networks, ws 07/081 max planck institute molecular genetics humboldt university berlin theoretical...

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 in Metabolism and Signaling

Edda Klipp Humboldt University Berlin

Lecture 1 / WS 2007/08Introduction




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




Fashions in Biology

Early biology

DescriptivePhysiologyWhole organisms

“Last century”

Molecules, Proteins,Genes,….Biochemistry/ Molecular Biology

Systems biology

Networks, InteractionsHolistic view on processes




Examples




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.




Metabolic Network

Human Glycolysis and GluconeogenesisAs taken from KEGG

Contains metabolites and enzymes




Layers of Metabolic Regulation

Metabolite Metabolite

Enzyme

mRNA

Genes




Signaling Networks

Bhalla & Iyengar, 1999, Science




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)




Human Disease Network, 1




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




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.




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




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




PPI Local View

Splicing complex associated with sex determination. Giot et al, 2003, ScienceExpress




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)




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)




Gene Regulation Network Sea Urchin Embryo

Davidson, 2002,Dev Biol

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