Istvan
(Steve) Ladunga, Ph.D.
Professor
of Computational Biology
Head of
the Computational Biology Research Lab
Department of Statistics
University of Nebraska - Lincoln
E145 Beadle Center, University of Nebraska-Lincoln
1901 Vine St., Lincoln, NE 68588-0665
Phone: (402) 472-6074
Research
The Laboratory that I lead conducts research in the computational biology (you can call it bioinformatics) of:
- Structural principles of nucleosomes and nucleosome mapping,
- New generation sequencing,
- Transcriptional regulation,
- Optimal design of short interfering RNA duplexes,
- Pattern analysis, and
- Physical sequence analysis.
Nucleosomes are complexes of sharply bent DNA and histone that cover ~60% of the yeast genome. The localization, type and modifications of nucleosomes contribute to DNA packaging, accessibility, replication and repair, as well as to the regulation of transcription. We have developed a sequential quadratic programming optimization model that threads the DNA around the histone octamer. Since model can relatively accurately map nucleosomes to the experimental locations, we have shown that for the majority of nucleosomes, their
positioning is determined by the bendability of the DNA , not by histone remodeling processes.Another focus of our Team is the systems biology of transcriptional regulation. To this end, we use chromatin immunoprecipitation and next-generation sequencing (currently Solexa, ChIP-sequencing) experiments over the both the mouse and the Arabidopsis thaliana genome. We also develop gene-specific regulation models using machine learning models over a large set of publicly available gene expression observations in Arabidopsis.
We develop a detailed computational model of gene regulation based on the above experiments. This model would help us to test hypotheses and to survey the sensitivity, modularity and redundancy analysis of gene regulatory networks.
Istvan Ladunga: More Complete Gene Silencing by Fewer siRNAs: Transparent Optimized Design and Biophysical Signature
Nucleic Acids Res. 2007;35(2):433-40. Epub 2006 Dec 14.
Highly accurate knockdown functional analyses based on RNA interference (RNAi) requires the most complete hydrolysis of the targeted mRNA while avoiding the degradation of untargeted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untargeted genes. Therefore, selecting a small number of highly active siRNAs is critical for obtaining the most complete knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and transparent machine-learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility, and self-hairpin features over 2,200 published siRNAs. This tool reaches an accuracy of 92.3% in cross-validation experiments. We fully present the underlying biophysical signature that involves free energy, accessibility, and dinucleotide characteristics. We show that while complete silencing is possible at certain structured target sites, accessibility information improves the prediction of the 90% active siRNA target sites.
Online RNAi target selecttion can be performed on our web-server.
Education
MS and BS in Biology, Budapest University of Sciences.
Professional Experience
Center for Biotechnology and Department of
Statistics, University of Nebraska-Lincoln
- Large-scale analysis of nucleosome-depleted regions, transcription factor binding sites and histone modifications using chromatin immunoprecipitation and next-generation (Illumina/Solexa) sequencing
- Reconstruction of transcriptional regulatory networks.
- Prediction of transcription factor binding sites
- Optimization of siRNA selection for the highest knockdown activity using machine learning and biophysical analysis
- Education: graduate course STAT892/BIOS832 Computational Molecular Biology, Fall 2007
Celera Genomics/Applied Biosystems, Foster City, CA
Senior Staff Scientist
- Led a group of 12 professionals, identified and patented novel and splice-variant secreted proteins from genomic and EST sequences.
- Directed the creation of a pipeline utilizing Celera’s proprietary OTTO automatic prediction tool, the full repertoire of BLAST, sim4, lap over public and patent nucleic acid and protein databases using Celera’s Compute Farm by the Load Sharing Facility, translated SW on a Paracel-Dell computer, novelty checks, GeneWise searches, signal peptide and domain predictions, and alternative transcripts.
- Managed the large-scale annotations of secreted proteins from the Celera genome.
- Mentored & trained coworkers on bioinformatics and annotation, .
- Identified candidate novel microRNAs in humans.
AGY Therapeutics, Inc., South San Francisco, CA
- Reconstructed major post-stroke events of neuronal regeneration, memory and learning using systems biology, pathways and ontologies applied to microarray and in situ hybridization experiments in rat models of stroke.
SmithKline Beecham (now GlaxoSmithKine) Pharmaceuticals,
King of Prussia, PA
1996 - 2000
Principal Investigator, Bioinformatics Dept.
Analyzed gene expression in prostate cancer.
Adapted and refined algorithms for the maximization of correct classifications using mathematical programming.
Baylor College of Medicine, Human Genome Center, Houston
1994 - 1996 Visiting Research Associate
Created scoring theory for substitution patterns, implemented in pattern-to-single-residue scoring matrices.
Revealed conservation of biological, physical, sterical, and chemical features of amino acids.
Department of Mathematics, Stanford University
1993 –1994 Visiting Research Associate
Performed research in mathematical statistics and computational biology with Samuel Karlin, member National Academy of Sciences, principal author of the probability theory applied in BLAST.
Department of Genetics, Budapest University of Sciences
1989 –1993 Research Associate
Co-organized a Conference of the European Society for Evolutionary Biology. Predicted signal peptides by computational neural networks.
Developed a preliminary classification of proteins.
