Systems Biology of Immunology Seminar
This is a resource for students in the Systems Biology of Immunology Seminar in our group. Here we have listed a number of papers that are suggestions for your presentation.
Paper suggestions: image analysis
Here you can find a list of suggested papers that involve image analysis. You can also find suitable papers on your own (ask Thilo Figge).
If you are unhappy with a paper you can always ask Thilo Figge to look for a replacement topic.
You will have to write a one-page summary of the paper and send it to Thilo Figge a few days before your presentation.
Deep Learning in Microscopy
| Citation | Link | Keywords |
| Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). | https://www.nature.com/articles/s41592-020-01018-x | Segmentation, Tools, Deep Learning |
| Archit, A., Freckmann, L., Nair, S. et al. Segment Anything for Microscopy. Nat Methods 22, 579–591 (2025). | https://www.nature.com/articles/s41592-024-02580-4 | Segmentation, Tools, Deep Learning |
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Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).
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https://arxiv.org/pdf/2010.11929/1000 |
Deep Learning |
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Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing.
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https://arxiv.org/pdf/1505.04597 |
U-Net, Deep Learning, Biomedical image segmentation |
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Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B. and Glocker, B., 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
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https://arxiv.org/pdf/1804.03999 |
Attention U-Net |
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Jiang, J., Chen, X., Tian, G. and Liu, Y., 2023, April. ViG-UNet: vision graph neural networks for medical image segmentation. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
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https://arxiv.org/pdf/2306.04905 |
Graph neural networks, U-Net |
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Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G. and Naidich, D.P., 2019. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature medicine, 25(6), pp.954-961.
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https://mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63_27.05.19_End-to-end_lung_cancer_screening_with_three-dimens.pdf |
Deep Learning (3D), lung cancer detection, computed tomography |
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McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G.S., Darzi, A. and Etemadi, M., 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788), pp.89-94.
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https://www.nature.com/articles/s41586-019-1799-6 |
AI system for breast cancer screening |
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Chen, T., Kornblith, S., Swersky, K., Norouzi, M. and Hinton, G.E., 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems, 33, pp.22243-22255.
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https://proceedings.neurips.cc/paper_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf |
Big self-supervised models |
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Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T. and Natarajan, V., 2021. Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3478-3488).
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https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf |
Big self-supervised models |
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Zhao, L., Jia, C., Ma, J., Shao, Y., Liu, Z. and Yuan, H., 2023. Medical image segmentation based on self-supervised hybrid fusion network. Frontiers in Oncology, 13, p.1109786.
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf |
Self-supervised medical image segmentation |
Imaging and image analysis in Biological Systems
| Citation | Link | Keywords |
| van Ooijen, Hanna et al.
A thermoplastic chip for 2D and 3D correlative assays combining screening and high-resolution imaging of immune cell responses. Cell Reports Methods, Volume 5, Issue 1, 100965 |
https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00001-3 |
imaging, microwell, correlative imaging, high-resolution, tumor microenvironment, natural killer cell |
| et al. (2025). A fluorescence lifetime separation approach for FLIM live-cell imaging. Journal of Microscopy, 1–16. | https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.70036 | FLIM, live cell imaging |
| Imaging of cellular dynamics from a whole organism to subcellular scale with self-driving, multiscale microscopy
S Daetwyler, H Mazloom-Farsibaf, FY Zhou, D Segal, E Sapoznik, B Chen, ...
Nature methods 22 (3), 569-578
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https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/ | multi-scale microscopy, zebrafish |
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Bray, Mark-Anthony, et al. "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes." Nature protocols 11.9 (2016): 1757-1774.
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https://www.nature.com/articles/nprot.2016.105 |
segmentation, data analysis, image based biology |
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(2024) Selective uptake into inflamed human intestinal tissue and immune cell targeting by wormlike polymer micelles. Small 20(21), 2470162.
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https://www.nature.com/articles/s41592-022-01744-4 |
inflammatory bowel disease (IBD), nanoparticles, shape control |
Paper suggestions: modeling
Here you can find a list of suggested papers that involve modeling. You can also find suitable papers on your own (ask Thilo Figge).
