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Toward Automatically Drawn Metabolic Pathway Atlas with Peripheral Node Abstraction Algorithm. Myungha Jang, Arang Rhie , and Hyun- Seok Park * Bioinformatics Laboratory, School of Engineering Ewha Womans University Seoul, Korea. IEEE BIBM, 18-21 Dec 2010, Hong Kong. - PowerPoint PPT Presentation
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Toward Automatically Drawn Metabolic Pathway Atlas with Peripheral Node Abstraction Algorithm
Myungha Jang, Arang Rhie, and Hyun-Seok Park*
Bioinformatics Laboratory, School of EngineeringEwha Womans University
Seoul, Korea
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
Table of Contents
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
I. Introduction
II. Topological Nature of Metabolic Networks at Peripheral Nodes
III. Node Abstraction Featured Scale-free Algorithm
IV. Experimental Results
V. Discussion and Future Work
• Abstract graph structure ⇒ visual representation
• Graphical diagrams are intuitively helpful to understand
biochemical reaction networks
- Node : compound, Edge : reactions
•Optimal solutions : NP-hard problems
Automatic graph layout algorithms in systems biology
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
I. INTRODUCTION
• A complete metabolic network indicates all the metabolic potential and capacity. • The shift of research focus: single pathways to multiple pathways. • Visualization serves an important role in understanding large scale metabolic
network.• KEGG Atlas(http://www.genome.ad.jp/kegg), 2008 • Terms : Global (metabolic) pathway, Multiple pathway, Atlas
Focusing on Global Metabolic Pathway
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
I. INTRODUCTION
Our Efforts Toward Automatic Global Layout
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
I. INTRODUCTION
• Not enough to deal with the global pathway!• How can we obtain a complete view? • No attempts for automatic visualization for Atlas
Related work: KEGG Atlas • The map integration process is carried out manually by curators. • Based on curator’s experience • However, that metabolic networks are dynamic in nature should not be
disregarded Systematic approach is necessary
How To Deal With Large-scale Metabolic Pathway?I. INTRODUCTION
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
How To Deal With Large-scale Metabolic Pathway? (con’d)INTRODUCTION
Our Strategy
We provide a novel algorithmic approach in drawing multiple metabolic pathways by considering two properties:
1. Automatic abstraction criteria: by analyzing a topological nature of metabolic networks based on the graphical property of relation distance, linear reactions were abstracted as a unit reaction.
2. the consistency of highly connected nodes
• We obtained 255 map data by parsing KEGG XML (KGML) documents of version 0.6 using our KGML Parser.
Two terms were defined: 1. Relation degree the number of edges branching from a node 2. Relation distance a factor to measure the length between any two compounds encompassing nodes which all have relation degrees less than or equal to p (p = 2)
KGML
+
• A dedicated analysis on peripheral nodes with low connectivity was performed.
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
II. TOPOLOGICAL NATURE OF METABOLIC NETWORKS AT PERIPHERAL NODES
Relation Distance Term Clarification
II. TOPOLOGICAL NATURE OF METABOLIC NETWORKS AT PERIPHERAL NODES
• Definition: The length between any two compounds encompassing nodes which all have relation degrees equal to p • Here, p = 2
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
Relation Distance Example in Map
II. TOPOLOGICAL NATURE OF METABOLIC NETWORKS AT PERIPHERAL NODES
cpd:C01291
cpd:C01290
cpd:C16475
cpd:C16466
cpd:C16470
cpd:C16468
cpd:C16469
cpd:C16471
cpd:C00369
RD(C01290, C00369) = 7
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
Layout Components according to High Connectivity
Basic Motivation
• Observation: 66.83% of the total compounds within the complete metabolic pathways were of low connectivity, with less than relation degree of 3.• The number of compounds with higher relation degree, i.e. more than 6 edges, was much less.
