English Intern
Rudolf-Virchow-Zentrum - Center for Integrative and Translational Bioimaging

Zimmer Group/ Maschinelle Biophotonik

>>> Still hiring : please see "Jobs" below <<<

Biomedical discoveries are often enabled by technological advances that originate from other fields, such as engineering, physics or computer science. Our laboratory’s mission is to develop and apply methods and ideas from physics, machine learning and related fields to important biomedical questions with a high translational impact.

Prior to the creation of the Machine Biophotonics lab in 2024, our activity (at the Pasteur Institute in Paris) has focused on the following main areas:

  • Super-resolution microscopy: we have developed experimental and computational methods for single molecule microscopy, a technique that allows to characterize biological structures at near molecular resolution 1–4. We have applied these methods to questions in cell biology and microbiology, for example to better understand the ultrastructure of intermediate filaments5 or how HIV infects host cells6–8.
  • Chromatin biophysics: we have developed computational models based on polymer physics to predict the 3D organization and dynamics of eukaryotic genomes9–12. In combination with super-resolution or live cell microscopy, we have used these models to quantitatively understand the biophysical principles of genome folding13–15.
  • Deep learning for biology: we have embraced deep learning as a powerful means of extracting biological information from unstructured data such as images or text, or of improving limitations of current imaging techniques. For example, we have adapted deep learning methods to accelerate super-resolution microscopy16,17 and 3D CNNs to diagnose heart anomalies in mice18. Recently, we also used large language models to extract epidemilogical information from free text data19.

Much of our work has led to the release of new computational tools and/or data sets that we freely share with the community2–4,16–18,20,21.

In the Machine Biophotonics lab, we now focus on developing experimental and computational methods for phenotypic drug discovery - especially to help find badly needed novel antibiotics22,23. Our approach is to gather expertise in imaging, AI and biology within the same lab. We implement high-throughput and high-resolution microscopy, automation with robots, and develop our own machine learning tools, thereby ensuring efficient cycles of data generation and analysis. Our work is done in close collaboration with our sister lab  at the Pasteur Institute and several other international teams.

See "Research" below for more specific projects.

References:

  1. Lelek, M. et al. Single-molecule localization microscopy. Nat. Rev. Methods Primer 1, 1–27 (2021).
  2. Aristov, A., Lelandais, B., Rensen, E. & Zimmer, C. ZOLA-3D allows flexible 3D localization microscopy over an adjustable axial range. Nat. Commun. 9, 2409 (2018).
  3. Henriques, R. et al. QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ. Nat. Methods 7, 339–340 (2010).
  4. Mueller, F. et al. FISH-quant: automatic counting of transcripts in 3D FISH images. Nat. Methods 10, 277–8 (2013).
  5. Nunes Vicente, F. et al. Molecular organization and mechanics of single vimentin filaments revealed by super-resolution imaging. Sci. Adv. 8, eabm2696 (2022).
  6. Lelek, M. et al. Superresolution imaging of HIV in infected cells with FlAsH-PALM. Proc. Natl. Acad. Sci. U. S. A. 109, 8564–9 (2012).
  7. Lelek, M. et al. Chromatin organization at the nuclear pore favours HIV replication. Nat. Commun. 6, 6483 (2015).
  8. Rensen, E. et al. Clustering and reverse transcription of HIV‐1 genomes in nuclear niches of macrophages. EMBO J. 40, (2021).
  9. Wong, H. et al. A Predictive Computational Model of the Dynamic 3D Interphase Yeast Nucleus. Curr. Biol. CB 22, 1881–90 (2012).
  10. Arbona, J.-M., Herbert, S., Fabre, E. & Zimmer, C. Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations. Genome Biol. 18, 81 (2017).
  11. Sabaté, T., Lelandais, B., Bertrand, E. & Zimmer, C. Polymer simulations guide the detection and quantification of chromatin loop extrusion by imaging. Nucleic Acids Res. 51, 2614–2632 (2023).
  12. Parmar, J. J., Woringer, M. & Zimmer, C. How the Genome Folds: The Biophysics of Four-Dimensional Chromatin Organization. Annu. Rev. Biophys. 48, 231–253 (2019).
  13. Herbert, S. et al. Chromatin stiffening underlies enhanced locus mobility after DNA damage in budding yeast. EMBO J. e201695842 (2017) doi:10.15252/embj.201695842.
  14. Hao, X. et al. Super-resolution visualization and modeling of human chromosomal regions reveals cohesin-dependent loop structures. Genome Biol. 22, 150 (2021).
  15. Sabaté, T. et al. Universal dynamics of cohesin-mediated loop extrusion. 2024.08.09.605990 Preprint at doi.org/10.1101/2024.08.09.605990 (2024).
  16. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).
  17. Ouyang, W. et al. ShareLoc — an open platform for sharing localization microscopy data. Nat. Methods 19, 1331–1333 (2022).
  18. Nguyen, H. et al. Deep learning-based detection of murine congenital heart defects from µCT scans. 2024.04.06.588383 Preprint at doi.org/10.1101/2024.04.06.588383 (2024).
  19. Bizel-Bizellot, G. et al. Extracting circumstances of Covid-19 transmission from free text with large language models. Nat. Commun. 16, 5836 (2025).
  20. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. ImJoy: an open-source computational platform for the deep learning era. Nat. Methods 16, 1199–1200 (2019).
  21. Berger, A. B. et al. High-resolution statistical mapping reveals gene territories in live yeast. Nat. Methods 5, 1031–1037 (2008).
  22. Krentzel, D., Shorte, S. L. & Zimmer, C. Deep learning in image-based phenotypic drug discovery. Trends Cell Biol. 33, 538–554 (2023).
  23. Krentzel, D. et al. Deep learning recognises antibiotic modes of action from brightfield images. bioRxiv 2025–03 (2025).

