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Dr. rer. nat. Johanna Mazur

How can it be that mathematics, being after all a product of human thought which is independent of experience, is so admirably appropriate to the objects of reality?

(Albert Einstein)


Essential Curriculum Vitae


since 03/2012: PostDoc at Bium(MZ) (Core Facility Bioinformatics) at the Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg-Universität Mainz
06/2009 - 07/2012: Fellow of the HGS MathComp (Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences)
09/2007 - 02/2012: PhD Student at the Viroquant Research Group Modeling at Ruprecht-Karls-Universität Heidelberg; supervisors: Prof. Dr. L. Kaderali, Prof. Dr. Dres. h.c. H. G. Bock, and Dr. Stefan Körkel
Title: Bayesian Inference of Gene Regulatory Networks: From Parameter Estimation to Experimental Design
10/2001 - 04/2007: Mathematic Studies at the Technical University of Darmstadt
01/2007: Diploma thesis; supervisor: Prof. Dr. K.-H. Neeb
Title: Examples of Continuum Contragredient Lie Algebras
06/2001: Abitur at the Gymnasium Gernsheim; high school graduation


Projects


Current Research Project

PhD Project


Scientific Skills


Systems Biology / Bioinformatics

Mathematics

Computer science


Software



Publications


Written Publications

  1. A. Schwarz, S. Tenzer, M. Hackenberg, J. Erhart, A. Gerhold-Ay, J. Mazur, J. Kuharev, J. Ribeiro, Michail Kotsyfakis (2014). A systems level analysis reveals transcriptomic and proteomic complexity in Ixodes ricinus midgut and salivary glands during early attachment and feeding. Molecular & Cellular Proteomics, in press, doi: 10.1074/mcp.M114.039289
  2. M. Renovanz, A. Gutenberg, M. Haug, E. Strittmatter, J. Mazur, M. Nadji-Ohl, A. Giese, N. Hopf (2013). Postsurgical screening for psychosocial disorders in neurooncological patients. Acta Neurochirurgica, 55(12):2255-2261, doi:10.1007/s00701-013-1884-9
  3. J. Mazur, L. Kaderali (2013). The Importance and Challenges of Bayesian Parameter Learning in Systems Biology. In: "Model Based Parameter Estimation: Theory and Applications". H. G. Bock, T. Carraro, W. Jäger, S. Körkel, R. Rannacher, J. P. Schlöder (Eds.) Contributions in Mathematical and Computational Sciences, (Vol. 4), Springer
  4. A. Ruggieri, E. Dazert, P. Metz, S. Hofmann, J.-P. Bergeest, J. Mazur, P. Bankhead, M.-S. Hiet, S. Kallis, G. Alvisi, C. E. Samuel, V. Lohmann, L. Kaderali, K. Rohr, M. Frese, G. Stoecklin, R. Bartenschlager (2012). Dynamic Oscillation of Translation and Stress Granule Formation Mark the Cellular Response to Virus Infection. Cell Host & Microbe, 12(1):71-85, doi:10.1016/j.chom.2012.05.013
  5. P. Metz, E. Dazert, A. Ruggieri, J. Mazur, L. Kaderali, A. Kaul, U. Zeuge, M. Trippler, V. Lohmann, M. Binder, M. Freese, R. Bartenschlager (2012). Identification of type I and type II interferon-induced effectors controlling hepatitis C virus replication. Hepatology, 56(6):2082-2093, doi:10.1002/hep.25908
  6. J. Mazur, L. Kaderali (2012). The Relevance of Bayesian Experimental Design for Modeling in Systems Biology. In: "Computational Intelligence". Alexandru Floares (Eds.) Computer Science, Technology and Applications, Nova, pp. 69-74
  7. J. Mazur, L. Kaderali (2011). Bayesian Experimental Design for the Inference of Gene Regulatory Networks. In: "Proceedings of the Fifth International Workshop on Machine Learning in Systems Biology, Vienna, Austria, July 20-21, 2011". Stefan Kramer and Neil Lawrence (Eds.), 54-58
  8. J. Mazur, D. Ritter, G. Reinelt, L. Kaderali (2009). Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling. BMC Bioinformatics, 10:448, doi:10.1186/1471-2105-10-448

