Ascona Workshop: Spatial and temporal statistical modeling in molecular biology

8-13 September 2024 at Congressi Stefano Franscini on Monte Verità in Ascona, CH

Start date

2024-09-08

Organizers: Niko Beerenwinkel (ETH), Valentina Boeva (ETH), Peter Bühlmann (ETH), Wolfgang Huber (EMBL)

Scope

Living systems operate in space and time, and recent developments in technologies, including spatial omics of tissues and organs, cell-scale structural biology, and planet-scale molecular sampling, are creating exciting new challenges for mathematical modeling, statistical inference and data science. This workshop has two main objectives:

  • to bring together scientists close to data generation and scientists with advanced modeling and theoretical perspectives, and
  • to explore conceptual and methodological analogies between different data types and spatiotemporal scales.

At the micrometer scale, it is now becoming possible to bridge from individual macromolecules to whole cells using light and electron microscopy and tomography, merging structural biology with cell biology; at the millimeter scale, we can image hundreds of biomolecules in tissues and organ(oid)s using multiplexed immunohistochemistry, fluorescence hybridization or spatially resolved mass spectrometry; at kilo- and megameter scale, systematic sampling of soil, sediment, (waste) water, air, or of model organisms characterizes species diversity, and molecular and genetic variation.

This progress in data acquisition creates novel elementary challenges including data size, exploration and visualization, calibration and harmonization. There are also new conceptual and theoretical challenges to develop suitable mathematical descriptions and models of the underlying biological systems. How can they optimally and practically be fit to the data? How can we use the results to understand the underlying dynamics of the biological systems? What are the important variables (“coarse graining”)? Can we use our models for prediction? How can such insights be used for interventions (e.g., maintenance of health, therapy against disease, bioengineering to improve crop yield, regulation to keep an ecosystem intact)?

These questions will be explored by an interdisciplinary group of speakers and participants.

Program

day time what title
Sunday September 8th 15:00-19:00 Arrival and Check-in
18:00-19:00 Welcome Drinks
19:00 Dinner
Monday September 9th 8:45–9:00 Welcome
9:00–9:50 Keynote talk: Shila Ghanzanfar Multiscale approaches for understanding single cell spatial omics data
9:50-10:15 Contributed talk: Helena Crowell Colorectal cancer through the lense of whole transcriptome imaging
10:15-10:45 Coffee break
10:45-11:35 Keynote talk: Andreas Moor Systematic perturbation of cellular interactions in the tumor microenvironment
11:35-12:00 Contributed talk: Davide Risso Preprocessing and quality control of imaging-based spatial transcriptomics data
12:15 Lunch
Unstructured time
15:30-16:00 Coffee and cake
16:00-16:50 Keynote talk: Stefanie Hicks Scalable computational methods and software for single-cell and spatial data science
17:00-19:00 Poster session 1 (Posters 1-23)
19:00 Dinner
Tuesday September 10th 9:00-9:50 Keynote talk: Lara Urban One Health and Genomics
9:50-10:15 Contributed talk: Constantin Ahlmann-Eltze Analysis of multi-condition single-cell data with latent embedding multivariate regression
10:15-10:45 Coffee break
10:45-11:35 Keynote talk: Ewa Szczurek Modeling Evolutionary, Spatial, Transcriptional, and Cell Type Composition of Tissues
11:35-12:00 Contributed talk: Charlotte Bunne Modeling Cellular Behavior in Space and Time using the Virtual Cell and Artificial Intelligence
12:15 Lunch
14:00 Excursion Hike in the Verzasca Valley
19:00 Dinner
Wednesday September 11th 9:00-9:50 Keynote talk: Peer Bork Microbiome analysis for human and planetary health
9:50-10:15 Contributed talk: Simon van Vliet From molecules to communities: predicting the emergent properties of spatially structured microbial systems
10:15-10:45 Coffee break
10:45-11:10 Contributed talk: David Dreifuss Estimating growth advantages of SARS-CoV-2 variants through Bayesian hierarchical modeling of wastewater sequencing data across space and time
11:10-11:35 Contributed talk: Ben Raphael Alignment and Integration of Spatial Transcriptomics Data
11:35-12:00 Contributed talk: Mark Robinson OPTASO: Optimal annotation of spatial omics data
12:15 Lunch
Unstructured time
15:30-16:00 Coffee and cake
16:00-16:50 Keynote talk: Dmitry Kobak All embedddings are wrong, but some are useful
17:00-18:30 Poster session 2 (Posters 24-47)
18:30 Depart for walk to Conference Dinner Grotto Broggini restaurant
18:45 Bus departing Bus will be waiting on the main road (parking at the entrance of the property)
19:00 Conference Dinner Grotto Broggini restaurant
Thursday September 12th 9:00-9:50 Keynote talk: Anna Kreshuk
9:50-10:15 Contributed talk: Chenchen Zhu Performance comparison of spatially resolved transcriptomics methods at single cell resolution for cell atlasing
10:15-10:45 Coffee break
10:45-11:35 Keynote talk: Virginie Uhlmann Turning morphology into numbers and streamlining bioimage analysis at scale
11:35-12:00 Contributed talk: Ming Yu Spatial transcriptomics analysis of human colon adenomas reveals distinct features of precancer microenvironment associated with tumor progression
12:15 Lunch
Unstructured time
15:30-16:00 Coffee and cake
16:00-16:50 Keynote talk: Maria Christina Gambetta Megabase-range control of neuronal gene transcription in Drosophila
16:50-17:15 Contributed talk: Daniela Corbetta Conformal inference for cell type prediction leveraging the cell ontology
19:00 Dinner
Friday September 13th 9:00-9:50 Keynote talk: Emma Schymanski Tracking Chemical Exposures in Time and Space
9:50-10:15 Contributed talk: Philipp Weiler CellRank 2: Unified fate mapping in multiview single-cell data
10:15-10:45 Coffee break
10:45-11:10 Contributed talk: Phillip Nicol Identifying spatially variable genes by projecting to morphologically relevant one-dimensional curves
11:10-11:35 Contributed talk: Shadi Shafighi Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues
11:35-12:00 Contributed talk: Eloise Withnell SpottedPy uncovers new environmental cues of epithelial-mesenchymal plasticity in cancer
12:15 Lunch
Departure

