systems Biology Department of Plant Systems Biology
Flanders Interuniversity Institute for Biotechnology (VIB)Flanders Interuniversity Institute for Biotechnology (VIB)University of GentUniversity of Gent
International course 2005International course 2005
Genomics
.
.
Functional Genomics
SystemsBiology
gene
pathway
network
Molecular Biology60s to mid 80s
Molecular Geneticssince mid 80s
Systems Biologysince mid 90s
From genes to networksFrom genes to networks
The large-scale organisation of metabolic The large-scale organisation of metabolic networksnetworks
¤ 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 studyCase 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
Reprinted from: Jeong et al (2000) Nature 407: 651
Nodes are substratesLinks are metabolic reactions (with EC enzyme numbers)
Graph theoretic representationGraph theoretic representation
Reprinted from: Jeong et al (2000) Nature 407: 651
Probability that
a node has k links
randomuniform
scale-freeheterogeneous
The World Wide Web and social
networks have a scale-free structure
Theoretical Network ArchitecturesTheoretical 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 distributionConnectivity 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. coliAverage path length
(43)
ArchaeBacteriaEukarya
incoming links outgoing links
Unexpectedly, network diameter does not increase with complexity. Therefore interconnectivity grows with the addition of substrates.
Network diameterNetwork 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 propertiesHub properties
ConclusionsConclusions
¤ 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 conceptExtension 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
needmoredata!
A long way to go…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 readingRecommended 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 readingFurther 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