If you are unhappy with a paper you can always ask Thilo Figge to look for a replacement topic.
You will have to write a one-page summary of the paper and send it to Thilo Figge a few days before your presentation.
| Citation | Link | Keywords |
| Zitzmann C and Kaderali L (2018) Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling. Front. Microbiol. 9:1546. doi: 10.3389/fmicb.2018.01546 | https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.01546/full | mathematical modeling, viral kinetics, viral replication, human immunodeficiency virus, Hepatitis C virus, Influenza A virus, antiviral therapy, immune response |
| Almansour S, Dunster JL, Crofts JJ, Nelson MR (2024) Modelling the continuum of macrophage phenotypes and their role in inflammation, Mathematical Biosciences, Volume 377, 109289, ISSN 0025-5564, https://doi.org/10.1016/j.mbs.2024.109289. |
https://www.sciencedirect.com/science/article/pii/S0025556424001494 | mathematical modeling, macrophages and inflammation, Bifurcation analysis, PDE |
| Chathoth K, Fostier L, Martin B, Baysse C, Mahé F (2022) A Multi-Skilled Mathematical Model of Bacterial Attachment in Initiation of Biofilms. Microorganisms,10(4):686. https://doi.org/10.3390/microorganisms10040686 | https://www.mdpi.com/2076-2607/10/4/686 | biofilm, bacterial attachment, mathematical model, stochastic, 2D and 3D |
| Schmid N, Fernandes Del Pozo D, Waegeman W, Hasenauer J (2025) Assessment of uncertainty quantification in universal differential equations. Philos Trans A Math Phys Eng Sci; 383(2293):20240444. doi:10.1098/rsta.2024.0444 | https://pubmed.ncbi.nlm.nih.gov/40172556/ | uncertainty quantification, universal differential equations, scientific machine learning |
| Maddu SCheeseman BLSbalzarini IFMüller CL (2022) Stability selection enables robust learning of differential equations from limited noisy data. Proc. A; 478 (2262): 20210916. https://doi.org/10.1098/rspa.2021.0916 | https://royalsocietypublishing.org/rspa/article/478/2262/20210916/54488/Stability-selection-enables-robust-learning-of | stability selection, sparse regression, PDE identification |
| Heinrich V, Simpson WD 3rd, Francis EA (2017) Analytical Prediction of the Spatiotemporal Distribution of Chemoattractants around Their Source: Theory and Application to Complement-Mediated Chemotaxis. Front Immunol.; 8:578. Published 2017 May 26. doi:10.3389/fimmu.2017.00578 | https://pmc.ncbi.nlm.nih.gov/articles/PMC5445147/ | chemotaxis, reaction–diffusion, mathematical model, single-cell, host–pathogen |
| Niemann J-H, Klus S, Schütte C (2021) Data-driven model reduction of agent-based systems using the Koopman generator. PLoS ONE 16(5): e0250970. https://doi.org/10.1371/journal.pone.0250970 | https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0250970 | ABM, PDEs, data-driven reduction |
| Lorenzi TPainter KJ (2025) Pattern formation within phenotype-structured chemotactic populations. Proc. A 1; 481 (2324): 20250483. https://doi.org/10.1098/rspa.2025.0483 | https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2025.0483 | PDEs, pattern formation, chemotaxis, non-local advection-diffusion-reaction eqs. |
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Kejie C, Kai-Rong O (2021) Random Walks of a Cell With Correlated Speed and Persistence Influenced by the Extracellular Topography, Frontiers in Physics, Volume 9, 10.3389/fphy.2021.719293 |
https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.719293/full | Random walks, complex environments, PRWs, Cell migration |
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Ohno K, Kobayashi Y, Uesaka M et al. (2021) A computational model of the epidermis with the deformable dermis and its application to skin diseases. Sci Rep 11, 13234. https://doi.org/10.1038/s41598-021-92540-1 |
https://www.nature.com/articles/s41598-021-92540-1 | ABM, skin modelling, skin disease, cellular layer |
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