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 Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020). https://arxiv.org/pdf/2010.11929/1000 Deep Learning 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. https://arxiv.org/pdf/1505.04597 U-Net, Deep Learning, Biomedical image segmentation 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 . https://arxiv.org/pdf/1804.03999 Attention U-Net 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. https://arxiv.org/pdf/2306.04905 Graph neural networks, U-Net 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. 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 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. https://www.nature.com/articles/s41586-019-1799-6 AI system for breast cancer screening 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. https://proceedings.neurips.cc/paper_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf Big self-supervised models 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). https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf Big self-supervised models 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. 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 Wetzker, C. 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 https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/ multi-scale microscopy, zebrafish 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. https://www.nature.com/articles/nprot.2016.105 segmentation, data analysis, image based biology  Gardey E,  et al. (2024) Selective uptake into inflamed human intestinal tissue and immune cell targeting by wormlike polymer micelles. Small 20(21), 2470162. 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 S ,  Cheeseman BL ,  Sbalzarini IF ,  Müller 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 T ,  Painter 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. 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 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