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, Dec 2010, Hong Kong Ewha Womans University

Table of Contents IEEE BIBM, 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, 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( 2008http:// Terms : Global (metabolic) pathway, Multiple pathway, Atlas Focusing on Global Metabolic Pathway IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University I. INTRODUCTION

Our Efforts Toward Automatic Global Layout IEEE BIBM, 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, 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) KG ML + A dedicated analysis on peripheral nodes with low connectivity was performed. IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University II. T OPOLOGICAL N ATURE OF M ETABOLIC N ETWORKS AT P ERIPHERAL N ODES

Relation Distance Term Clarification II. T OPOLOGICAL N ATURE OF M ETABOLIC N ETWORKS AT P ERIPHERAL N ODES Definition: The length between any two compounds encompassing nodes which all have relation degrees equal to p Here, p = 2 IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University

Relation Distance Example in Map II. T OPOLOGICAL N ATURE OF M ETABOLIC N ETWORKS AT P ERIPHERAL N ODES cpd:C01291 cpd:C01290 cpd:C16475 cpd:C16466 cpd:C16470 cpd:C16468 cpd:C16469 cpd:C16471 cpd:C00369 RD(C01290, C00369) = 7 IEEE BIBM, 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, Dec 2010, Hong Kong Ewha Womans University III. N ODE A BSTRACTION F EATURED S CALE - FREE A LGORITHM

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, Dec 2010, Hong Kong Ewha Womans University III. N ODE A BSTRACTION F EATURED S CALE - FREE A LGORITHM

B. Layout Components according to High Connectivity Input : Metabolic Pathway Graph Output : coordinates of each node void LayoutPathway (Pathway graph) { IF highly connected nodes (N d ) exist in graph LayoutHighlyConnectedNode (graph, N d ); ELSE IF any cycle(N c ) exists in graph AND size of cycle ≥ 6 LayoutCircular (graph, N c ); ELSE LayoutHierarchic (graph); } Highly Connected Nodes: Nodes with relation degree bigger than 6 LayoutHighConnectedNode() Algorithm Steps 1.Find a highly Connected node N d 2.Each component connected to N d is decomposed into sub-graph 3.Each decomposed sub-graph is treated as a super node to apply the spring-embedding algorithm 6 3 IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University III. N ODE A BSTRACTION F EATURED S CALE - FREE A LGORITHM

IV. E XPERIMENTAL 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 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, Dec 2010, Hong Kong Ewha Womans University … single pathways … Categorized pathways Global pathway

III. E XPERIMENTAL RESULTS Pathway Number of Nodes Before Abstraction Number of Nodes After Abstraction Abstraction Rate Carbohydrate Metabolism % Lipid Metabolism % Nucleotide Metabolism % Amino Acid Metabolism % Metabolism of Other Amino Acid % Metabolism of Cofactor and Vitamins % Biosynthesis of Secondary Metabolism % Xenobiotics Biodegradation % Global Pathway (Atlas) % Result 1B The Number of Nodes Before and After Applying Node Abstraction IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University Peripheral path as supplementary nodes

III. E XPERIMENTAL RESULTS Results drawn with Cytoscape, using conventional spring embedding The red-colored edges represent the abstracted edges. (abstraction rate : 70%) Result 1A IEEE BIBM, Dec 2010, Hong Kong Ewha Womans University Original Network Abstracted Network Peripheral path as super edges

III. E XPERIMENTAL 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, Dec 2010, Hong Kong Ewha Womans University

IV. D ISCUSSION 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, Dec 2010, Hong Kong Ewha Womans University

IV. W HY 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, Dec 2010, Hong Kong Ewha Womans University