In order to prove that the problem is NP-hard, we provide a reduction from the Minimum Chromatic Number (Min-CN) problem, in its natural decision version: Given a graph ) and an integer k, does there exist a proper vertex coloring of G C using at most k colors ? This problem has been shown to be NP-hard for ,
D is obtained from G C by replacing each edge by two arcs in opposite directions, and by adding an arc from any vertex to v r , while G is a star whose center is v r . We now prove the following property: there exists a proper coloring for G C using k colors iff there exists a valid cardinality k cover C = {V 1 , V 2 . . . V k } of V . (?) Given a proper coloring of G C with k colors, let S i , 1 ? i ? k, be the set of vertices assigned color k. We compute a cardinality k cover C = {V 1 , V 2 . . . V k } of V as follows: for any 1 ? i ? k, V i = S i ? {v r }. By definition of a proper coloring, each S i is an independent set in G C (thus in D). Hence, since the outdegree of v r is equal to zero, any D[V i ], 1 ? i ? k, is a DAG. Moreover, any G[V i ] is indeed connected due to the fact that (i) G is star whose center is v r , and (ii) every V i , 1 ? i ? k, contains v r . Hence, C is valid ,
The reduction provided in proof of Proposition 9 is actually an L-reduction, since the sizes of the solutions in the two problems are equal (we have indeed proved, following our reduction, that " there exists a proper coloring for G C using k colors iff there exists a valid cardinality k cover C = {V 1 , V 2 . . . V k } of V " ). Hence, given any approximation algorithm for Min-DAGCC-Cover, one can derive an algorithm for Min-CN, with the same approximation ratio ,
Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data, Bioinformatics, vol.21, issue.23, pp.4209-4215, 2005. ,
DOI : 10.1093/bioinformatics/bti711
Multiple Alignment of Biological Networks: A Flexible Approach, Proc. 20th Annual Symposium on Combinatorial Pattern Matching (CPM), pp.263-273, 2009. ,
DOI : 10.1016/S0962-8924(00)01902-4
The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles, BMC Systems Biology, vol.2, issue.1, 2008. ,
DOI : 10.1186/1752-0509-2-100
A patternguided approach to compare heterogeneous networks, 2011. ,
Graemlin: General and robust alignment of multiple large interaction networks, Genome Research, vol.16, issue.9, pp.1169-1181, 2006. ,
DOI : 10.1101/gr.5235706
Improved Orientations of Physical Networks, Proc. 10th Workshop on Algorithms in Bioinformatics, pp.215-225, 2010. ,
DOI : 10.1007/978-3-642-15294-8_18
Computers and Intractability: A guide to the theory of NP-completeness, 1979. ,
Discovering pathways by orienting edges in protein interaction networks, Nucleic Acids Research, vol.39, issue.4, p.22, 2011. ,
DOI : 10.1093/nar/gkq1207
A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling, Journal of Theoretical Biology, vol.252, issue.3, pp.456-464, 2008. ,
DOI : 10.1016/j.jtbi.2007.09.042
Conserved pathways within bacteria and yeast as revealed by global protein network alignment, Proceedings of the National Academy of Sciences, vol.100, issue.20, pp.11394-11399, 2003. ,
DOI : 10.1073/pnas.1534710100
PathBLAST: a tool for alignment of protein interaction networks, Nucleic Acids Research, vol.32, issue.Web Server, p.32, 2004. ,
DOI : 10.1093/nar/gkh411
Topological network alignment uncovers biological function and phylogeny, Journal of The Royal Society Interface, vol.25, issue.12, pp.1341-1354, 2010. ,
DOI : 10.1093/bioinformatics/btp196
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894889
A Probabilistic Functional Network of Yeast Genes, Science, vol.306, issue.5701, pp.1555-1558, 2004. ,
DOI : 10.1126/science.1099511
An Algorithm for Orienting Graphs Based on Cause-Effect Pairs and Its Applications to Orienting Protein Networks, Proc. 8th Workshop on Algorithms in Bioinformatics, pp.222-232, 2008. ,
DOI : 10.1007/978-3-540-87361-7_19
Comparing Protein Interaction Networks via a Graph Match-and-Split Algorithm, Journal of Computational Biology, vol.14, issue.7, pp.892-907, 2007. ,
DOI : 10.1089/cmb.2007.0025
Evidence against the selfish operon theory, Trends in Genetics, vol.20, issue.6, pp.232-234, 2004. ,
DOI : 10.1016/j.tig.2004.04.001
Optimization, approximation, and complexity classes, Journal of Computer and System Sciences, vol.43, issue.3, pp.425-440, 1991. ,
DOI : 10.1016/0022-0000(91)90023-X
Alignment of metabolic pathways, Bioinformatics, vol.21, issue.16, pp.3401-3408, 2005. ,
DOI : 10.1093/bioinformatics/bti554
Homology, pathway distance and chromosomal localisation of the small molecule metabolism enzymes in Escherichia coli ,
Modeling cellular machinery through biological network comparison, Nature Biotechnology, vol.42, issue.4, pp.427-433, 2006. ,
DOI : 10.1038/nbt1196
PAIRWISE ALIGNMENT OF INTERACTION NETWORKS BY FAST IDENTIFICATION OF MAXIMAL CONSERVED PATTERNS, Biocomputing 2009, pp.99-110, 2009. ,
DOI : 10.1142/9789812836939_0010
Computational Identification of Operons in Microbial Genomes, Genome Research, vol.12, issue.8, pp.1221-1230, 2002. ,
DOI : 10.1101/gr.200602
Linear degree extractors and the inapproximability of max clique and chromatic number, Proceedings of the thirty-eighth annual ACM symposium on Theory of computing , STOC '06, pp.103-128, 2007. ,
DOI : 10.1145/1132516.1132612