Seminar Computational Life Science

Seminar Computational Life Science

Students will be able to independently develop the contents of scientific publications from at least two different fields of Computational Modelling and Simulation and present them to third parties in a comprehensible manner, and critically analyse the acquired knowledge. They are able to critically analyse and communicate the application of computational modelling methods in two different application areas and to recognise cross-application approaches.


Analysis and discussion of scientific publications on a topic of the student's choice in the fields of Computational Life Sciences.

Program / Module

M.Sc. Computational Modeling and Simulation
Module: CMS-SEM - Literature Review in Computational Modelling

Summer Term

Seminar: Thursdays, 14:50-16:20h, APB


2 SWS seminar, self-study

2,5 credits


Oral presentation ("Referat") of 30 minutes

Registration to the course

For students of the Master program "Computational Modeling and Simulation: via CampusNet SELMA


Lecture: Prof. Ivo F. Sbalzarini

Teaching language: ENGLISH

Seminar Papers

Click here to see the list of suggested seminar papers to choose from

Seminar Schedule

  • May 9: (only 14:50 - 15:35 h)
    RootNav: Navigating Images of Complex Root Architectures. Michael P. Pound, Andrew P. French, Jonathan A. Atkinson, Darren M. Wells, Malcolm J. Bennett, and Tony Pridmore*. Plant Physiology, 2013, Vol. 162, pp. 1802–1814.
    — Peter Molenaar

  • May 16: No seminar

  • May 23:
    Shafique, K. & Shah, M. A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 27, 51–65 (2005).
    — Julian Mendez

    Ilya Shmulevich, Edward R. Dougherty, Seungchan Kim and Wei Zhang. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18(2):261-274, 2002.
    — Xinjing Jiang

    Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE T. Bio-Med. Eng., 57(4):841–852, April 2010.
    — Felix Eberhardt

  • May 30: Public holiday - no lectures

  • Jun 6:
    Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Siân Culley, Mauricio Rocha-Martins, Fabián Segovia-Miranda, Caren Norden, Ricardo Henriques, Marino Zerial, Michele Solimena, Jochen Rink, Pavel Tomancak, Loic Royer, Florian Jug & Eugene W. Myers Content-aware image restoration: pushing the limits of fluorescence microscopy, Nature methods 15(12):1090, 2018.
    — Fahad Fareed

    Data‐driven modelling of a gene regulatory network for cell fate decisions in the growing limb bud. M Uzkudun, L Marcon, J Sharpe. Molecular systems biology 11 (7), 815
    — Bianca Güttner

    C. H. Wolters, A. Anwander, X. Tricoche, D. Weinstein, M. A. Koch, and R. S. MacLeod. Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling. NeuroImage, 30:813–826, 2006.
    — Hamzah Ahmed Khan

  • Jun 20: OUTPUT.DD - day of the computer science faculty - no lectures.

  • Jun 27:
    Godinez, W.J. et al. Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences. Med. Image Anal. 13, 325–342 (2009).
    — Nithya Bhasker

    Olivo-Marin, J.-C. Extraction of spots in biological images using multiscale products. Pattern Recognit. 35, 1989–1996 (2002).
    — Leonardo Invernizzi

    J. S. van Zon and P. R. ten Wolde: Green’s-function reaction dynamics: A particle-based approach for simulating biochemical networks in time and space. J. Chem. Phys., 123:234910, 2005.
    — Sia Hranova

  • Jul 4:
    A. Dufour, R. Thibeaux, E. Labruyere, N. Guillen, and J.-C. Olivo-Marin. 3-D active meshes: Fast discrete deformable models for cell tracking in 3-D time-lapse microscopy. IEEE Trans. Image Process., 20(7):1925–1937, 2011.
    — Soorya M

    Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells F Buettner, KN Natarajan, FP Casale, V Proserpio, A Scialdone, FJ Theis, ... Nature biotechnology 33 (2), 155
    — Qing Cao

    Accurate coarse-grained modeling of red blood cells IV Pivkin, GE Karniadakis Physical review letters 101 (11), 118105
    — Siddique Akbar

  • Jul 11:
    S. Verma, G. Novati, and P. Koumoutsakos, “Efficient collective swimming by harnessing vortices through deep reinforcement learning," Proceedings of the national academy of sciences, p. 201800923, 2018.
    — Trevor D’Silva

    V. Garcia, M. Birbaumer, and F. Schweitzer. Testing an agent-based model of bacterial cell motility: How nutrient concentration affects speed distribution. Eur. Phys. J. B, 82(3-4):235–244, 2011.
    — Paul Bustos

    J. R. Karr, J. C. Sanghvi, D. N. Macklin, M. V. Gutschow, J. M. Jacobs, B. Bolival Jr., N. Assad-Garcia, J. I. Glass, and M. W. Covert. A whole-cell computational model predicts phenotype from genotype. Cell, 150:389–401, 2012.
    — Abdulbagi Mohammed