Talks

  1. J. Mazur, I. Zwiener, H. Binder (2014). Combining gene expression measurements from different platforms with a simultaneous boosting approach. 35th Annual Conference of the International Society for Clinical Biostatistics (ISCB) 2014, Vienna, Austria, August 24-27, 2014
  2. J. Mazur (2014). RNA-Seq Data: From Statistical Analysis to Experimental Design. Institute of Computational Biology Seminar, Helmholtz Zentrum München, January 20, 2014, invited talk
  3. J. Mazur, H. Binder (2013). Designing RNA-Seq Experiments for Multiple Groups. DAGStat 2013, Freiburg, March 19-22, 2013
  4. J. Mazur, L. Kaderali (2011). Bayesian Experimental Design for the Inference of Gene Regulatory Networks. International Workshop on Machine Learning in Systems Biology (MLSB) 2011, Vienna, Austria, July 20-21, 2011
  5. J. Mazur (2010). Bayesian experimental design for the inference of ordinary differential equations models in systems biology. HGS MathComp Annual Colloquium 2010, Heidelberg, November 19, 2010
  6. J. Mazur, L. Kaderali (2010). Rekonstruktion molekularer Netzwerke mit regularisierten Schätzverfahren. GMDS Annual Meeting 2010, Mannheim, September 5-9, 2010
  7. J. Mazur, D. Ritter, G. Reinelt, L. Kaderali (2009). Reconstructing Nonlinear Dynamic Models of Gene Regulation using Stochastic Sampling. Model Based Parameter Estimation - Theory and Applications (Workshop), July 15-17, IWR (Heidelberg), 2009

Selected Posters

  1. J. Mazur, H. Binder (2013), Experimental Design for Multiple Group RNA-Seq Data. International Conference on Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology (ECCB) 2013, Berlin, July 21-23, 2013
  2. J. Mazur, L. Kaderali (2011), Optimum Bayesian Experimental Design for the Inference of Ordinary Differential Equations Models in Systems Biology. International Conference on Systems Biology (ICSB) 2011, Heidelberg/Mannheim, August 28 - September 1, 2011
  3. J. Mazur, D. Ritter, G. Reinelt, L. Kaderali (2010), Reverse Engineering of Gene Regulatory Networks using Nonlinear Dynamics and Stochastic Sampling. International Conference on Systems Biology of Human Disease (SBHD) 2010, Boston, June 16-18, 2010
  4. J. Mazur, D. Ritter, G. Reinelt, L. Kaderali (2009). Reverse Engineering of Gene Regulatory Networks with a Nonlinear ODE-Model Embedded into a Bayesian Framework. German Conference on Bioinformatics (GCB) 2009, Halle (Saale), September 28-30, 2009
  5. J. Mazur, D. Ritter, G. Reinelt, L. Kaderali (2009). Reverse Engineering of Gene Regulatory Networks with a Nonlinear ODE-Model by means of Stochastic Sampling. German Symposium on Systems Biology, Heidelberg, May 12-15, 2009
  6. L. Kaderali, J. Mazur, D. Ritter (2008). Statistical inference of biochemical networks with delay differential equations. International Conference on Systems Biology (ICSB) 2008, Gothenburg, August 22-28, 2008.
  7. D. Ritter, J. Mazur, G. Reinelt, L. Kaderali (2008). Reconstructing Gene Regulatory Networks using Differential Equations in a Stochastic Framework. Conference on Systems Biology of Mammalian Cells (SBMC) 2008, Dresden, May 22-24, 2008.

Supervised Theses

  1. D. Ritter (2008), Machine Network Learning - Bayesian Inference of Gene Regulatory Networks with Differential Equations using Stochastic Simulation. Master Thesis, Faculty of Mathematics and Computer Science, University of Heidelberg