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CSE891-002 Selected Topics in Bioinformatics

Jin Chen232 Plant Biology Bld.

2011 Spring

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About me…• Jin Chen, Assistant Professor in CSE and PRL from 2009

• Office: 232 Plant Biology Lab. Tel: (517) 355-5015. Email: jinchen@msu.edu

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Outline

• Course Description

• Introduction to Computational Network Biology

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Course Description• Course objectives: study interesting computational network biology

problems and their algorithms, with a focus on the principles used to design those algorithms. (3 credits)

• Instructor: Jin Chen, Office: 232 Plant Biology Bld. Email: jinchen@msu.edu

• Office hours: Thursday 2PM-3PM. If you cannot attend office hours, email me about scheduling a different time.

• Web page: http://www.msu.edu/~jinchen/cse891a

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Course Description• Course work: One 80 minutes lecture, and 80 minutes of

discussion & student presentations each week

• Grading policies: The course will be graded on attendance (10%), participation (20%), and presentation (70%).

• No Final Exam

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Course Description• Prerequisites: Graduate students in science or engineering.

Note: an override is necessary for non-CSE graduate students; please send your PID & NetID to me.

• No prior knowledge of biology is required. Computationally inclined biology graduate students are encouraged to take the class as well.

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Suggested books• A.-L. Barabási, Linked: The new science of networks

• U. Alon, An Introduction to Systems Biology

• B. Palsson. Systems Biology: Properties of Reconstructed Networks

• K. Kaneko, Life: An Introduction to Complex Systems Biology

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Course Description

Graph model Graph clustering subgraph mining

Protein-protein interaction networkGene regulatory network

Metabolic networkIntegrative study

Graph Mining

Network Biology

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Date Title Topics1/11/11 Course Organization. Introduction. Introduction to Computational Network Biology1/13/11 Protein-Protein Interaction networks I PPI network construction and false positive detection1/18/11 Student Presentation & Discussion 1

1/20/11 Protein-Protein Interaction networks II Topological analysis in PPI networks. Network motif.1/25/11 Student Presentation & Discussion 2

1/27/11 Protein-Protein Interaction networks IIIApplications of PPI network (protein function prediction, network comparison)

2/1/11 Student Presentation & Discussion 32/3/11 Gene Correlation Networks I Gene co-expression study 2/8/11 Student Presentation & Discussion 4

2/10/11 Gene Correlation Networks II Gene co-regulation study2/15/11 Student Presentation & Discussion 52/17/11 Gene Transcriptional Regulation Networks I cis-elements and gene co-regulation2/22/11 Student Presentation & Discussion 62/24/11 Gene Transcriptional Regulation Networks II Bayesian network for GRN construction3/1/11 Student Presentation & Discussion 73/3/10 Gene Transcriptional Regulation Networks III ChIP-seq and its applications in GRN construciton3/7-11 Spring Break3/15/11 Student Presentation & Discussion 83/17/11 Gene Transcriptional Regulation Networks IV GRN topological study3/22/11 Student Presentation & Discussion 93/24/11 Metabolic Networks I Flux balance analysis and metabolic control analysis3/29/11 Student Presentation & Discussion 103/31/11 Metabolic Networks II Integrative study: r-FBA model4/5/11 Student Presentation & Discussion 114/7/11 Graph Mining I Graph models

4/12/11 Student Presentation & Discussion 124/14/11 Graph Mining II Graph clustering and partitioning4/19/11 Student Presentation & Discussion 134/21/11 Graph Mining III Frequent subgraph mining4/26/11 Student Presentation & Discussion 14

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Paper list1. Chua et al. Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions.

Bioinformatics (2006) 22 (13): 1623-1630.

2. Kashani et al. Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 2009, 10:318

3. Deng et al. Prediction of Protein Function Using Protein–Protein Interaction Data. Journal of Computational Biology. December 2003, 10(6): 947-960.

4. Hu et al. Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics. Vol. 21 Suppl. 1 pp. i213–i221. 2005

5. Xu et al. Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles. ICDE 2006

6. Xu et al, Discovering cis-Regulatory RNAs in Shewanella Genomes by Support Vector Machines. PLoS Computational Biology. 5(4) 2009

7. Huang et al. Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining. Decision Support Systems. 43. 1207–1225. 2007

8. Honkela et al. Model-based method for transcription factor target identification with limited data. PNAS vol 107(17) pp. 7793–7798. 2009

9. Vermeirssen et al. Transcription factor modularity in a Gene-Centered C. elegans Protein-DNA interaction network. Genome Research 17, 061-1071. 2007

10. Covert et al, Transcriptional Regulation in Constraints-Based Metabolic Models of Escherichia coli, Journal of Biological Chemistry, 277(31): pp. 28058-28064. 2002

11. Herrgard et al. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Research. 16:627–635. 2006

12. Barabási et al. Network Biology: Understanding the Cell's Functional Organization. Nature Reviews Genetics 5, 101-113. 2004

13. Dongen. A cluster algorithm for graphs. Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000

14. Huan et al. Mining Family Specific Residue Packing Patterns from Protein Structure Graphs, RECOMB, pp. 308-315, 2004

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Course Description• Select at least one paper for presentation from the paper list.