Other work experience
Headed the Department of International Cooperation at the National Committee for Technological Development, Budapest.Research Associate at the Institute for Computer Science, Academy of Sciences, Budapest. Working in the Team of Istvan Lang, Secretary General of the Hungarian Academy of Sciences, contributed to the reduction of nationwide crop yield losses by ~1.6 percent by optimized culture allocation.
Selected Publications
- Ladunga, I. (2008) (Editor) The Computational Biology of Transcription Factor Binding.In the series:
Methods in Molecular Biology. Springer, Berlin.In preparation - Y. Wang, I. Ladunga, A.R. Miller, K. M. Horken, T. Plucinak, D. P. Weeks, and C. P. Bailey. (2008) The Small Ubiquitin-like Modifier (SUMO) and SUMO Conjugating System of Chlamydomonas reinhardtii. Genetics,179(1):177-192.
- Abdelaty S, Alvarez-Venegas R, Yilmaz M, Le O, Hou Q, Sader M, Al-Abdallat A, Xia Y, Lu G, Ladunga I, Avramova Z. (2008). The highly similar
Arabidopsis homologs of Trithorax atx1 and atx2 encode divergent biochemical functions. Plant Cell, 20(3):568-579. - Ladunga, I. (2006) More Complete Gene Silencing by Fewer siRNAs: Transparent Optimized Design and Biophysical Signature. Nucleic Acids Res.35(2):433-440 Epub: 2006 Dec 14.
- Ladunga, I. (1999) PHYSEAN: PHYsical SEquence ANalysis of Proteins. Bioinformatics, 15: 128-138.
- Ladunga, I. (2000) Large-scale discovery of secretory proteins from nucleic acid sequences. Current Opinion in Biotechnology,11:13-18, invited paper.
- Ladunga, I., and Smith, R.F.(1997) Amino acid substitution patterns conserve folding of proteins by preserving steric and hydrophobicity properties. Protein Engineering 10: 187-196.
- Ladunga, I., Wiese, B.A. and Smith, R.F. (1996) FASTA-SWAP and FASTA-PAT: pattern database searches using combinations of aligned amino acids and a novel scoring theory. Journal of Molecular Biology. 259: 840-854.
- Ladunga, I. (2002) Finding homologs in amino acid sequences using network BLAST searches. Curr. Prot. Bioinf., Chapter 3.4. Published online November 2002
- Ladunga, I. (2002) Finding homologs in nucleic acid sequences using network BLAST searches. Curr. Prot. Bioinf., Chapter 3.3. Published online August 2002
- Karlin, S., and Ladunga, I. (1994). Novel comparisons of genomic sequences in Eukaryotes. Proc. Natl. Acad. Sci. U.S.A., 91: 2832-12836.
- Karlin, S., Ladunga, I., and Blaisdell, B.E.(1994). Heterogeneity of genomes: measures and values. Proc. Natl. Acad. Sci. U.S.A., 91: 12837-12841.
- Ladunga, I. (1992) Phylogenetic continuum indicates "galaxies" in the protein universe. Journal of Molecular Evolution, 34: 358-375.
- Ladunga, I., Czakó, F, Csabai, I., and Geszti, T. (1991) Improving prediction accuracy of signal peptides by simulated neural networks. Computer Applications in the BioSciences, 7: 485-487.
- Ladunga, I. (1999). OLIGOPAT: Oligopeptide Pattern Analysis and Prediction of Proteins by Mathematical Programming Protein Sci,.8, Suppl. 1., 157.
- Ladunga, I. (1998) A Novel Sequence Analysis Based on Weighted Physicochemical Properties of Amino Acid Residues. Protein Sci. 7: 75.
- Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., Vandergriff, J.A., and Doremieux, O. (2003) PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids. Res., 31: 334-341.
Patents and copyrights
Selected patents/copyrights:
- Ladunga, I., Smith, R.F and Wiese B.A.: FASTA-SWAP and FASTA-PAT: pattern database searches using combinations, filed in June, 1996. Copyright.
- Ladunga, I. Methods and systems for identification of protein classes
Patent Number: US5987390, 1999, Industry, United States of America. - A browsable database for biological use
Patent Number: WO2004053769 (A3) , 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: US2005149269, 2005, Industry, United States of America. - A browsable database for biological use
Patent Number: WO2004053769 (A2) , 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: EP1576524 (A3), 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: EP1576524 (A2), 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: EP1576524 (A0), 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: AU2003299589 (A1), 2004, Industry, United States of America. - A browsable database for biological use
Patent Number: WO2004053769, 2004, Industry, United States of America. - Isolated human secreted proteins, nucleic acid molecules encoding human secreted proteins, and uses thereof
Patent Number: US2005048560, 2005, Industry, United States of America. - Isolated human secreted proteins, nucleic acid molecules encoding human secreted proteins, and uses thereof
Patent Number: US2005043229, 2005, Industry, United States of America.
Book chapters and dissertations
Ladunga, I. (1982) Molecular evolution. In: Vida G.(ed.): The Genetic Basis of Evolution. (in Hungarian), pp. 157-207, Natura, Budapest.Ladunga, I. Computer Analyses of Protein Evolution. Ph.D. Thesis (in Hungarian). Budapest University of Sciences.
Ladunga I. Computer Analysis and Simulation of Protein Evolution. Master’s Thesis (in Hungarian). Budapest University of Sciences.
Professional Organizations
Institute for Electric and Electronic Engineers, Computer Society
American Association for the Advancement of Science
National Committee for Technological Development, Hungary
European Society for Evolutionary Biology