Publications

L. M. Himmelmann (2001) Networkexternalities, Diploma Thesis, University Mannheim, Supervisor Prof. Dr. Conrad (german). PDF

L. M. Himmelmann, D. Metzler (2007) A Study on the Empirical Support for Prior Distributions on Phylogenetic Tree Topologies in: C. Falter, A. Schliep, J. Selbig, M. Vingron, D. Walther (Eds.) Proceedings of the German Conference on Bioinformatics, September 26-28, 2007, Potsdam, Germany, Lecture Notes in Informatics - Series of the Gesellschaft für Informatik (GI) 115, 101-110. PDF, Supplementary Material

Tree reconstruction and dating of Tudorella, in cooperation with PD Dr. Markus Pfenninger, University Frankfurt, coming soon.

Tree reconstruction and dating of Lizards, in cooperation with Dr. Johannes Müller, Humbold-University Berlin (Museum of Natural History), coming soon.

Talks

Prior Distributions on Phylogenetic Tree Topologies, GCB 2007, Potsdam. PDF

Monopolistic Competition. PDF

Evolutionary Algorithmns and evolutionary Strategies. PDF

Approximation with Ridge-Functions, sigmoidal Functions und Neuronal Networks. PDF

Seminar Lectures

In the course of a research assistant at the Goethe University Frankfurt, I supervised the following Seminar Lectures from 2004 until 2007:

Microarrays

Analysis of microarray gene expression data. Huber, W., von Heydenbreck, A., and Vingron, M. (2003b). Number 1/3 in 2, Chichester, UK.

Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Huber, W., von Heydenbreck, A., S¨ultmann, H., Poustka, A., and Vingron, M. (2002). Bioinformatics, 18(1):96–104.

Parameter estimation for the calibration and variance stabilization of microarray data. Huber, W., von Heydenbreck, A., Sueltmann, H., Poustka, A., and Vingron, M. (2003a). Statistical Applications in Genetics and Molecular Biology, 2(1). Art. 3.

Sample size determination in microarray experiments for class comparison and prognostic classification; Biostatistics 6(1) S.27-38; Dobbin, Kevin, Simon, Richard; 2005.

Power and sample size for DNA microarray studies; Statistics in medicine 21(23) S.3543-3570; Lee, Mei-Ling Ting,Whitmore, G. A.; 2002.

RNA Structure Prediction

Pseudoknots in rna secondary structures. Lyngs, R. and Pedersen, C. (2000).

Predicting RNA secondary structures with arbitrary pseudoknots by maximizing the number of stacking pairs; Journal of computational biology 10(6) S.981-995; Ieong, Samuel,Kao, Ming-Yang,Lam, Tak-Wah,Sung, Wing-Kin,Yiu, Siu-Ming; 2003.

The language of RNA: a formal grammar that include pseudoknots; Bioinformatics 16(4) S.334-340; Eddy, Sean R.,Rivas, Elena; 2000.

Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics; BMC Bioinformatics 5(104) Giegerich, Robert,Reeder, Jens; 2004.

A dynamic programming algorithm for RNA structure prediction including pseudoknots; Journal of molecular biology 285S.2053-2068; Eddy, Sean R.,Rivas, Elena; 1999.

A probabilistic model for the evolution of RNA structure; BMC Bioinformatics 5(166) Holmes, Ian; 2004.

RNA secondary structure prediction with simple pseudoknots; Proceedings of the second conference on asia-pacific bioinformatics 29S.239 - 246; Deogun, Jitender S.,Donis, Ruben,Komina, Olga,Ma, Fangrui; 2004.

Co-transcriptional folding is encoded within RNA genes; BMC Molecular Biology 5(10) Meyer, Irmtraud M.,Miklos, Istvan; 2004.

Moments of boltzmann distribution for RNA secondary structures; Bulletin of Mathematical Biology Meyer, Irmtraud M.,Miklos, Istvan,Nagy, Borbala; 2005.

A graph theoretical approach to predict common RNA secondary sructure motifs including pseudoknots in unaligned sequences; Bioinformatics 20(10) S.1591-1602; Ji, Yongmei,Stormo, Gary D.,Xu, Xing; 2004.

An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots; Bioinformatics 20(1) S.58-66; Ruan, Jianhua,Stormo, Gary D.,Zhang, Weixiong; 2004.

Stochastic modeling of RNA pseudoknotted structures: a grammatical approach; Bioinformatics 19(1) S.i66-i73; Cai, Liming,Malmberg, Russell L.,Wu, Yunzhou; 2003.

