Conference Paper

Homomorphisms of Multisource Trees into Networks with Applications to Metabolic Pathways

Georgia State Univ., Atlanta
DOI: 10.1109/BIBE.2007.4375587 Conference: Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on
Source: IEEE Xplore


Network mapping is a convenient tool for comparing and exploring biological networks; it can be used for predicting unknown pathways, fast and meaningful searching of databases, and potentially establishing evolutionary relations. Unfortunately, existing tools for mapping paths into general networks (PathBlast) or trees into tree networks allowing gaps (MetaPathwayHunter) cannot handle large query pathways or complex networks. In this paper we consider homomorphisms, i.e., mappings allowing to map different enzymes from the query pathway into the same enzyme from the networks. Homomorphisms are more general than homeomorphism (allowing gaps) and easier to handle algorithmically. Our dynamic programming algorithm efficiently finds the minimum cost homomorphism from a multisource tree to directed acyclic graphs as well as general networks. We have performed pairwise mapping of all pathways for four organisms (E. coli, S. cerevisiae, B. subtilis and T. thermophilus species) and found a reasonably large set of statistically significant pathway similarities. Further analysis of our mappings identifies conserved pathways across examined species and indicates potential pathway holes in existing pathway descriptions.

Download full-text


Available from: A. Zelikovsky
  • Source
    • "Their graph matching allows to delete disassociated vertices or induced subnetwork in query network and then aligns its remainder to target network by subgraph isomorphism. Papers [6] [7] consider network alignment of metabolic pathways without vertex deletion. A polynomial-time algorithm for mapping tree pattern into an arbitrary text is proposed. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Abstract Network alignments,are extensively used for compar- ing, exploring, and predicting biological networks. Exist- ing alignment tools are mostly based on isomorphic and homeomorphic,embedding,and require solving a problem that is NP-complete even when searching a match for a tree in acyclic networks. On the other hand, if the mapping of different nodes from the query network (pattern) into the same node from the text network is allowed, then trees can be optimally mapped,into arbitrary networks in polynomial time. In this paper we present the first polynomial-time algo- rithm for finding the best matching pair consisting of a sub- tree in a given tree pattern and a subgraph,in a given text (represented by an arbitrary network) when both insertions and deletions of degree-2 vertices are allowed on any path. Our dynamic programming,algorithm is an order of magni- tude faster than the previous network alignment algorithm when deletions are forbidden. The algorithm has been also generalized to pattern networks with cycles: with a modest increase in runtime it can handle patterns with the limited vertex feedback set. We have applied our algorithm to matching,metabolic pathways of four organisms (E. coli, S. cerevisiae, B. sub- tilis and T. thermophilus species) and found a reasonably large set of statistically significant alignments. We show advantages,of allowing pattern vertex deletions and give an example validating biological relevance of the pathway alignment.
    Full-text · Conference Paper · Jan 2008
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The network-mapping tool integrated with protein database search can be used for filling pathway holes. A metabolic pathway under consideration (pattern) is mapped into a known metabolic pathway (text), to find pathway holes. Enzymes that do not show up in the pattern may be a hole in the pattern pathway or an indication of alternative pattern pathway. We present a data-mining framework for filling holes in the pattern metabolic pathway based on protein function, prosite scan and protein sequence homology. Using this framework we suggest several fillings found with the same EC notation, with group neighbors (enzymes with same EC number in first three positions, different in the fourth position), and instances where the function of an enzyme has been taken up by the left or right neighboring enzyme in the pathway. The percentile scores are better when closely related organisms are mapped as compared to mapping distantly related organisms.
    Preview · Article ·
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Network mappings has been commonly applied on it to detect con- served subnetworks across species in biological network. Our previous work fo- cused on finding best mapping of pattern graph to text graph in which vertices in the image of pattern graph are allowed to be deleted (1),(2). Arising from the requirement of allowing deleting pattern vertices in mapping, we reformulate the graph comparison problem to be the one of a homo-home morphism mapping among arbitrary graphs and further analyze and compare our work with previous ones.
    Full-text · Article ·
Show more

We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.