Chin-Wen Ho

Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology

One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.

Fault-free Hamiltonian cycles in faulty arrangement graphs

The arrangement graph A/sub n,k/, which is a generalization of the star graph (n-k=1), presents more flexibility than the star graph in adjusting the major design parameters: number of nodes, degree, and diameter. Previously, the arrangement graph has proved Hamiltonian. In this paper, we further show that the arrangement graph remains Hamiltonian even if it is faulty. Let |F/sub e/| and |F/sub v/| denote the numbers of edge faults and vertex faults, respectively. We show that A/sub n,k/ is Hamiltonian when 1) (k=2 and n-k/spl ges/4, or k/spl ges/3 and n-k/spl ges/4+[k/2]), and |F/sub e/|/spl les/k(n-k)-2, or 2) k/spl ges/2, n-k/spl ges/2+[k/2], and |F/sub e/|/spl les/k(n-k-3)-1, or 3) k/spl ges/2, n-k/spl ges/3, and |F/sub e/|/spl les/k, or 4) n-k/spl ges/3 and |F/sub v/|/spl les/n-3, or 5) n-k/spl ges/3 and |F/sub v/|+|F/sub e/|/spl les/k. Besides, for A/sub n,k/ with n-k=2, we construct a cycle of length at least 1) [n!/(n-k!)]-2 if |F/sub e/|/spl les/k-1, or 2) [n!/(n-k)!]-|F/sub v/|-2(k-1) if |F/sub v/|/spl les/k-1, or 3) [n!/(n-k)!]-|F/sub v/|-2(k-1) if |F/sub e/|+|F/sub v/|/spl les/k-1, where [n!/(n-k)!] is the number of nodes in A/sub n,k/.

Hamiltonian-laceability of star graphs

Suppose that G is a bipartite graph with its partite sets of equal size. G is said to be strongly Hamiltonian-laceable if there is a Hamiltonian path between every two vertices that belong to different partite sets and there is a path of (maximal) length N - 2 between every two vertices that belong to the same partite set, where N is the order of G. In other words, a strongly Hamiltonian-laceable graph has a longest path between every two of its vertices. In this paper, we show that the star graphs with dimension four or larger are strongly Hamiltonian-laceable.

Efficient broadcasting in wormhole-routed multicomputers: a network-partitioning approach

In this paper, a network-partitioning approach for one-to-all broadcasting on wormhole-routed networks is proposed. To broadcast a message, the scheme works in three phases. First, a number of data-distributing networks (DDNs), which can work independently, are constructed. Then the message is evenly divided into submessages, each being sent to a representative node in one DDN. Second, the submessages are broadcast on the DDNs concurrently. Finally, a number of data-collecting networks (DCNs), which can work independently too, are constructed. Then, concurrently on each DCN, the submessages are collected and combined into the original message. Our approach, especially designed for wormhole-routed networks, is conceptually similar but fundamentally very different from the traditional approach of using multiple edge-disjoint spanning trees in parallel for broadcasting in store-and-forward networks. One interesting issue is on the definition of independent DDNs and DCNs, in the sense of wormhole routing. We show how to apply this approach to tori, meshes, and hypercubes. Thorough analyses and comparisons based on different system parameters and configurations are conducted. The results do confirm the advantage of our scheme, under various system parameters and conditions, over other existing broadcasting algorithms.

Counting clique trees and computing perfect elimination schemes in parallel

Abstract In a previous result, the authors showed that a clique tree of a chordal graph can be constructed in O(log n ) parallel computing time with O( n 3 ) processors on CRCW PRAM, where n is the number of nodes of the graph. In this paper, it will be shown that this result can be extended in two ways. First, we show that from the parallel clique tree constructing algorithm, we can derive an exact formula of counting clique trees of a labeled connected chordal graph. Next, we show that a perfect elimination scheme of a chordal graph can be computed in O(log n ) time with O( n 2 ) processors on CREW PRAM once a clique tree of the graph is given. This implies that a perfect elimination scheme of a chordal graph can be computed in O(log n ) time with O( n 3 ) processors on CRCW PRAM.

cytoHubba: identifying hub objects and sub-networks from complex interactome

BackgroundNetwork is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks.ResultsWe introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network.ConclusionsCytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.

Longest fault-free paths in star graphs with edge faults

In this paper, we aim to embed longest fault-free paths in an n-dimensional star graph with edge faults. When n/spl ges/6 and there are n-3 edge faults, a longest fault-free path can be embedded between two arbitrary distinct vertices, exclusive of two exceptions in which at most two vertices are excluded. Since the star graph is regular of degree n-1, n-3 (edge faults) is maximal in the worst case. When n/spl ges/6 and there are n-4 edge faults, a longest fault-free path can be embedded between two arbitrary distinct vertices. The situation of n<6 is also discussed.

Longest fault-free paths in star graphs with vertex faults

The star graph Sn has been recognized as an attractive alternative to the hypercube. Since S1,S2, and S3 have trivial structures, we focus our attention on Sn with n⩾4 in this paper. Let Fv denote the set of faulty vertices in Sn. We show that when |Fv|⩽n−5,Sn with n⩾6 contains a fault-free path of length n!−2|Fv|−2(n!−2|Fv|−1) between arbitrary two vertices of even (odd) distance. Since Sn is bipartite with two partite sets of equal size, the path is longest for the worst-case scenario. The situation of n⩾4 and |Fv|>n−5 is also discussed.

A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles

BackgroundMany research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.ResultsIn this paper, we propose a novel hu b-attachment based method to detect functional modules from confidence-scored protein inte ractions and expression pr ofiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology.ConclusionA C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.

Graph Searching on Some Subclasses of Chordal Graphs

Abstract. In the graph-searching problem, initially a graph with all the edges contaminated is presented. The objective is to obtain a state of the graph in which all the edges are simultaneously cleared by using the least number of searchers. Two variations of the graph-searching problem are considered. One is edge searching, in which an edge is cleared by moving a searcher along this edge, and the other is node searching, in which an edge is cleared by concurrently having searchers on both of its two endpoints. We present a uniform approach to solve the above two variations on several subclasses of chordal graphs. For edge searching, we give an O(mn2) -time algorithm on split graphs (i.e., 1-starlike graphs), an O(m+n) -time algorithm on interval graphs, and an O(mnk) -time algorithm on k -starlike graphs (a generalization of split graphs), for a fixed k\geq 2 , where m and n are the numbers of edges and vertices in the input graph, respectively. There is no polynomial algorithm known previously for any of the above problems. In addition, we also show that the edge-searching problem remains NP-complete on chordal graphs. For node searching, we give an O(mnk) -time algorithm on k -starlike graphs for a fixed k \geq 1 . This result implies that the pathwidth problem on k -starlike graphs can also be solved in this time bound which greatly improves the previous results.