Keynote Speakers

  • Peer Bork, EMBL
  • Maria Cristina Gambetta, Univ. of Lausanne
  • Shila Ghazanfar, Univ. of Sydney
  • Stephanie Hicks, Johns Hopkins University
  • Dmitry Kobak, Univ. of Tübingen
  • Anna Kreshuk, EMBL
  • Andreas Moor, ETH
  • Emma Schymanski, Univ. of Luxemburg
  • Ewa Szczurek, Univ. of Warsaw, Helmholtz Munich
  • Virginie Uhlmann, Univ. of Zürich
  • Lara Urban, Technical University Munich, Helmholtz Center

Posters

  1. Maialen Arrieta-Lobo – Integrating Tessellation-Based Quantification with Spatial Omics for Improved Gene Colocalization Analysis of Glioblastoma Tumor Microenvironment (Maialen Arrieta-Lobo, Sébastian Lillo, Thomas Daubon and Macha Nikolski)
  2. Benjamin Bancher – Benchmarking cell instance segmentation algorithms in highly multiplexed microscopy images across modalities
  3. Daniela Beisser – Analysing stressor effects on decomposer communities in freshwater ecosytems
  4. Joana P. Bernardes – Longitudinal single cell transcription profiling of peripheral blood disentangles shared and biologic specific patters of early remission in active IBD patients
  5. Ilaria Billato – Integrating machine learning and omics data to address batch effects in histopathological image analysis for cancer research
  6. Alice Blondel – RNA point cloud segmentation for image-based spatial transcriptomics
  7. Jinseong Bok – Composite hidden Markov models for sequence data with clustered hidden states
  8. Thomas Bonte – Deep learning method for cell cycle phase classification from microscopy data
  9. Giulia Capitoli – Spatially informed sparse Gaussian Graphical Mixture Model to detect latent patterns in mass spectrometry imaging
  10. Pawel Czyz – Estimating growth advantages of SARS-CoV-2 variants through Bayesian hierarchical modeling of wastewater sequencing data across space and time
  11. Thomas Defard – RNA point cloud segmentation for image-based spatial transcriptomics
  12. Alessia Del Panta – Visualizing realized interactions in space
  13. Maciej Dobrzynski – Detection and quantification of emergent collective signalling in time-lapse microscopy images
  14. Yixing Dong – A comprehensive benchmarking on the impact of normalization across various Spatial Transcriptomics technologies
  15. Francesca Drummer – InterScale: Towards Understanding Long-Range Interactions in Spatial Transcriptomics
  16. Martin Emons – spatialFDA - a tool for spatial multi-sample comparisons
  17. Andreas Futschik – Statistical Inference for Time Series Allele Frequency Data
  18. Johannes Gawron – Phylogenetic inference reveals clonal heterogeneity in circulating tumor cell clusters
  19. Krzysztof Gogolewski – Probabilistic modeling of tumor infiltration processes
  20. Luca Gortana – Cell-type deconvolution from spatial transcriptomics data and single-cell-level histology
  21. Samuel Gunz – sosta: a framework to analyse spatial structures from spatial omics data
  22. Nikolai Köhler – Identifying Changes in Subcellular RNA Localization Across Cells
  23. Jack Kuipers – Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies
  24. Thi Kim Hue Nguyen – Structure learning of dynamic graphical models for count data, with an application to single - cell RNA sequencing data
  25. Lennart Opitz – A Comparative Analysis of Spatial Transcriptomics in Colon Cancer Samples: 10x Visium vs. 10x Visium HD Slides
  26. Ahmed Osman – Explainable Machine Learning for Identifying cis-Regulatory Elements Over Development Trajectories
