Networks, WS 07/081 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 07/082 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Networks in Metabolism and Signaling Edda Klipp Humboldt University Berlin Lecture 1 / WS 2007/08 Introduction

Networks, WS 07/083 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 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 07/084 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Fashions in Biology Early biology Descriptive Physiology Whole organisms “Last century” Molecules, Proteins, Genes,…. Biochemistry/ Molecular Biology Systems biology Networks, Interactions Holistic view on processes

Networks, WS 07/085 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Examples

Networks, WS 07/086 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 Mg 2+ -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 07/087 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Metabolic Network Human Glycolysis and Gluconeogenesis As taken from KEGG Contains metabolites and enzymes

Networks, WS 07/088 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Layers of Metabolic Regulation Metabolite Enzyme mRNA Genes

Networks, WS 07/089 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Signaling Networks Bhalla & Iyengar, 1999, Science

Networks, WS 07/0810 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 07/0811 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Human Disease Network, 1

Networks, WS 07/0812 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Human Disease Network, 2

Networks, WS 07/0813 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Human Disease Network, 3

Networks, WS 07/0814 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 protein names) and the underlying data are available online at cellcycle. Lichtenberg et al., Science, 2005

Networks, WS 07/0815 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 07/0816 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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. Giot et al, 2003, ScienceExpress

Networks, WS 07/0817 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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

Networks, WS 07/0818 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp PPI Local View Splicing complex associated with sex determination. Giot et al, 2003, ScienceExpress

Networks, WS 07/0819 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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 07/0820 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp 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)

Networks, WS 07/0821 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Edda Klipp Gene Regulation Network Sea Urchin Embryo Davidson, 2002, Dev Biol