Bulletin of Mathematical Biology
, Volume 49, Issue 4, pp 461467
Computational complexity of inferring phylogenies from dissimilarity matrices
 William H. E. DayAffiliated withDepartment of Computer Science, Memorial University of Newfoundland
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Molecular biologists strive to infer evolutionary relationships from quantitative macromolecular comparisons obtained by immunological, DNA hybridization, electrophoretic or amino acid sequencing techniques. The problem is to find unrooted phylogenies that best approximate a given dissimilarity matrix according to a goodnessoffit measure, for example the leastsquaresfit criterion or Farris'sf statistic. Computational costs of known algorithms guaranteeing optimal solutions to these problems increase exponentially with problem size; practical computational considerations limit the algorithms to analyzing small problems. It is established here that problems of phylogenetic inference based on the leastsquaresfit criterion and thef statistic are NPcomplete and thus are so difficult computationally that efficient optimal algorithms are unlikely to exist for them.
 Title
 Computational complexity of inferring phylogenies from dissimilarity matrices
 Journal

Bulletin of Mathematical Biology
Volume 49, Issue 4 , pp 461467
 Cover Date
 198707
 DOI
 10.1007/BF02458863
 Print ISSN
 00928240
 Online ISSN
 15229602
 Publisher
 Kluwer Academic Publishers
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 Authors

 William H. E. Day ^{(1)}
 Author Affiliations

 1. Department of Computer Science, Memorial University of Newfoundland, A1C 5S7, St. John's, Newfoundland, Canada