Email me which paper you will present by next Mon (1/17/2011)

• Each presentation is 45 min, including 15 min Q&A, followed with a discussion

• Your grade will be largely determined by the presentation (70%)

• Presentation starts from next Tue (1/18/2011)

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Important Days:

Class Begins 1/10/2011 Open adds end 1/14/2011Last day to drop with refund 2/3/2011 Last day to drop with no grade reported 3/2/2011 Class Ends 5/6/2011

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Introduction to Computational Network Biology

• Network biology belongs to systems biology, which belongs to genomics

• Interested in the relations between entities rather than the entities themselves

http://bionet.bioapps.biozentrum.uni-wuerzburg.de/

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Network’s everywhere• Internet, social network, anti-terrorism network

• Biological networks – Protein-protein interaction (PPI) network– protein-DNA interaction network– gene correlation network– gene regulatory network– metabolic network– signaling network…

• Network is a tool for under standing complex systems

• Network models explains network properties and support network behavior study

• Network measures provide quantitative analysis for complex systems

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Definition of network (graph)

Node (vertex)

G(V,E)

Self-loop

EdgeMulti-set of edges

Simple graph: does not have loops (self-edges) and does not have multi-edges.

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Definition of network (graph)

Directed graphvs.Undirected graph

Labeled graphvs.Unlabeled graph

Symmetric graphvs.Asymmetric graph

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Webpage layout

M. Newman and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 2004

Pages on a web site and the hyperlinks between them

18Adopted from R Albert’s slides

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Biological networks

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Hawoong Jeong

Yeast Protein-Protein Interaction network

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Eric Davidson

Gene regulation network of sea urchin

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Abhishek Murarka

Metabolic flux analysis of E. coli

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Why study networks?• Complex systems cannot be described in a reductionist view

• Behavior study of complex systems starts with understanding the network topology

• Network - related questions:– How do we reconstruct a network?– How can we quantitatively describe large networks?– How did networks get to be the way they are?

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Simple measures• Node Degree: the number of edges connected to the node

– In-degree & Out-degree– Total in-degree == total out-degree

• Average Degree: the average of node degrees for all the nodes in the network, denoted as:

• Degree distribution: the degree distribution P(k) gives the fraction of nodes that have k edges

where N is the number of nodes in the network, ki is the node degree of node i

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Simple measures• Shortest path: to find a path between two nodes such that the

sum of the weights of its constituent edges is minimized

• Graph diameter: the longest shortest path between any pair of nodes in the graph.

• Connected graph: any two vertices can be joined by a path

• Bridge: if we erase the edge, the graph becomes disconnected

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Simple measures• Betweenness centrality: for all node pairs (i, j), find all the shortest paths between

nodes i and j, denoted as C(i,j), and determine how many of these pass through node k, denoted as Ck(i,j). Betweenness centrality of node k is

• Calculating the betweenness involves calculating the shortest paths between all pairs of vertices on a graph. O(V2logV + VE) for sparse graph with Johnson’s algorithm.

L. C. Freeman, Sociometry 40, 35 (1977); P. E. Black, Dictionary of Algorithms and Data Structures (2004)

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Complex measures

• Frequent subgraph mining

• Graph comparison & classification

• Graph isomorphic testing

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Useful software

• Visualization & Topological Analysis– Cytoscape (www.cytoscape.org)– Pajek (vlado.fmf.uni-lj.si/pub/networks/pajek)

• Graph related programming– LEDA (www.algorithmic-solutions.com)– Nauty

(www.cs.sunysb.edu/~algorith/implement/nauty/implement.shtml)

1960 1999 2002

Real networks are much more complex

• Transcription regulatory networks of Yeast and E. coli show an interesting example of mixed characteristics– how many genes a TF interacts with – how many TFs interact with a given gene

- scale-free- exponential

Modularity and network motif• Cellular function are likely to be carried out in a highly

modular manner

• Modular -- a group of genes/proteins that work together to achieve distinct functions

• Biology is full of examples of modularity

Remaining challenges• Discovery of network motifs is closely related to the

generation of random networks

• Structure of network motifs does not necessary determine function

• Relation between higher-level organizational, functional states and networks has not yet been studied

Voigt, W. et al. Genetics 2005 Ingram P.J.et al. BMC Genomics 2006

Eric Werner. Nature 2007

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Next class

• PPI network construction

• False-positive detection