TY - CHAP
T1 - Computational Grand Challenges in Assembling the Tree of Life
T2 - Problems and Solutions
AU - Bader, David A.
AU - Roshan, Usman
AU - Stamatakis, Alexandros
N1 - Funding Information:
Bader's research discussed in this chapter has been supported in part by NSF Grants CAREER CCF-0611589, ACI-00-93039, CCF-0611589, DBI-0420513, ITR ACI-00-81404, ITR EIA-01-21377, Biocomplexity DEB-01-20709, and ITR EF/BIO 03-31654; and DARPA Contract NBCH30390004.
PY - 2006
Y1 - 2006
N2 - The computation of ever larger as well as more accurate phylogenetic (evolutionary) trees with the ultimate goal to compute the tree of life represents one of the grand challenges in High Performance Computing (HPC) Bioinformatics. Unfortunately, the size of trees which can be computed in reasonable time based on elaborate evolutionary models is limited by the severe computational cost inherent to these methods. There exist two orthogonal research directions to overcome this challenging computational burden: First, the development of novel, faster, and more accurate heuristic algorithms and second, the application of high performance computing techniques. The goal of this chapter is to provide a comprehensive introduction to the field of computational evolutionary biology to an audience with computing background, interested in participating in research and/or commercial applications of this field. Moreover, we will cover leading-edge technical and algorithmic developments in the field and discuss open problems and potential solutions.
AB - The computation of ever larger as well as more accurate phylogenetic (evolutionary) trees with the ultimate goal to compute the tree of life represents one of the grand challenges in High Performance Computing (HPC) Bioinformatics. Unfortunately, the size of trees which can be computed in reasonable time based on elaborate evolutionary models is limited by the severe computational cost inherent to these methods. There exist two orthogonal research directions to overcome this challenging computational burden: First, the development of novel, faster, and more accurate heuristic algorithms and second, the application of high performance computing techniques. The goal of this chapter is to provide a comprehensive introduction to the field of computational evolutionary biology to an audience with computing background, interested in participating in research and/or commercial applications of this field. Moreover, we will cover leading-edge technical and algorithmic developments in the field and discuss open problems and potential solutions.
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U2 - 10.1016/S0065-2458(06)68004-2
DO - 10.1016/S0065-2458(06)68004-2
M3 - Chapter
AN - SCOPUS:33751114437
SN - 0120121689
SN - 9780120121687
T3 - Advances in Computers
SP - 127
EP - 176
BT - Computational Biology and Bioinformatics
A2 - Tseng, Chau-Wen
ER -