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VL Netzwerke, WS 2007/08 Edda Klipp 1 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Networks in Metabolism.

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Presentation on theme: "VL Netzwerke, WS 2007/08 Edda Klipp 1 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Networks in Metabolism."— Presentation transcript:

1 VL Netzwerke, WS 2007/08 Edda Klipp 1 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Networks in Metabolism and Signaling Edda Klipp Humboldt University Berlin Lecture 3 / WS 2007/08 Random Networks: Scale-free Networks

2 VL Netzwerke, WS 2007/08 Edda Klipp 2 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Random Networks: Scale-free Networks Most “natural” networks do not have a typical degree value, they are free of a characteristic “scale”: they are scale-free. Their degree distribution follows a Power law: P(k) ~ k -  Heterogeneity: hubs with high connectivity, many nodes with low connectivity.

3 VL Netzwerke, WS 2007/08 Edda Klipp 3 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Random Networks: Scale-free Networks The probability that a node is highly connected is statistically more significant than in a random graph, the network’s properties often being determined by a relatively small number of highly connected nodes that are known as hubs. Barabási–Albert model of a scale-free network: Growth: Start with small number of nodes ( m 0 ) At each time point add a new node with m ≤ m 0 links to the network Preferential attachment: Probability for a new edge is where k I is the degree of node I and J is the index denoting the sum over network nodes. The network that is generated by this growth process has a power-law degree distribution that is characterized by the degree exponent  = 3.

4 VL Netzwerke, WS 2007/08 Edda Klipp 4 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Evolution of Scale-free Networks After t time steps  network with N = t + m 0 nodes and m t edges. Continuum theory: k i as continuous real variable, change proportional to  (k i )

5 VL Netzwerke, WS 2007/08 Edda Klipp 5 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Evolution of Scale-free Networks After t time steps  network with N = t + m 0 nodes and m t edges. Continuum theory: k i as continuous real variable, change proportional to  (k i ) Degree distribution independent of time t And independent of network size N But proportional to m 2

6 VL Netzwerke, WS 2007/08 Edda Klipp 6 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Evolution of Scale-free Networks After t time steps  network with N = t + m 0 nodes and m t edges. Master equation approach: probability p(k,t i,t) that a node i introduced at time t i has degree k at time t. Main idea: Dorogovtsev&Mendes-2001

7 VL Netzwerke, WS 2007/08 Edda Klipp 7 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Degree Distribution

8 VL Netzwerke, WS 2007/08 Edda Klipp 8 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Clustering coefficient No inherent clustering coefficent C(k)

9 VL Netzwerke, WS 2007/08 Edda Klipp 9 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Average Path Length Shortest path length l : distance between two vertices u and v with unit length edges Fully connected network: Rough estimation for random network: Average number of nearest neighbors:  vertices are at distance l or closer total number of vertices

10 VL Netzwerke, WS 2007/08 Edda Klipp 10 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Average Path Length Is obtained by fitting

11 VL Netzwerke, WS 2007/08 Edda Klipp 11 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Error and Attack Tolerance Question: consider arbitrary connected graph of N nodes and assume that a p fraction of edges have been removed. What is the probability of the resulting graph being still connected? Usually: existence of a threshold probability pc. More severe: removal of nodes

12 VL Netzwerke, WS 2007/08 Edda Klipp 12 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Scale-free Networks: Error and Attack Tolerance Node Removal The relative size S (a),(b) and average path length l (c),(d) of the largest cluster in an initially connected network when a fraction f of the nodes are removed. (a),(c) Erdös-Renyi random network with N= and =4; (b),(d) scale-free network generated by the Barabasi- Albert model with N= and =4. , random node removal; º, preferential removal of the most connected nodes.

13 VL Netzwerke, WS 2007/08 Edda Klipp 13 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Random Networks: Scale-free Networks Difference to classical random networks: Growth of the network – given number of nodes Preferential attachment – equal probability for all edges Examples: References in www: connections frequently to existing hubs Metabolism: many molecules are involved in only a few (1,2) reactions, others (like ATP or water) in many Wagner/Fell: highly connected molecules are evolutionary “old”

14 VL Netzwerke, WS 2007/08 Edda Klipp 14 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Properties of Scale-Free Networks Small-world – short paths between arbitrary points Robustness – Topological robustness

15 VL Netzwerke, WS 2007/08 Edda Klipp 15 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Watts & Strogatz Model Problem: real networks have short average path length and greater clustering coefficients than classical random graphs Construction: Initially, a regular one dimensional lattice with periodical boundary conditions is present. Each of L vertices has z ≥ 4 nearest neighbors. Then one takes all the edges of the lattice in turn and with probability p rewires to randomly chosen vertices. In such a way, a number of far connections appears. Obviously, when p is small, the situation has to be close to the original regular lattice. For large enough p, the network is similar to the classical random graph.

16 VL Netzwerke, WS 2007/08 Edda Klipp 16 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Watts-Strogatz-Model By definition of Watts and Strogatz, the smallworld networks are those with “small” average shortest path lengths and “large” clustering coecients. remains constant During rewiring

17 VL Netzwerke, WS 2007/08 Edda Klipp 17 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Random Networks: Hierarchical Networks To account for the coexistence of modularity, local clustering and scale- free topology in many real systems it has to be assumed that clusters combine in an iterative manner, generating a hierarchical network. The starting point of this construction is a small cluster of four densely linked nodes. Next, three replicas of this module are generated and the three external nodes of the replicated clusters connected to the central node of the old cluster, which produces a large 16-node module. Three replicas of this 16-node module are then generated and the 16 peripheral nodes connected to the central node of the old module, which produces a new module of 64 nodes….

18 VL Netzwerke, WS 2007/08 Edda Klipp 18 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Random Networks: Hierarchical Networks Clustering coefficent scales with the degree of the nodes

19 VL Netzwerke, WS 2007/08 Edda Klipp 19 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics

20 VL Netzwerke, WS 2007/08 Edda Klipp 20 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics

21 VL Netzwerke, WS 2007/08 Edda Klipp 21 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Application to Metabolic Networks Jeong H et al, 2000, Nature

22 VL Netzwerke, WS 2007/08 Edda Klipp 22 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Metabolic Networks: Degree Distributions

23 VL Netzwerke, WS 2007/08 Edda Klipp 23 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Metabolic Networks: Hubs

24 VL Netzwerke, WS 2007/08 Edda Klipp 24 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Metabolic Networks: Pathway Lengths E.coli43 different organisms

25 VL Netzwerke, WS 2007/08 Edda Klipp 25 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Metabolic Networks: Robustness Hubs removed first Random removal M=60 – 8% of substrates

26 VL Netzwerke, WS 2007/08 Edda Klipp 26 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Protein-Protein-Interaction Networks Yeast proteome a)Map of protein-protein interactions Largest cluster: 78% of all proteins Red – lethal Green – non-lethal Orange – slow-growth Yellow – unknown Cut-off: k c =20 b) Connectivity distribution c) Fraction of essential proteins with k links Random removal – no effect Hubs removal – lethal

27 VL Netzwerke, WS 2007/08 Edda Klipp 27 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Protein-Protein-Interaction Networks Random removal – no effect Hubs removal – lethal 93% of proteins have 5 or less links only 21% of them are essential 0.7% of proteins have more than 15 links 62% of them are lethal

28 VL Netzwerke, WS 2007/08 Edda Klipp 28 Max Planck Institute Molecular Genetics Humboldt University Berlin Theoretical Biophysics Protein-Protein-Interaction Networks


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