  27. Pratibha Panwar – clustSIGNAL: a method for cell type clustering using Spatially Informed Gene expression with Neighbourhood Adapted Learning
  28. Ellis Patrick – Context is important! Identifying context aware spatial relationships with Kontextual.
  29. Lotte Pollaris – Revealing spatial expression patterns within cells with SPArrOW, a workflow for subcellular resolution spatial transcriptomics assays.
  30. Michael Prummer – SAUCE for a fast and robust detection of spatially variable genes
  31. Auguste Rimaite – Finding SARS-CoV-2 mutational patterns in wastewater NGS data
  32. Mayra Luisa Ruiz Tejada Segura – Nichesphere: Niches of differential cell - cell interactions
  33. Bechara Saade – Exploring Novel Spatial-temporal Models for Nuclear Receptor Activation: A Stability Analysis and Investigation of Oscillatory Solutions
  34. Antonietta Salerno – Unveiling the effects of copper-chelation therapy in Neuroblastoma immune microenvironment with a multi-modal approach
  35. Ela Sauerborn – Detection of hidden antibiotic resistance using real-time genomic technology
  36. Alexander Schönhuth – Generating synthetic human genomes using diffusion models
  37. Christoph Schultheiss – Assessing the overall and partial causal well-specification of nonlinear additive noise models
  38. Swayamshree Senapati – Polymer and Kinetic Modeling Unveils Quantitative Association of Chromatin Conformation and Gene Regulation
  39. Lutecia Servius – Accurate Prediction of Antibody Isotype Distribution During Immune Response Time Course Using Aggregate Data
  40. Nikolay Shvetsov – Graph Neural Networks for Disease Gene Identification: Unveiling Disease-Specific Networks through Link Prediction
  41. Leon Strenger – Graph-based RNA Colocalization Analysis in Subcellular Spatial Transcriptomics Data
  42. Alena van Bömmel – Nonlinear DNA methylation trajectories in aging
  43. Michiel Ver Cruysse – ASAP: a Machine-Learning-Powered Automated Pipeline for Comparative Spatial Analysis of Liver Tissue
  44. Lin Wan – Mean-field modeling and learning of spatial-temporal transcriptome snapshot data
  45. Witold Wolski – Enhancing Mass Spectrometry Imaging Accuracy via Spatially Informed Mass Recalibration
  46. Zhi Zhao – Identification of cell composition-based omics features for cancer prognosis
  47. Norio Zimmermann – Tree inference from single-cell RNA sequencing data

How to participate

Pre-registration is now closed. Following pre-registration, abstracts will be selected by the program committee. Pre-registration is non-binding and at no cost. Successful applications will be invited to register by mid-May.

Dates

Deadline for pre-registration: 05 May 2024, 23:59 CEST
Notification and invitation to register: before 20 May 2024
Deadline for registration: 31 July 2024
Workshop: 8-13 September 2024 (Sunday evening till Friday noon)

Location

Congressi Stefano Franscini, Monte Verità, Ascona, Switzerland
For more information on how to get there, visit https://csf.ethz.ch/monte-verita/by-train.html.
We will provide you with additional travel information shortly.

Registration fees (not due until final registration)

Academia 300 CHF
Industry 900 CHF

Important: Accommodation and full board will be available at the conference venue, and will be charged directly to you. Usually, most but not all conference attendees use this option. The venue is in Ascona, a lively tourist town, and in walking distance to many additional accommodation options.

Contact

Sponsors