# 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).

<p class="callout danger">If you are unhappy with a paper you can always ask Thilo Figge to look for a replacement topic.</p>

<p class="callout info">You will have to write a one-page summary of the paper and send it to Thilo Figge a few days before your presentation.</p>

### Deep Learning in Microscopy 

<table id="bkmrk-citation-link-keywor" style="border-collapse:collapse;width:120.952%;height:1046.4px;"><colgroup><col style="width:40.754%;"></col><col style="width:25.9921%;"></col><col style="width:33.3731%;"></col></colgroup><thead><tr style="height:29.8px;"><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Citation</span>**</td><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Link</span>**</td><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Keywords</span>**</td></tr></thead><tbody><tr style="height:63.4px;"><td style="height:63.4px;">Stringer, C., Wang, T., Michaelos, M. *et al.* Cellpose: a generalist algorithm for cellular segmentation. *Nat Methods* **18**, 100–106 (2021). </td><td style="height:63.4px;">[https://www.nature.com/articles/s41592-020-01018-x](https://www.nature.com/articles/s41592-020-01018-x)</td><td style="height:63.4px;">Segmentation, Tools, Deep Learning</td></tr><tr style="height:63.4px;"><td style="height:63.4px;">Archit, A., Freckmann, L., Nair, S. *et al.* Segment Anything for Microscopy. *Nat Methods* **22**, 579–591 (2025).</td><td style="height:63.4px;">[https://www.nature.com/articles/s41592-024-02580-4](https://www.nature.com/articles/s41592-024-02580-4)</td><td style="height:63.4px;">Segmentation, Tools, Deep Learning</td></tr><tr style="height:63.4px;"><td style="height:63.4px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." *arXiv preprint arXiv:2010.11929* (2020).</div></div></td><td style="height:63.4px;">[https://arxiv.org/pdf/2010.11929/1000](https://arxiv.org/pdf/2010.11929/1000)</td><td style="height:63.4px;">Deep Learning

</td></tr><tr style="height:113.8px;"><td style="height:113.8px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:113.8px;">[https://arxiv.org/pdf/1505.04597](https://arxiv.org/pdf/1505.04597)</td><td style="height:113.8px;">U-Net, Deep Learning, Biomedical image segmentation

</td></tr><tr style="height:97px;"><td style="height:97px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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*.</div></div></td><td style="height:97px;">[https://arxiv.org/pdf/1804.03999](https://arxiv.org/pdf/1804.03999)</td><td style="height:97px;">Attention U-Net

</td></tr><tr style="height:97px;"><td style="height:97px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:97px;">[https://arxiv.org/pdf/2306.04905](https://arxiv.org/pdf/2306.04905)</td><td style="height:97px;">Graph neural networks, U-Net

</td></tr><tr style="height:113.8px;"><td style="height:113.8px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:113.8px;">[https://mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63\_27.05.19\_End-to-end\_lung\_cancer\_screening\_with\_three-dimens.pdf](https://mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63_27.05.19_End-to-end_lung_cancer_screening_with_three-dimens.pdf)</td><td style="height:113.8px;">Deep Learning (3D), lung cancer detection, computed tomography

</td></tr><tr style="height:97px;"><td style="height:97px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:97px;">[https://www.nature.com/articles/s41586-019-1799-6](https://www.nature.com/articles/s41586-019-1799-6)</td><td style="height:97px;">AI system for breast cancer screening

</td></tr><tr style="height:97px;"><td style="height:97px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:97px;">[https://proceedings.neurips.cc/paper\_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf](https://proceedings.neurips.cc/paper_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf)</td><td style="height:97px;">Big self-supervised models

</td></tr><tr style="height:130.6px;"><td style="height:130.6px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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).</div></div></td><td style="height:130.6px;">[https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi\_Big\_Self-Supervised\_Models\_Advance\_Medical\_Image\_Classification\_ICCV\_2021\_paper.pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf)</td><td style="height:130.6px;">Big self-supervised models

</td></tr><tr style="height:80.2px;"><td style="height:80.2px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0">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.</div></div></td><td style="height:80.2px;">[https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf](https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf)</td><td style="height:80.2px;">Self-supervised medical image segmentation

</td></tr></tbody></table>

### Imaging and image analysis in Biological Systems

<table id="bkmrk-citation-link-keywor-1" style="border-collapse:collapse;width:100%;"><colgroup><col style="width:33.3731%;"></col><col style="width:33.3731%;"></col><col style="width:33.3731%;"></col></colgroup><tbody><tr><td style="height:29.4667px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Citation</span>**</td><td style="height:29.4667px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Link</span>**</td><td style="height:29.4667px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Keywords</span>**</td></tr><tr><td style="height:96.6667px;">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</td><td style="height:96.6667px;">[https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00001-3](https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00001-3)</td><td style="height:96.6667px;">imaging, microwell, correlative imaging, high-resolution, tumor microenvironment, natural killer cell

</td></tr><tr><td><span class="author">Wetzker, C. </span>et al. (<span class="pubYear">2025</span>). <span class="articleTitle">A fluorescence lifetime separation approach for FLIM live-cell imaging</span>. *Journal of Microscopy*, <span class="pageFirst">1</span>–<span class="pageLast">16</span>.</td><td>[https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.70036](https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.70036)</td><td>FLIM, live cell imaging</td></tr><tr><td style="height:79.8667px;">Imaging of cellular dynamics from a whole organism to subcellular scale with self-driving, multiscale microscopy <div class="gs_gray">S Daetwyler, H Mazloom-Farsibaf, FY Zhou, D Segal, E Sapoznik, B Chen, ...</div><div class="gs_gray">Nature methods 22 (3), 569-578</div></td><td style="height:79.8667px;">[https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/](https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/)</td><td style="height:79.8667px;">multi-scale microscopy, zebrafish</td></tr><tr><td style="height:79.8667px;"><div class="gs_citr" tabindex="0">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.</div></td><td style="height:79.8667px;">[https://www.nature.com/articles/nprot.2016.105](https://www.nature.com/articles/nprot.2016.105)</td><td style="height:79.8667px;">segmentation, data analysis, image based biology

