Networks, WS 07/08 1
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Lecture on NetworksWS 2007/08
Prof. Edda Klipp
Mondays, 12:00-13:30, Zentrallabor
Written exam
Problems all two weeks, discussion during next lecture
Networks, WS 07/08 2
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Networks in Metabolism and Signaling
Edda Klipp Humboldt University Berlin
Lecture 1 / WS 2007/08Introduction
Networks, WS 07/08 3
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Overview
Content:
Networks, networks, networks,….
Examples
Basic definitions
Random networks, scale-free networks
Bayesian networks
Boolean networks
Petri nets
Kauffman networksDifferent views for metabolic networks (FBA)
Gene expression networks
Aims:
Common organization principles
Describe network structure
Properties of different networks
robustness, scalefree,
pathlength,…
Biological applications & conclusions
Cellular design principles
Network-based dynamics
Networks, WS 07/08 4
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Fashions in Biology
Early biology
DescriptivePhysiologyWhole organisms
“Last century”
Molecules, Proteins,Genes,….Biochemistry/ Molecular Biology
Systems biology
Networks, InteractionsHolistic view on processes
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Examples
Networks, WS 07/08 6
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Metabolic Networks
Barabasi & Oltvai, Nature Rev Gen 5, 101 (2004)
To study the network characteristics of the metabolism a graph theoretic description needs to be established. (a) illustrates the graph theoretic description for a simple pathway (catalysed by Mg2+-dependant enzymes).(b) In the most abstract approach all interacting metabolites are considered equally. The links between nodes represent reactions that interconvert one substrate into another. For many biological applications it is useful to ignore co-factors, such as the high-energy-phosphate donor ATP, which results (c) in a second type of mapping that connects only the main source metabolites to the main products.
Networks, WS 07/08 7
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Metabolic Network
Human Glycolysis and GluconeogenesisAs taken from KEGG
Contains metabolites and enzymes
Networks, WS 07/08 8
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Layers of Metabolic Regulation
Metabolite Metabolite
Enzyme
mRNA
Genes
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Signaling Networks
Bhalla & Iyengar, 1999, Science
Networks, WS 07/08 10
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Yeast Protein-Protein Interactions
A map of protein–protein interactions in Saccharomyces cerevisiae, which is based on early yeast two-hybrid measurements, illustrates that a few highly connected nodes (which are also known as hubs) hold the network together.
The largest cluster, which contains 78% of all proteins, is shown. The color of a node indicates the phenotypic effect of removing the corresponding protein (red = lethal, green = non-lethal, orange = slow growth, yellow = unknown).
Barabasi & Oltvai, Nature Rev Gen 5, 101 (2004)
Networks, WS 07/08 11
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Human Disease Network, 1
Networks, WS 07/08 12
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Human Disease Network, 2
Networks, WS 07/08 13
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Human Disease Network, 3
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Temporal protein interaction network of the yeast mitotic cell cycle. Cell cycle proteins that are part of complexes or other physical interactions are shown within the circle. For the dynamic proteins, the time of peak expression is shown by the node color; static proteins are represented by white nodes. Outside the circle, the dynamic proteins without interactions are both positioned and colored according to their peak time and thus also serve as a legend for the color scheme in the network. More detailed versions of this figure (including all proteinnames) and the underlying data are available online at www.cbs.dtu.dk/cellcycle.
Lichtenberg et al., Science, 2005
Networks, WS 07/08 15
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Textmining: Protein-Protein Interaction
(A) The known pheromone signalling pathway [17]. (B) Thick lines indicate the ‘backbone’ linking a cell-surface receptor (Ste2) to a transcription factor (Cln1). The backbone follows the most reliable edges in a yeast interaction network based on statistical associations in Medline abstracts. The thin lines link ‘associated factors’ to the backbone. These nodes are generally connected to the backbone proteins.
Lappe et al., 2005, Biochem. Soc. Trans.
Networks, WS 07/08 16
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
A Protein Interaction
Map of Drosophila
melanogasterDrosophila melanogaster is a proven model system for many aspects of human biology. Here we present a twohybrid–based protein-interaction map of the flyproteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 nteractions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulatedknown pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms including humans.
Giot et al, 2003, ScienceExpress
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Global views of the protein interaction
map
(A) Protein family/human disease ortholog view. Proteins are color-coded according to protein family as annotated by the Gene Ontology hierarchy. Proteins orthologous to human disease proteins have a jagged starry border. Interactions were sorted according to interaction confidence score and the top 3000 interactions are shown with their corresponding3522 proteins. This corresponds roughly to a confidence score of 0.62 and higher. (B) Subcellular localization view. This view shows the fly interaction map with each protein colored by its Gene Ontology Cellular Component annotation. This map has been filtered by only showing proteins with less than or equal to 20 interactions and with at least one Gene Ontology annotation (not necessarily a cellular component annotation). We show proteins for all interactions with a confidence score of 0.5 or higher. This results in a map with 2346 proteins and 2268 interactions.
Giot et al, 2003, ScienceExpress
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
PPI Local View
Splicing complex associated with sex determination. Giot et al, 2003, ScienceExpress
Networks, WS 07/08 19
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Transcriptional regulatory networks RegulonDB: database with information on transcriptional regulation and operon
organization in E.coli; 105 regulators affecting 749 genes
7 regulatory proteins (CRP, FNR, IHF, FIS, ArcA, NarL and Lrp) are sufficient
to directly modulate the expression of more than half of all E.coli genes.
Out-going connectivity follows
a power-law distribution In-coming connectivity follows
exponential distribution (Shen-Orr).
Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)
Networks, WS 07/08 20
Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Regulatory cascades The TF regulatory network in E.coli.
When more than one TF regulates a gene,
the order of their binding sites is as given in
the figure. An arrowhead is used to indicate
positive regulation when the position of the
binding site is known.
Horizontal bars indicates negative regulation
when the position of the binding site is
known. In cases where only the nature of
regulation is known, without binding site
information, + and – are used to indicate
positive and negative regulation.
The DBD families are indicated by circles of
different colours as given in the key. The
names of global regulators are in bold. Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)
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Max Planck Institute Molecular Genetics
Humboldt University BerlinTheoretical Biophysics
Gene Regulation Network Sea Urchin Embryo
Davidson, 2002,Dev Biol