Download Lecture 1 / WS 2007/08

Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Lecture on Networks
WS 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
Edda Klipp
1
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Networks in Metabolism and Signaling
Edda Klipp
Humboldt University Berlin
Lecture 1 / WS 2007/08
Introduction
Networks, WS 07/08
Edda Klipp
2
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Overview
Content:
Networks, networks, networks,….
Examples
Basic definitions
Random networks, scale-free networks
Bayesian networks
Boolean networks
Petri nets
Kauffman networks
Different views for metabolic
networks (FBA)
Gene expression networks
Networks, WS 07/08
Edda Klipp
Aims:
Common organization principles
Describe network structure
Properties of different networks
robustness, scalefree,
pathlength,…
Biological applications & conclusions
Cellular design principles
Network-based dynamics
3
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Fashions in Biology
Early biology
“Last century”
Systems biology
Descriptive
Physiology
Whole organisms
Molecules, Proteins,
Genes,….
Biochemistry/
Molecular Biology
Networks, Interactions
Holistic view on
processes
Networks, WS 07/08
Edda Klipp
4
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Examples
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Edda Klipp
5
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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
Edda Klipp
6
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Metabolic Network
Human Glycolysis and Gluconeogenesis
As taken from KEGG
Contains metabolites and enzymes
Networks, WS 07/08
Edda Klipp
7
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Layers of Metabolic Regulation
Genes
mRNA
Enzyme
Metabolite
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Edda Klipp
Metabolite
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Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Signaling Networks
Bhalla & Iyengar, 1999, Science
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Edda Klipp
9
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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)
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Edda Klipp
10
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Human Disease Network, 1
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Edda Klipp
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Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Human Disease Network, 2
Networks, WS 07/08
Edda Klipp
12
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Human Disease Network, 3
Networks, WS 07/08
Edda Klipp
13
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Temporal protein
interaction network of
the yeast mitotic cell
cycle. Cell cycle proteins that
Lichtenberg et al., Science, 2005
Networks, WS 07/08
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 protein
names) and the underlying
data are available online at
www.cbs.dtu.dk/
cellcycle.
Edda Klipp
14
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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.
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Edda Klipp
Lappe et al., 2005, Biochem. Soc. Trans.
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Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
A Protein
Interaction Map
of Drosophila
melanogaster
Drosophila melanogaster is a proven model system
for many aspects of human biology. Here we present
a twohybrid–based protein-interaction map of the fly
proteome. 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 recapitulated
known 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.
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Edda Klipp
Giot et al, 2003, ScienceExpress
16
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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 corresponding
3522 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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Edda Klipp
17
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
PPI Local View
Splicing complex associated with sex determination.
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Edda Klipp
Giot et al, 2003, ScienceExpress
18
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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)
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Edda Klipp
19
Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical 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.
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Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)
Edda Klipp
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Max Planck Institute
Molecular Genetics
Humboldt University Berlin
Theoretical Biophysics
Gene Regulation Network Sea Urchin Embryo
Davidson, 2002,
Dev Biol
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Edda Klipp
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