Abstracting Compounds With Linear Interaction
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
III. NODE ABSTRACTION FEATURED SCALE-FREE ALGORITHM
A. Abstracting Compounds With Linear Interaction
• We abstracted and hid all those compounds that appear within these linear interactions. • This approach could be called “chain reduction”(M. Chimani et al) • All green compounds in the figure will be hidden in the graph layout according to this approach.
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
III. NODE ABSTRACTION FEATURED SCALE-FREE ALGORITHM
B. Layout Components according to High Connectivity
Input : Metabolic Pathway Graph
Output : coordinates of each node
void LayoutPathway (Pathway graph)
{
IF highly connected nodes (Nd) exist in graph
LayoutHighlyConnectedNode (graph, Nd);
ELSE IF any cycle(Nc) exists in graph
AND size of cycle ≥ 6
LayoutCircular (graph, Nc);
ELSE LayoutHierarchic (graph);
}
• Highly Connected Nodes: Nodes with relation degree bigger than 6
• LayoutHighConnectedNode() Algorithm Steps1. Find a highly Connected node Nd
2. Each component connected to Nd is decomposed into sub-graph
3. Each decomposed sub-graph is treated as a super node to apply the spring-embedding algorithm
63
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
III. NODE ABSTRACTION FEATURED SCALE-FREE ALGORITHM
IV. EXPERIMENTAL RESULTS
Experiments : To compare compression rate of compounds, we obtained the number of
abstracted compounds and edge crossings by applying two different layout algorithms:
Result 1
• Node compression rate performance
• Scope
1. 84 single metabolic pathways
2. 8 major categorized metabolic pathways
3. the global pathway
Result 2
• The number of edge crossing comparison between by
1. Conventional algorithm
2. Our Node abstraction featured scale-free layout algorithm
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
…
single pathways
… Categorized pathways
Globalpathway
III. EXPERIMENTAL RESULTS
Pathway Number of Nodes Before Abstraction
Number of Nodes After Abstraction Abstraction Rate
Carbohydrate Metabolism 1235 972 21.2%
Lipid Metabolism 1043 805 22.8%
Nucleotide Metabolism 424 351 17.21%
Amino Acid Metabolism 1327 980 26.14%
Metabolism of Other Amino Acid 332 262 21.08%
Metabolism of Cofactor and Vitamins
250 175 30%
Biosynthesis of Secondary Metabolism
800 536 33%
Xenobiotics Biodegradation 542 348 35.79%
Global Pathway (Atlas) 5675 4371 22.98%
Result 1B
The Number of Nodes Before and After Applying Node Abstraction
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
Peripheral path as supplementary nodes
III. EXPERIMENTAL RESULTS
Results drawn with Cytoscape, using conventional spring embeddingThe red-colored edges represent the abstracted edges. (abstraction rate : 70%)
Result 1A
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
Original Network Abstracted Network
Peripheral path as super edges
III. EXPERIMENTAL RESULTS
• In single metabolic pathways, the node abstraction featured algorithm
reduced edge crossings by 63.31%.
• In a global metabolic pathway, the number of edge crossings has reached a
reduction of 58.08% in total.
• Our proposed algorithm with node abstraction resulted in 86,067 edge
crossings, whereas the one without node abstraction resulted in 205,316 edge
crossings.
Result 2 : Edge Crossing Reduction
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
IV. DISCUSSION
• Two approaches were used: 1. Abstracting compound pairs according to a consistent criteria 2. Layout components according to high connectivity
• Our experimental results show that node abstraction feature reduced the number of compounds by approximately 23% in global pathway.
• Further discussion is necessary regarding enzyme reactions
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University
IV. WHY IS OUR WORK IMPORTANT?
• The first systematic approach for Atlas visualization focusing on peripheral nodes
• Fundamental to building a hierarchical structure of Atlas
• Our approach is flexible upon pathway database change that frequently updates
• It is a crucial preliminary step toward automatically drawn metabolic pathway
• Future research on individual biological meaning of each peripheral nodes and abstracted path
IEEE BIBM, 18-21 Dec 2010, Hong Kong Ewha Womans University