  • November 2024: ERC Synergy grant.

 We are very grateful for the EU's support to our project (AI4AMR) on accelerating antibiotic drug discovery, together with Ivo Gomperts Boneca and Mark Broenstrup. See the press release.

 

 

 

Our main current projects include:

  • Predicting antibiotic drug MoA by imaging and AI:

Antimicrobial resistance (AMR) is associated to about 5 million deaths per year, calling for the urgent development of antimicrobials with novel modes of action (MoA). Traditional drug discovery methods based on growth inhibition screens are failing to address this need at the required pace, thus innovative approaches are required. We aim to accelerate antibiotic drug discovery by developing a pipeline to determine the MoA of candidate compounds from images of bacteria exposed to genetic and chemical perturbations. In our lab, this project leverages high-throughput microscopy, CRISPRi to generate inducible mutants, and deep learning-based image analysis (as illustrated in Krentzel et al. biorxiv 2025).

Going forward, we plan to develop multimodal models that combine different types of images with chemical information and/or metabolomics data, as well as explainable AI methods to extract biologically meaningful information from the trained models.

This effort is part of AI4AMR, a collaboration with the teams of Ivo Boneca (Institut Pasteur, Paris) and Mark Brönstrup (Helmholtz Institute, Braunschweig) supported by an ERC Synergy grant. In this project, we plan to study at least 7 bacterial species including major pathogens, over 80,000 mutants and nearly 30,000 drug treatments, using high-throughput imaging and metabolomics workflows. By analyzing this data set we hope to be able to accelerate the discovery of novel antibiotics and the identification of their MoA and prepare for the advent of a new pandemic bacterial pathogen (pathogen X).   

More details are here.

  • Generative AI to find novel antibiotics:

Drugs (including antibiotics) occupy only a tiny fraction of the astronomically vast chemical space of small molecules (a common estimate is 10^60). Neither experimental drug screening nor traditional in silico screens can systematically analyze such large spaces. We plan to explore the use of generative AI methods to guide the synthesis of new compounds with desirable properties, e.g. against bacterial growth. These compounds will then be studied by imaging and other techniques to experimentally validate them and determine their MoA.

  • Accelerating clinical diagnosis of bacteria infections by imaging and AI:

Sepsis is a potentially life-threatening condition that requires rapid identification of the pathogens to initiate targeted antibiotic therapy. We plan to explore the potential of deep learning to improve and/or speed up the diagnosis of bacterial infections in blood from stained or unstained microscopy images. Once validated, we aim to integrate this technology into real-world clinical workflows to enable faster and more accessible bacterial diagnostics, particularly in resource-limited settings.

This project is in collaboration with the teams of Christoph Schoen and Christoph Spahn and will benefit from their expertise in clinical diagnosis and microbiology.   

We are open to exploring additional related directions with coworkers or collaborators as we are growing the team.

Preprints (selected):

Recent publications (selected) :

Full publication list

For a full list of publications, see the Google Scholar profile here

CV of Christophe Zimmer:

  • starting 2024: W3 Professor, Rudolf Virchow Center, University of Würzburg, Germany
  • since 2021: Professor, Institut Pasteur, Paris, France
  • 2020 – 2023: Director of the Computational Biology department, Institut Pasteur, France
  • 2015 – 2016: Visiting Scholar, University of California at Berkeley, USA
  • 2010 – 2021: Research Director, Institut Pasteur, France
  • 2009: Habilitation to direct research (HDR), University Paris 11, France
  • since 2008: Head of Imaging and Modeling Unit, Institut Pasteur
  • 2003 – 2007:  Staff scientist, Institut Pasteur, Paris, France
  • 2000 – 2002:  Postdoctoral researcher, Institut Pasteur, Paris, France
  • 1998 – 2000: Assistant research geophysicist, Space Physics Group, University of California Los Angeles, USA
  • 1997: PhD, Astrophysics and Space Techniques, University Paris 7, France

As of June 2025, we have openings for:

- 6 postdocs,

- 2 PhD students and 

- 2 to 4 Master students

We expect to fill most of these positions by the end of 2025.

We are looking for highly motivated coworkers with a solid background in one or more of the following fields (or related fields):

  • AI, computer science, applied mathematics, computational biology, bioinformatics
  • Physics, biophysics, engineering, microscopy, optics 
  • Cell biology, microbiology

We expect strong commitment, a good team spirit and fluency in English (written and spoken).

If this applies to you and you are interested in joining our lab, please send the following as a single PDF file to: christophe[dot]zimmer[at]uni-wuerzburg[dot]de with a copy to: inka[dot]robinson[at]uni-wuerzburg[dot]de

  • A cover letter explaining why you would like to join us and have the relevant skills
  • A detailed CV including a publication list and a summary of your past research achievements, if relevant (5 pages max)
  • The names of at least three references, preferably former or current supervisors
  • Copies of transcripts and diplomas

Applications will be reviewed on a rolling basis until the positions are filled.

We gratefully acknowledge generous funding by :

-the University of Würzburg

-the Rudolf Virchow Center

-the Bavarian Distinguished Professorship Programm

-the EU ERC Synergy program 

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