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Network Biology: Understanding The Cell’s Functional Organization

Network Biology: Understanding The Cell’s Functional Organization. Albert-L á szl ó Barab á si 1) and Zolt á n N. Oltvai 2) 1) Dept. of physics, University of Notre Dame 2) Dept. of Pathology, Northwestern University Presentation: Haseong Kim (2007. 1. 8)

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Network Biology: Understanding The Cell’s Functional Organization

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  1. Network Biology: Understanding The Cell’s Functional Organization Albert-László Barabási 1) and Zoltán N. Oltvai 2) 1) Dept. of physics, University of Notre Dame 2) Dept. of Pathology, Northwestern University Presentation: Haseong Kim (2007. 1. 8) Bioinformatics and Biostatistics Lab. SNU

  2. Introduction • A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. • Most biological characteristics arise from complex interactions between the cell’s numerous constituents, such as proteins, DNA, RNA and small molecules. • New technology platforms to determine how and when these molecules interact with each other. • Microarrays, Protein chips, Yeast two-hybrid screens Two-hybrid system Yeast two-hybrid screen

  3. Complex Networks • The complexity of numerous social, biological, or communication system is composed of the system’s components and their interactions • Metabolic network • Substrates and enzymes, and edges represent chemical interactions. • Social network • Individuals or organizations connected by social interactions • Business world • Nodes are companies and edges represent divers trade relationships • World-wide web • Html docs connected by links • Internet service provider networks ISP networks

  4. Network models • The exponential network is homogeneous: most nodes have approximately the same number of links. • The scale-free network is inhomogeneous: the majority of the nodes have one or two links but a few nodes have a large number of links, guaranteeing that the system is fully connected.

  5. Network Measures • Degree • How many links the node has to other nodes (k) • kin, kout • Degree distribution • P(k) gives the probability that a selected node has exactly k link. P(k)=N(k)/N • Scale-free networks and the degree exponent • The degree distribution approximates a power law, P(k) ~ k-γ • Shortest path and mean path length • How many links we need to pass through to travel between two nodes. • Clustering coefficient • Node A  Node B  Node C then it is highly probable that A also has a direct link to C • CI=2nI / k(k-1) where nI is the # links connecting the kI neighbours of node I to each other. CA=2/20 • <k>, <l>, <C> depend on the number of nodes and links (NandL) • P(k) and C(k) functions are independent of the network’s size. • P(k) and C(k) capture a network’s generic features, which allows them to be used to classify various networks.

  6. ReferenceAlbert-László Barabási, Zoltán N. Oltvai, H. Jeong, R. Albert, E. Ravasz, G. Bianconi, E. AlmaasThe Architecture of Complexity: From the topology of the WWW to cell's genetic network2005 Norway 19 degrees of separation R. Albert et al Nature (99) based on 800 million webpages [S. Lawrence et al Nature (99)] nd.edu IBM A. Broder et al WWW9 (00) Network Measures 3 l15=2 [125] l17=4 [1346  7] … < l > = ?? 6 1 4 7 5 2 < l > = 0.35 + 2.06 log(N) < l >

  7. Network models • Random Networks • Erdös–Rényi (ER) model • Nodes in the networks are connected pair wise with equal probability. • Start with N nodes and connects each pair of nodes with probability p, which creates a graph with approximately pN(N-1)/2 randomly placed links. • degree sequence: Binomial z=np • The mean path length is proportional to the logarithm of the network size, l~logN, which indicates that it is characterized by the Small-world property • The tail of the degree distribution P(k) decreases exponentially, which indicates that nodes that significantly deviate from the average are extremely rare. • The clustering coefficient is independent of a node’s degree, so C(k) appears a horizontal line if plotted as a function of k.

  8. Network Models • Scale-free networks • Power law degree distribution; The probability that a node has k links follows P(k)~k-γ, γ=3 • Scale-free Model • Networks continuously expand by the addition of new nodes (GROWTH) • New nodes prefer to link to highly connected nodes (PREFERENTIAL ATTACHMENT) • C(k) is independent of k • Small-world property P(k)~k-γ log N γ>3 l = log log N 2<γ<3 const γ=2 (ultra small world) Cohen,Havlin, PRL’03 The probability that a node connects to a node with k links is proportional to k Mean Field Theory A.-L.Barabási, R. Albert and H. Jeong, Physica A 272, 173 (1999)

  9. Network Models • Hierarchical networks • Account for the coexistence of modularity, local clustering and scale-free topology in many real systems. • Start from a small cluster of four densely linked nodes (Ca) • Modularity • Real networks are fragmented into group or modules • Scaling of the clustering coefficient; straight line of slope -1 on a log-log plot • A hierarchical architecture implies that sparsely connected nodes are part of highly clustered areas, with communication between the different highly clustered neighborhoods being maintained by a few hubs. Ravasz, Somera, Mongru, Oltvai, A-L. B,Science 297, 1551 (2002).

