1. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network Biology Speaker: Peng-An Chen Date: 2007/01/29

2. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Reference AL Barabasi, ZN Oltvai. Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics, 2004

3. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Outline Introduction Network measure & Network model Some topological feature Motifs & Modules

4. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Motivation Most biological characteristics arise from complex interactions between the cell’s numerous constituents Understand the structure and function of a living cell

5. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan From DNA to life

6. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Biology Technology How do we measure protein interaction? –Protein chips –Yeast two-hybrid screens

7. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network measures Degree Degree distribution Mean path length –the average over the shortest paths between all pairs of nodes Clustering coefficient –C = 2n/k(k–1) –n is the number of links connecting the k neighbors of node I to each other

8. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Metabolism as network

9. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Random network By Erdos-Renyi Connect each pair of node with prob p Expect value of edge is pN(N-1)/2 Poisson distribution –The node with high degree is rare

10. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Scale-free network Power-law degree distribution Hubs and nodes When a node add into network, it prefer to link to hubs

11. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Cellular networks are scale-free In metabolism –most metabolic substrates participate in only one or two reactions (node) –A few coenzyme participate in dozens reactions (hub) PPI in diverse eukaryotic species also have features of a scale-free network

12. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Scale-free hypothesis Some people don’t think PPI network is scale-free because –There are many false-positive link in PPI network –The data in PPI network is not complete

13. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan

14. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Small-world effect In complex networks, any two nodes can be connected with a path of few links This short path length indicates that local perturbations in metabolite concentrations could reach the whole network very quickly

15. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Disassortativity Hubs avoid linking directly to each other and instead connect to proteins with only a few interactions

16. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Evolutionary explain Preferential attachment –Nodes prefer to connect to nodes that already have many links Gene duplication

17. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Motifs and Modules Examples of modularity in biology –RNA complexes are at the core of many basic biological functions Motif –Subgraphs which are overrepresented as compared to a randomized version of the same network

18. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Functional Module The average clustering coefficient of most real networks is significantly larger that that of a random network Motif clusters –The motifs that occur in a given networks are not independent of each other –Motifs tend to aggregation into cluster

19. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Identify function module Breakdown the cellular network into a set of function modules The network is as likely to be partitioned into a set of cluster of 10-20 nodes The existence of relatively isolated modules is rare

20. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Network robustness The system’s ability to respond to changes in the external conditions or internal organization while maintaining relatively normal behavior –Topological robustness –Functional and dynamical robustness

21. Algorithms and Computational Biology Lab, Department of Computer Science and & Information Engineering, National Taiwan University, Taiwan Thanks!