A statistical sampling algorithm for RNA secondary structure prediction; Nucleic Acids Research 31(24) S.7280-7301; Ding, Ye,Lawrence, Charles E.; 2003.

Statistical evidence for conserved, local secondary structure in the coding regions of eukaryotic mRNAs and pre-mRNAs. Meyer, I.M. & Miklós, I. Nucleic Acids Res, 2005, 33, 6338-6348

A grammatical theory for the conformational changes of simple helix bundles. Chiang, D.; Joshi, A.K. & Dill, K.A. J Comput Biol, 2006, 13, 21-42

Using motion planning to study RNA folding kinetics. Tang, X.; Kirkpatrick, B.; Thomas, S.; Song, G. & Amato, N.M. J Comput Biol, 2005, 12, 862-881

Noncoding RNA gene detection using comparative sequence analysis; BMC Bioinformatics 2(8) Eddy, Sean R.,Rivas, Elena; 2001.

Genomics

Bayesian estimation of genomic distance; Genetics 166(1) S.621-629; Durrett, Richard,Nielsen, Rasmus,York, Thomas L.; 2004.

Bayesian estimation of the number of inversions in the history of two chromosomes; Journal of computational biology 9(6) S.805-818; Durrett, Richard,Nielsen, Rasmus,York, Thomas L.; 2002.

Subfunctionalization: How often does it occur? How long does it take?; Theoretical Population Biology 66(2) S.93-100; Durrett, Richard,Ward, Rachel; 2004.

Multiple genome rearrangement; Proceedings of the second annual international conference on computational molecular biology S.243 - 247; Blanchette, Mathieu,Sankoff, David; 1998.

Multiple genome rearrangement: a general approach via the evolutionary genome graph; Bioinformatics 18(1) S.303-311; Goldfarb, Lev,Korkin, Dmitry; 2002.

Identifying conserved gene clusters in the presence of homology families. He, X. & Goldwasser, M.H. J Comput Biol, 2005, 12, 638-656

Chromosomal breakpoint reuse in genome sequence rearrangement. Sankoff, D. & Trinh, P. J Comput Biol, 2005, 12, 812-821

Gene tree reconstruction and orthology analysis based on an integrated model for duplications and sequence evolution; Proceedings of the eighth annual international conference on Computational molecular biology S.326-335; Arvestad, Lars,Berglund, Ann-Charlotte,Lagergren, Jens,Sennblad, Bengt; 2004.

Bayesian gene/species tree reconciliation and orthology analysis using MCMC; Bioinformatics 19(1) S.i7-i15; Arvestad, Lars,Berglund, Ann-Charlotte,Lagergren, Jens,Sennblad, Bengt; 2003.

Mcmc genome rearrangement. Miklos, I. (2003). Bioinformatics, 19(2):130–137.

A Probabilistic Model for Gene Content Evolution with Duplication, Loss, and Horizontal Transfer. M. Csürös and I. Miklós. In Research in Computational Molecular Biology, 10th Annual International Conference, RECOMB 2006, Venice, Italy, April 2-5, 2006. Proceedings, volume 3909 of Lecture Notes in Computer Science, pages 206-220, 2006.

DNA Alignment

Linear time algorithms for finding and representing all the tandem repeats in a string. Gusfield, D. and Stoye, J. (1998). Technical Report CSE-98-4, Department of Computer Science, UC Davis.

An eulerian path approach to global multiple alignment for dna sequencs. Zhang, Y. and Waterman, M. S. (2003). Journal of Computional Biology, 10(6):803–819.

Gibbs sampler for statistical multiple alignment. J.L. Jensen and J. Hein. Statistica Sinica, 15(4):889-908, 2004.

Metabolic Networks

Efficient detection of network motifs.S. Wernicke. IEEE/ACM Trans Comput Biol Bioinform, 3(4):347-59, 2006.

Unicyclic Networks: Compatibility and Enumeration. Charles Semple and Mike Steel IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 3, Number 1, January, 2006

Motif search in graphs: application to metabolic networks. V. Lacroix, C.G. Fernandes, and M.F. Sagot. IEEE/ACM Trans Comput Biol Bioinform, 3(4):360-8, 2006.

Proteomics

Efficient methods for estimating amino acid replacement rates. L. Arvestad. J Mol Evol, 62(6):663-73, 2006

Three-dimensional shape-structure comparison method for protein classification. P. Daras, D. Zarpalas, A. Axenopoulos, D. Tzovaras, and M.G. Strintzis. IEEE/ACM Trans Comput Biol Bioinform, 3(3):193-207, 2006.

Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. Amato, N. M., Dill, K. A., and Song, G. (2003). Journal of Computional Biology, 10(3/4):239–255.

Prediction of protein function using protein-protein interaction data. Deng, M., Zhang, K., Mehta, S., Chen, T., and Sun, F. (2003). Journal of Computional Biology, 10(6):947–960.

Classification of protein quaternary structure with support vector machine. Zhang, S.-W., Pan, Q., Zhang, H., Zhang, Y.-L., and Wang, H.-Y. (2003). Bioinformatics, 19(18):2390–2396.

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. W. Chu, Z. Ghahramani, A. Podtelezhnikov, and D.L. Wild. IEEE/ACM Trans Comput Biol Bioinform, 3(2):98-113, 2006.

Accelerating screening of 3d protein data with a graph theoretical approach. Froemmel, C., Gille, C., Goede, A., Gr¨opl, C., Hougardy, S., Nierhoff, T., and Robert Preissner, M. T. (2003). Bioinformatics, 19(16):2112–2121.

Algorithmic complexity of protein identification: Combinatorics of weighted strings. Cieliebak, M., Erlebach, T., Liptak, Z., Stoye, J., and EmoWelzl (2004). Discrete Applied Mathematics, 137(1):27–46.

Phylogenetics

Bayesian logistic regression using a perfect phylogeny. T.G. Clark, M. De Iorio, and R.C. Griffiths. Biostatistics, 8(1):32-52, 2007.

Relaxed phylogenetics and dating with confidence. A.J. Drummond, S.Y. Ho, M.J. Phillips, and A. Rambaut. PLoS Biol, 4(5):e88, 2006.

On computing the nearest neighbor interchange distance. DasGupta, B.; He, X.; Jiang, T.; Li, M.; Tromp, J. & Zhang, L. 1997 C. Daskalakis, C. Hill, A. Jaffe, R. Mihaescu, E. Mossel, and S. Rao.

Likelihood-based tests of topologies in phylogenetics; Systematic Biology 49(4) S.652-670; Anderson, Jon P.,Goldman, Nick,Rodrigo, Allen G.; 2000.

The splits in the neighbourhood of a tree; Annals of Combinatorics 8(1) S.1-11; Bryant, David; 2004.

A classification of consensus methods for phylogenies; Series in Discrete Mathematics and Theoretical Computer Science 61S.163-184; Bryant, David; 2003.

A genetical theory of species selection; Journal of theoretical Biology 177(3) S.237-245; Rice, Sean H.; 1995.

On Distances between Phylogenetic Trees. DasGupta; He; Jiang; Li; Tromp & Zhang SODA: ACM-SIAM Symposium on Discrete Algorithms (A Conference on Theoretical and Experimental Analysis of Discrete Algorithms), 1997, 427-436

Computing Distances between Evolutionary Trees. Handbook of Combinatorial Optimization Bhaskar DasGupta, X.H. & Zhang, L. Du, D.Z. & Pardalos, P. (ed.) , Kluwer Academic Publishers, 1998, 2, 35-76

On the nearest neighbour interchange distance between evolutionary trees. Li, M.; Tromp, J. & Zhang, L. J Theor Biol, 1996, 182, 463-467

Maximal Accurate Forests from Distance Matrices. In Research in Computational Molecular Biology, 10th Annual International Conference, RECOMB 2006, Venice, Italy, April 2-5, 2006. Proceedings, volume 3909 of Lecture Notes in Computer Science, pages 281-295, 2006.

IQPNNI: Moving Fast Through Tree Space and Stopping in Time. Mol Biol Evol. 2004 Aug;21(8):1565-71.

Other

How many samples are needed to build a classifier: a general sequential approach; Bioinformatics 21(1) S.63-70; Carroll, Raymond J.,Dougherty, Edward R.,Fu, Wenjiang J.,Mallick, Bani; 2005.

Directional selection and the site-frequency spectrum; Genetics 159(4) S.1779-1788; Bustamante, Carlos D.,Hartl, Daniel L.,Sawyer, Stanley,Wakeley, John; 2001.

A sufficient condition for reducing recursions in hidden Markov models. Y.S. Song. Bull Math Biol, 68(2):361-84, 2006./td>

On the complexity of fundamental computational problems in pedigree analysis. Piccolboni, A. and Gusfield, D. (2003). Journal of Computional Biology, 10(5):763–773.