Alexei A. Podtelezhnikov

Curriculum Vitæ


1335 Stoney River Dr
Ambler PA 19002
apodtele­@­gmail­·­com
(215)237÷7865

Research Interests: Genomics, Genetics, RNA-Seq and DNA-Seq Data Analysis, Statistical Learning, Computational Biophysics and Biochemistry, Structural Bioinformatics, Molecular Modeling


Teaching Interests: Numerical and Computational Methods in Biophysics and Biochemistry, Statistical Methods, Advanced Monte Carlo Methods, Bioinformatics, Genetics, Genomics, Proteomics


Education New York University
    Ph.D. in Biomolecular Chemistry (2000),
    Dissertation: Kinetics and thermodynamics of DNA cyclization.
Moscow Institute of Physics and Technology
    M.S. in Molecular Biophysics (1994) and
    B.S. in Applied Physics and Mathematics (1992),
    Thesis: Testing the quality of electron microscope mapping data for DNA molecules with sequence-specific ligands.

Research Experience:
Merck & Co., Inc. Merck Research Laboratories

West Point, PA
07/2008-present
Principal Scientist — Genetics and Pharmacogenomics
  • Statistical analysis of gene expression profiling
  • Development of predictive toxicity and carcinogenicity biomarkers
  • Devise biomarkers for optimal bioprocess harvest
  • Disease progression and target engagement biomarkers for neuroscience
  • Characterization of animal disease models through mRNA profiling
  • Integration of genomics and genetics of brain aging
Michigan Technological University

Houghton, MI
08/2007-06/2008
Visiting Assistant Professor — Department of Physics
  • Contrastive divergence learning of protein energetics
  • Bayesian modeling of residue contacts in proteins
  • Monte Carlo reconstruction of protein secondary structure
  • Protein structure prediction using knowledge-based potentials
Keck Graduate Institute of Applied Life Sciences

Claremont, CA
11/2003-06/2007
Postdoctoral Fellow — Wild Research Group
  • Knowledge-based protein structure prediction
  • Machine learning of hydrogen bonding and other protein interactions
  • Data mining of domain sequences and their secondary structures
  • Efficient Monte Carlo sampling of peptide conformations
Howard Hughes Medical Institute (UC San Diego)

San Diego, CA
11/2000-11/2003
Research Associate — McCammon Research Group
  • Modeling tetrameric HIV-1 integrase complex with DNA
  • Hydrodynamics and electrostatics of HIV-1 integrase
  • Docking of small ligands to smallpox topoisomerase
  • Brownian dynamics of ligand binding to acetylcholinesterase
New York University, Department of Chemistry

New York, NY
09/1995-10/2000
Graduate Assistant — Vologodskii Research Group
  • Brownian dynamics and Monte Carlo simulations of DNA cyclization
  • Supercoiling and knotting of long DNA molecules
  • DNA topology and its implications for oncology
  • Non-equilibrium transcriptional properties of DNA
Institute of Molecular Genetics of Russian Academy of Science

Moscow, Russia
09/1991-08/1995
Intern — Frank-Kamenetskii Research Group
  • Brownian dynamics simulations of diffusional polymer cyclization
  • Alignment of DNA images for Electron Microscopy mapping
  • Electron microscopy of specific DNA-ligand binding

Teaching
Experience
Visiting Assistant Professor — Michigan Technological University (2007-2008)
  • Teaching and grading Statistical Mechanics and Solid State Physics
  • Supervising a senior project for a Physics-major student
Graduate Supervisor — University of California, San Diego (2002-2003)
  • Guided a graduate student project on solution and visualization of Poisson-Boltzmann equation for small molecule docking studies.
Teaching Assistant — New York University (1995-2000)
  • Instructed laboratory and recitation sessions for College Chemistry I and II. Graded exams, homeworks, and laboratory reports.
Awards
and Honors
  • Merck New Jersey Reward & Recognition Awardee (2013).
  • Participant in the NSF-funded Proteomics program, IPAM UCLA (2004).
  • M.S. diploma with excellence, cum laude (1994).
Additional
Skills
  • Statistical methods and numerical methods
  • C/C++ and Fortran programming, parallel programming with MPI
  • Matlab and R
  • Linux, UNIX, and Windows environment
Software
Development
  • CRANKITE: a suite for polypeptide backbone conformation sampling
  • jfm2full: a calculator for DNA cyclization probability
References
  • David L. Wild, Ph.D., Molecular Biophysics, University of Oxford; Professor, Warwick Systems Biology Centre, University of Warwick.
  • J. Andrew McCammon, Ph.D., Chemical Physics, Harvard University; Investigator, Howard Hughes Medical Institute; Joseph E. Mayer Professor of Theoretical Chemistry, Professor of Pharmacology, UCSD.
  • Alexander V. Vologodskii, Ph.D., Moscow Institute of Physics and Technology, D.S., Moscow State University; Research Professor, Department of Chemistry, NYU.
  • Frederic D. Bushman, Ph.D., Cellular and Developmental Biology, Harvard University; Professor, Department of Microbiology, University of Pennsylvania School of Medicine.
  • Zoubin Ghahramani, Ph.D., Cognitive Neuroscience, Massachusetts Institute of Technology; Professor in Information Engineering, University of Cambridge.
(Contact information is available upon request)

