David Mester Ph.D.
(For more intormations see the Korol Lab. Website)
- Mathematical modeling, nonlinear and discrete optimization, optimization in distributed networks.
- Genetic and evolutionary algorithms for optimization problems (for genetic mapping and vehicle routing applications).
- Optimization NP-hard problems.
In the last years our efforts were concentrated on developing of two general approaches: (i) discrete and (ii) multi-extremal optimization for some genetic problems.
The first approach is related to several aspects of genome mapping: (1) ordering multilocus genetic maps (with hundreds and thousands marker loci), (2) assembling contigs and supercontigs in physical mapping, (3) evolutionary tree reconstruction, and (4) multilocus ordering for building consensus maps. The following multilocus consensus genetic mapping can be referenced: (4a) Multilocus genetic mapping with dominance complication; (4b) Multilocus map ordering with sexual differences in recombination; and (4c) Multilocus ordering for building consensus maps, based on mapping data generated in different genomic centers on different mapping populations. All these problems are considered as computationally very challenging, and for their solving we developed new powerful methods and fast Guided Evolution Strategy algorithms [6, 10, 11]. This algorithm proved one of the most powerful tool in discrete optimization (for the advantages of our algorithm see website www.top.sintef.no/vrp/). Using this algorithm, we successfully solved the problems refereed above as 1 [1, 10, 11, 12], 3 , 4a [1, 12] and 4b [1, 7]. The high quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was obtained by the algorithm on simulated data with ~50-1000 markers per chromosome. The developed approach and the algorithm were implemented in MultiPoint package (version 2.2, http://multiqtl.com). The developed analytical tools were applied to many mapping projects, e.g. .
Likewise, the European consortium of wheat physical mapping proposed that our group will build high quality genetic maps for their project, to anchor units of the physical map.
Korol, A.B., Mester, D., Frenkel, Z., and Ronin, Y. (2009) Methods for genetic analysis in the Triticeae. Chapt. 6 In: C. Feuillet and G. J. Muehlbauer(eds). Genetics and Genomics of the Triticeae.Springer. (Link)
Bräysy O., Dullaert W., Hasle G., Mester D., and Gendreau M. (2008) An Effective Multi-restart Deterministic Annealing Metaheuristic for the Fleet Size and Mix vehicle Routing Problem with Time Windows. Transportation Science 42(3): 371-386. (Link)
Mester D., Bräysy O., and Dulaert W. (2007) A Multi-parametric Evolution Strategies Algorithm for Vehicle Routing Problems. Expert Systems with Application32, 508-717. (Link)
Paz A., Mester D., Nevo E., and Korol A.B. (2007) Adaptation to expression level by purine-pyrimidine composition of precursor-RNAs: Looking for organization patterns of highly expressed genes. Molecular Biology and Evolution 64(2), 248-260. (Link)
Fu Y., Wen T-J., Ronin Y., Guo L., Chen D., Mester D., Yang Y., Korol A.B., Ashlock D., and Schnable P.S. (2006) Genetic Dissection of Maize Intermated. Recombinant Inbred Lines. Genetics174,1671-1683. (Link)
Mester D. and Bräysy O. (2005) Active Guided Evolution Strategies for Large Scale Vehicle Routing Problem with Time Windows. Computers & Operation Research32, 1593-1614. (Link)
Mester D., Ronin Y., Korostishevsky M., Glazman A., Pikus V., Nevo E., and Korol A.B. (2005) Multilocus consensus genetic maps (MCGM): Formulation, Algorithms, and Results. Computation Biology and Chemistry30, 12-20. (Link)
Paz A., Mester D., Baca I., Nevo A., and Korol A.B. (2004) Adaptive role of increased frequency of polypurine tracks in mRNa sequences of thermophilic prokaryotes. Proceeding of the National Academy of Science of USA 101(9), 2951-2956. (Link)
Korostishevsky M., Burd A., Mester D., Bonne'-Tamir B., Nevo E., and Korol A.B. (2004) Evolutionary Tree Reconstruction and Traveling Salesman Problem: A Powerful Algorithm for Shaggy Trees. Novosibirsk, Rossia. Bioinformatics of Genome Regulation and Structure (BGRS), 206-209. (Link)
Mester D., Ronin Y.,Korol A., and Nevo E. (2004) Fast and High Precision Algorithms for Optimization in Large Scale Genomic Problems. Computation Biology and Chemistry28, 281-290. (Link)
Mester D., Ronin Y., Minkov D., Nevo E., and Korol A.B. (2003) Constructing Large ScaleGenetic Maps Using Evolutionary Strategy Algorithm. Genetics 165, 2269-2282. (Link)
Mester D., Ronin E., Nevo E., and Korol A.B. (2003) Efficient Multipoint Mapping: making use of dominant markers repulsion-phase. Theoretical and Applied Genetics 107, 1002-1112. (Link)