Sie sind hier: FRIAS School of Life Sciences … Fellows Hauke Busch

Hauke Busch

Computational Biology
Molecular Medicine and Cell Research
Freiburg, Germany

Freiburg Institute for Advanced Studies
School of Life Sciences - LifeNet
Albertstr. 19
79104 Freiburg im Breisgau

Tel. +49 (0)761-203 9626
Fax +49 (0)761-203 97334


    Hauke Busch is a FRIAS Junior Fellow in the School of Life Sciences.

    He studied Physics at the Darmstadt University of Technology, Germany and at the Trinity College, Dublin. Hauke Busch received his PhD in 2004 from Institute for Applied Physics at the Darmstadt University of Technology working on in the field of non-linear dynamics on time series analysis and noise-induced pattern formation.

    After his PhD, he started his postdoctoral work at the German Cancer Research Center, Heidelberg, Germany. where he was leading the group 'Applied Systems Biology' in the division of Prof. Roland Eils. The research focus has been on optimal experiment design of biological experiments, large-scale modeling of protein signaling pathways, stochastic simulation of the chemical master equation and the reverse engineering of dynamic regulatory networks.


    FRIAS Project

    The group of Hauke Busch focuses on the development and verification of mathematical models for cellular behavior from an initial stimulus to the final phenotype.
    In a systems biology approach we combine experimental research on cell-cell communication with the development of appropriate multi-scale dynamic models to investigate the necessary and sufficient control points that lead to cell proliferation, differentiation, migration or death.
    We adapt concepts from non-linear dynamics and complex systems to develop appropriate dynamic models unraveling self-organizing properties in cellular behavior. Such behavior in a multicellular environment is most likely the results of time-sequential events, involving protein signaling and gene regulation in feedback-entangled processes lasting several hours.
    Systems theory suggests that the slowest evolving variables determine the long term outcome of a system.
    In a biological context, it is thus the change in gene expression that reflects the macroscopic decision of a cell.
    Formalizing these ideas in a dynamic modeling approach, we will use abstract neural network approaches to reconstruct the dynamic control logic of cellular decision processes based on gene expression kinetics. Time-resolved experimental data will be recorded in our lab under well defined cell culture and context-dependent conditions. Data will be collected on the cell population level using DNA microarrays and RT-PCR as well as on the single cell level by time-lapse microscopy.

    Selected Publications

    1. A. Singh, J.M. Nascimento, S. Kowar, H. Busch, M. Börries: Boolean approach to signalling pathway modelling in HGF-induced keratinocyte migration. Bioinformatics, 2012; 28 (18); i495-i501.
    2. A. Heinemann, Y. He, M. Börries, H. Busch, L. Bruckner-Tuderman, C. Has: Induction of Phenotype Modifying Cytokines by FERMT1 Mutations Hum Mutat, 2011; 32 (4): 397-406
    3. J. Bachmann, A. Raue, M. Schilling, H. Busch, J. Timmer, U. Klingmüller: Division of Labor by Dual Feedback Regulators Controls JAK2/STAT5 Signaling over Broad Ligand Range Mol Syst Biol, 2011; 7: 516
    4. N. Bhattacharya, S. Diener, S. Häbe, H. Busch, A. Habermann, M. Mertens: Nurse-like cells show deregulated expression of genes involved in immunocompetence Brit J Haematol, 2011 (in Druck)
    5. S. Mesecke, D. Urlaub, H. Busch, R. Eils, C. Watzl: Integration of Activating and Inhibitory Receptor Signaling by Regulated Phosphorylation of Vav1 in Immune Cells Science Signaling, 2011; 4 (175): ra36
    6. A. Riehl, T. Bauer, B. Brors, H. Busch, R. Mark, J. Nemeth, C. Gebhardt, A. Bierhaus, P. Nawroth, R. Eils, R. Konig, P. Angel, J. Hess: Identification of the Rage-dependent gene regulatory network in a mouse model of skin inflammation Bmc Genomics, 2010; 11: 537
    7. T. Maiwald, A. Schneider, H. Busch, S. Sahle, N. Gretz, T.S. Weiss, U. Kummer, U. Klingmuller: Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon alpha-induced early antiviral signalling Febs Journal, 2010; 277 (22): 4741-4754
    8. H. Busch, D. Camacho, Z. Rogon, K. Breuhahn, P. Angel, R. Eils and A. Szabowski, Gene Network Dynamics controlling Keratinocyte Migration, Mol Syst Biol, 4, 199 (2008).
    9. H. Busch, W. Sandmann and V. Wolf, A numerical aggregation algorithm for the enzyme-catalyzed substrate conversion, in Lecture Notes in Computer Science 4210, 298 (2006).
    10. E. Glatt, H. Busch, F. Kaiser and A. Zaikin, Noise-memory induced excitability and pattern formation in oscillatory neural models, Phys. Rev. E 73, 026216 (2006).
    11. H. Busch and R. Eils, Systems Biology, in Encyclopedia of Molecular Cell Biology and Molecular Medicine, 14, 123, (2005).
    12. H. Busch and M.-Th. Hütt, Scale-dependence of spatiotemporal filters inspired by cellular automata, Int. J. Bifurcation Chaos, 14, 1957, (2004).
    13. H. Busch, J. Garcia-Ojalvo, and F. Kaiser, Influence of spatiotemporal 1/f -noise on structure formation in excitable media, Proc. SPIE, 5114, 468, (2003).
    14. H. Busch and F. Kaiser, Influence of spatiotemporally correlated noise on structure formation in excitable media, Phys. Rev. E, 67, 041105, (2003)


    Group picture