Publications

  1. E. S. Arnardottir, E. V. Nikonova, K. R. Shockley, A. A. Podtelezhnikov, et al. Blood-gene expression reveals reduced circadian rhythmicity in individuals resistant to sleep deprivation, Sleep 37 1589-1600 (2014).
  2. B. Zhang, C. Gaiteri, L-G. Bodea, Zh. Wang, J. McElwee, A. A. Podtelezhnikov, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease, Cell 153, 707-720 (2013).
  3. A. Bernard, L. S. Lubbers, K. Q. Tanis, R. Luo, A. A. Podtelezhnikov, et al. Transcriptional architecture of the primate neocortex, Neuron 73, 1083-1099 (2012).
  4. I. Krop, T. Demuth, T. Guthrie, et al., A. Podtelezhnikov, et al. Phase I pharmacologic and pharmacodynamic study of the gamma secretase (Notch) inhibitor MK-0752 in adult patients with advanced solid tumors, JCO 30 2307-2313 (2012).
  5. A. A. Podtelezhnikov, K. Q. Tanis, M. Nebozhyn, W. J. Ray, D. J. Stone and A. P. Loboda. Molecular insights into the pathogenesis of Alzheimer's disease and its relationship to normal aging, PLoS ONE 6 e29610 (2011).
  6. A. McCampbell, K. Wessner, M. W. Marlatt, C. Wolffe, D. Toolan, A. Podtelezhnikov, et al. Induction of Alzheimer.s-like changes in brain of mice expressing mutant APP fed excess methionine, J. Neurochem. 116, 82-92 (2011).
  7. A. A. Podtelezhnikov and D. L. Wild. Reconstruction and stability of the secondary structure elements in the context of protein structure prediction, Biophysical Journal 96, 4399-4408 (2009).
  8. A. A. Podtelezhnikov and D. L. Wild. Comment on "Efficient Monte Carlo trial moves for polypeptide simulations" [J. Chem. Phys. 123, 174905 (2005)], J. Chem. Phys. 129, 027103 (2008).
  9. A. A. Podtelezhnikov and D. L. Wild. CRANKITE: A Fast Polypeptide Backbone Conformation Sampler, Source Code Biol. Med. 3, 12 (2008).
  10. A. A. Podtelezhnikov, Z. Ghahramani, D. L. Wild. Learning about protein hydrogen bonding by minimizing contrastive divergence, Proteins 66, 588-599 (2007).
  11. W. Chu, Z. Ghahramani, A. Podtelezhnikov, D. L. Wild. Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction, IEEE/ACM Trans. Comp. Biol. Bioinformatics 3, 98-113 (2006).
  12. A. A. Podtelezhnikov and D. L. Wild. Exhaustive Metropolis Monte Carlo sampling and analysis of polyalanine conformations adopted under the influence of hydrogen bonds, Proteins 61, 94-104 (2005).
  13. A. A. Podtelezhnikov, K. Gao, F. D. Bushman and J. A. McCammon. Modeling HIV-1 integrase complexes based on their hydrodynamic properties, Biopolymers 68, 110-120 (2003).
  14. A. V. Vologodskii, W. T. Zhang, V. V. Rybenkov, A. A. Podtelezhnikov, D. Subramanian, J. D. Griffith, N. R. Cozzarelli. Mechanism of topology simplification by type II DNA topoisomerases, Proc. Natl. Acad. Sci. U.S.A. 98, 3045-3049 (2001).
  15. A. A. Podtelezhnikov, C. Mao, N. C. Seeman and A. V. Vologodskii. Multimerization-cyclization of DNA fragments as a method of conformational analysis, Biophysical Journal 79, 2692-2704 (2000).
  16. A. A. Podtelezhnikov and A. V. Vologodskii. Dynamics of small loops in DNA molecules, Macromolecules 33, 2767-2771 (2000).
  17. A. A. Podtelezhnikov, N. R. Cozzarelli and A. V. Vologodskii. Equilibrium distributions of topological states in circular DNA: interplay of supercoiling and knotting, Proc. Natl. Acad. Sci. U.S.A. 96, 12974-12979 (1999).
  18. A. S. Krasilnikov, A. Podtelezhnikov, A. Vologodskii and S. M. Mirkin. Large-scale effects of transcriptional DNA supercoiling in vivo, J. Mol. Biol. 292, 1149-1160 (1999).
  19. A. A. Podtelezhnikov and A. V. Vologodskii. Simulations of polymer cyclization by Brownian Dynamics, Macromolecules 30, 6668-6673 (1997).
  20. A. A. Podtelezhnikov, A. V. Kurakin, A. V. Vologodskii and D. I. Cherny. Testing the quality of electron microscope mapping data for DNA molecules with sequence-specific ligands, Micron 25, 439-446 (1994).