</td></tr><tr><td style="height:63.0667px;"><div class="gs_citr" tabindex="0"><div class="gs_citr" tabindex="0"><span class="authors">Gardey E, et al. </span><span class="year">(2024)</span> <span class="title">Selective uptake into inflamed human intestinal tissue and immune cell targeting by wormlike polymer micelles.</span> *Small* 20(21), 2470162.</div></div></td><td style="height:63.0667px;">[https://www.nature.com/articles/s41592-022-01744-4](https://onlinelibrary.wiley.com/doi/10.1002/smll.202306482)</td><td style="height:63.0667px;">inflammatory bowel disease (IBD), nanoparticles, shape control

</td></tr></tbody></table>

# 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).

<p class="callout danger">If you are unhappy with a paper you can always ask Thilo Figge to look for a replacement topic.</p>

<p class="callout info">You will have to write a one-page summary of the paper and send it to Thilo Figge a few days before your presentation.</p>

<table id="bkmrk-citation-link-keywor" style="border-collapse:collapse;width:120.952%;height:902.8px;"><colgroup><col style="width:40.762%;"></col><col style="width:31.3432%;"></col><col style="width:28.012%;"></col></colgroup><thead><tr style="height:29.8px;"><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Citation</span>**</td><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Link</span>**</td><td style="height:29.8px;">**<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">Keywords</span>**</td></tr></thead><tbody><tr style="height:97px;"><td style="height:97px;"><span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">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</span></td><td style="height:97px;">[<span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.01546/full</span>](https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.01546/full)</td><td style="height:97px;"><span style="color:rgb(0,0,0);background-color:rgb(255,255,255);">mathematical modeling, viral kinetics, viral replication, human immunodeficiency virus, Hepatitis C virus, Influenza A virus, <span>antiviral therapy, immune response</span></span></td></tr><tr style="height:113.8px;"><td style="height:113.8px;"><span>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.</span></td><td style="height:113.8px;">[https://www.sciencedirect.com/science/article/pii/S0025556424001494](https://www.sciencedirect.com/science/article/pii/S0025556424001494)</td><td style="height:113.8px;"><span>mathematical modeling, macrophages and inflammation, Bifurcation analysis, PDE</span></td></tr><tr style="height:97px;"><td style="height:97px;">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</td><td style="height:97px;">[https://www.mdpi.com/2076-2607/10/4/686](https://www.mdpi.com/2076-2607/10/4/686)</td><td style="height:97px;"><span>biofilm, bacterial attachment, mathematical model, stochastic, 2D and 3D</span></td></tr><tr style="height:97px;"><td style="height:97px;">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</td><td style="height:97px;">[https://pubmed.ncbi.nlm.nih.gov/40172556/](https://pubmed.ncbi.nlm.nih.gov/40172556/)</td><td style="height:97px;"><span>uncertainty quantification, universal differential equations, scientific machine learning</span></td></tr><tr style="height:147.4px;"><td style="height:147.4px;">Maddu S<span class="al-author-delim">, </span>Cheeseman BL<span class="al-author-delim">, </span>Sbalzarini IF<span class="al-author-delim">, </span>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</td><td style="height:147.4px;">[https://royalsocietypublishing.org/rspa/article/478/2262/20210916/54488/Stability-selection-enables-robust-learning-of](https://royalsocietypublishing.org/rspa/article/478/2262/20210916/54488/Stability-selection-enables-robust-learning-of)</td><td style="height:147.4px;"><span>stability selection, sparse regression, PDE identification</span></td></tr><tr style="height:130.6px;"><td style="height:130.6px;">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</td><td style="height:130.6px;">[https://pmc.ncbi.nlm.nih.gov/articles/PMC5445147/](https://pmc.ncbi.nlm.nih.gov/articles/PMC5445147/)</td><td style="height:130.6px;"><span>chemotaxis, reaction–diffusion, mathematical model, single-cell, host–pathogen</span></td></tr><tr style="height:80.2px;"><td style="height:80.2px;">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</td><td style="height:80.2px;">[https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0250970](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0250970)</td><td style="height:80.2px;"><span>ABM, PDEs, data-driven reduction</span></td></tr><tr style="height:80.2px;"><td style="height:80.2px;">Lorenzi T<span class="al-author-delim">, </span>Painter KJ (2025) Pattern formation within phenotype-structured chemotactic populations. *Proc. A* 1; 481 (2324): 20250483. https://doi.org/10.1098/rspa.2025.0483</td><td style="height:80.2px;"><a class="waffle-rich-text-link">https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2025.0483</a></td><td style="height:80.2px;"><span>PDEs, pattern formation, chemotaxis, non-local advection-diffusion-reaction eqs.</span></td></tr><tr style="height:29.8px;"><td style="height:29.8px;">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

</td><td style="height:29.8px;">[<span>https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.719293/full</span>](https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.719293/full)</td><td style="height:29.8px;"><span>Random walks, complex environments, PRWs, Cell migration</span></td></tr><tr><td>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

</td><td>[https://www.nature.com/articles/s41598-021-92540-1](https://www.nature.com/articles/s41598-021-92540-1)</td><td><span><span style="font-size:13px;color:#000000;font-weight:normal;text-decoration:none;font-family:Arial;font-style:normal;">ABM, skin modelling, skin disease, cellular layer</span></span></td></tr><tr><td></td><td>  
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