  10. ReferenceAlbert-László Barabási, Zoltán N. Oltvai, H. Jeong, R. Albert, E. Ravasz, G. Bianconi, E. AlmaasThe Architecture of Complexity: From the topology of the WWW to cell's genetic network2005 Norway Scaling of the clustering coefficient C(k) Network Models The metabolism forms a hierachical network. Ravasz, Somera, Mongru, Oltvai, A-L. B,Science 297, 1551 (2002).

  11. ReferenceAlbert-László Barabási, Zoltán N. Oltvai, H. Jeong, R. Albert, E. Ravasz, G. Bianconi, E. AlmaasThe Architecture of Complexity: From the topology of the WWW to cell's genetic network2005 Norway 1. Scale-free 3. Scaling clustering coefficient (DGM) 2. Clustering coefficient independent of N Network Models Properties of hierarchical networks

  12. ReferenceThe Architecture of Complexity: From the WWW to network biology Albert-László Barabási 2004 Princeton GENOME protein-gene interactions PROTEOME protein-protein interactions METABOLISM Bio-chemical reactions Citrate Cycle

  13. Metabolic Networks Nodes: chemicals (substrates) Links: bio-chemical reactions

  14. Reference : The Architecture of Complexity: From the WWW to network biology Albert-László Barabási 2004 Princeton Protein Networks Nodes: proteins Links: physical interactions-binding C. Elegans Drosophila M. Giot et al. Science 2003 Li et al. Science 2004

  15. Reference : The Architecture of Complexity: From the WWW to network biology Albert-László Barabási 2004 Princeton Protein Networks Yeast protein interaction network H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)

  16. The origin of the scale-free topology and hubs in biological networks • Two basic mechanisms • Growth - Network emerges through the subsequent addition of new nodes • Preferential attachment - New nodes prefer to link to more connected nodes • Rich-gets-richer • Scale-free topology seems to have its origin in gene duplication • When cells divide, occasionally one or several genes are copied twice into the offspring’s genome. • The new protein has the same structure as the old one, so they both interact with the same proteins.

  17. ReferenceAlbert-László Barabási, Zoltán N. Oltvai, H. Jeong, R. Albert, E. Ravasz, G. Bianconi, E. AlmaasThe Architecture of Complexity: From the topology of the WWW to cell's genetic network2005 Norway 1 node failure S fc 0 1 Fraction of removed nodes, f Network Robustness • Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns) The size S is defined as the fraction of nodes contained in the largest cluster (that is, S = 1 for f = 0)

  18. 1 S 0 1 f Network Robustness Attacks Failures fc Albert, Jeong, Barabasi, Nature 406 378 (2000)

  19. Lethality and topological position • Yeast protein network Highly connected proteins are more essential (lethal)... Total 1571 proteins H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001) ReferenceAlbert-László Barabási, Zoltán N. Oltvai, H. Jeong, R. Albert, E. Ravasz, G. Bianconi, E. AlmaasThe Architecture of Complexity: From the topology of the WWW to cell's genetic network2005 Norway

  20. Beyond topology: characterizing the links • An ultimate description of cellular networks requires that both the intensity and the temporal aspects of the interactions are considered. • Metabolic networks • The flux of a given metabolic reaction, which represents the amount of substrate that is being converted to a product within a unit of time, offers the best measure of interaction strength. • Genetic regulatory networks • Microarray gene expression dataset • Transcription factor profiles (Direct regulatory) • Protein interaction (yeast two hybrid) • Taken together, these results indicate that the biochemical activity in both the metabolic and genetic networks is dominated by several ‘hot links’ that represent high activity interactions that are embedded into a web of less active interactions.

  21. Conclusion • Need to enhance the data collection abilities • Need to focus on the totality of interactions or snapshots of activity in various environments. • Integrated studies (metabolic, regulatory, spatial and so on) • Bottom up approach (molecules to motifs and modules) • Top down approach (network’s scale-free and hierarchical nature to the organism-specific modules and molecules) • Structure, topology, network usage, robustness and function are deeply interlinked so they must be acknowledged to complement the ‘local’ molecule-based research and address the properties of the cell as a whole

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