Invited Presentations

  1. A. A. Podtelezhnikov. Learning about Protein Energetics by Minimizing Contrastive Divergence. Machine learning in Structural Bioinformatics, Copenhagen Denmark, April 2008.
  2. A. A. Podtelezhnikov. Polypeptide sampling, protein structure prediction, and contrastive divergence, presented at Proteomics Reunion Conference IPAM, Lake Arrowhead CA, December 2005.
  3. A. A. Podtelezhnikov. Polypeptide sampling, knowledge-based potentials, and protein structure prediction, presented at Gatsby Computational Neuroscience Unit UCL, London UK, October 2005.
  4. A. A. Podtelezhnikov. Molecular dynamics vs Brownian dynamics vs Monte Carlo methods: pros and cons, presented at Proteomics Culminating Conference IPAM, Lake Arrowhead CA, June 2004.

Conference Abstracts

  1. A. A. Podtelezhnikov, K. Q. Tanis, D. J. Stone, A. P. Loboda. Accelerated aging and metabolic disruption in the brain provide the basis for Alzheimer's disease progression modeling. Alzheimer's & Dementia 6 (Suppl.): e28 (2010) — International Conference on Alzheimer's Disease, Honolulu HI, July 10 - 15, 2010.
  2. A. A. Podtelezhnikov, Z. Ghahramani, D. L. Wild. Learning about hydrogen bonding by minimizing contrastive divergence. An Isaac Newton Institute Workshop, Camdridge UK, October 30 - November 3, 2006.
  3. A. A. Podtelezhnikov, Z. Ghahramani, D. L. Wild. Contrastive divergence learning of hydrogen bonding and side-chain interactions in proteins using Metropolis Monte Carlo. An Isaac Newton Institute Workshop, Camdridge UK, December 11 - December 15, 2006.
  4. A. A. Podtelezhnikov, K. Gui, F. Bushman, J. A. McCammon. Modeling HIV-1 integrase complexes based on their electrostatic and hydrodynamic properties. Protein Science 11 (Suppl. 1): A541-A541 (2002) — 16th Symposium of the Protein Society, San Diego CA, August 17-21, 2002.
  5. A. Podtelezhnikov and A. Vologodskii. Thermodynamics and kinetics of DNA loop formation studied by computer simulations. Biophysical Journal 76 (1): A316-A316 Part 2 (1999) — 43rd Annual Biophysical Society Meeting, Baltimore MD, February 13-17, 1999.
  6. A. A. Podtelezhnikov and A. V. Vologodskii. Analysis of multimerization-cyclization of short bent DNA fragments. Efficient method of calculation of J-factors. — Gordon Research Conference on Biopolymers, Newport RI, June 14-19, 1998.
  7. A. A. Podtelezhnikov and A. V. Vologodskii. Structural interpretation of the cyclization kinetics of bent DNA fragments. 1. Extraction of the J-factor. Biophysical Journal 74 (2): A287-A287 Part 2 (1998) — 42nd Annual Biophysical Society Meeting, Kansas City MO, February 22-26, 1998.
  8. A. Podtelezhnikov and A. Vologodskii. Simulations of DNA Cyclization by Brownian Dynamics. J. Biomol Struct. Dyn. (1997) — 10th Conversation in the Discipline Biomolecular Stereodynamics, Albany NY, June 17-21, 1997.