@article{18807,
  abstract     = {Developing tissues interpret dynamic changes in morphogen activity to generate cell type diversity. To quantitatively study bone morphogenetic protein (BMP) signaling dynamics in the mouse neural tube, we developed an embryonic stem cell differentiation system tailored for growing tissues. Differentiating cells form striking self-organized patterns of dorsal neural tube cell types driven by sequential phases of BMP signaling that are observed both in vitro and in vivo. Data-driven biophysical modeling showed that these dynamics result from coupling fast negative feedback with slow positive regulation of signaling by the specification of an endogenous BMP source. Thus, in contrast to relays that propagate morphogen signaling in space, we identify a BMP signaling relay that operates in time. This mechanism allows for a rapid initial concentration-sensitive response that is robustly terminated, thereby regulating balanced sequential cell type generation. Our study provides an experimental and theoretical framework to understand how signaling dynamics are exploited in developing tissues.},
  author       = {Rus, Stefanie and Brückner, David and Minchington, Thomas and Greunz, Martina and Merrin, Jack and Hannezo, Edouard B and Kicheva, Anna},
  issn         = {1534-5807},
  journal      = {Developmental Cell},
  number       = {4},
  pages        = {567--580},
  publisher    = {Cell Press},
  title        = {{Self-organized pattern formation in the developing mouse neural tube by a temporal relay of BMP signaling}},
  doi          = {10.1016/j.devcel.2024.10.024},
  volume       = {60},
  year         = {2025},
}

@misc{20121,
  abstract     = {PyDaddy is an open source package which is a key contribution of the manuscript Nabeel et al, arXiv:2205.02645. The basic scientific premise for this package is to discover the nature of stochasticity in ecological time series datasets. It is well known that the stochasticity can affect the dynamics of ecological systems in counter-intuitive ways. Without understanding the equations (typically, in the form of stochastic differential equations or SDEs, in short) that govern the dynamics of populations or ecosystems, it's challenging to determine the impact of randomness on real datasets. In this manuscript and accompanying package, we introduce a methodology for discovering equations (SDEs) that transforms time series data of state variables into stochastic differential equations. This approach merges traditional stochastic calculus with modern equation-discovery techniques. We showcase the generality of our method through various applications and discuss its limitations and potential pitfalls, offering diagnostic measures to address these challenges.},
  author       = {Nabeel, Arshed and Karichannavar, Ashwin and Palathingal, Shuaib and Jhawar, Jitesh and Brückner, David and Danny Raj, Masila and Guttal, Vishwesha},
  publisher    = {Zenodo},
  title        = {{PyDaddy: A Python Package for Discovering SDEs from Time Series Data}},
  doi          = {10.5281/ZENODO.7137151},
  year         = {2024},
}

@article{14795,
  abstract     = {Metazoan development relies on the formation and remodeling of cell-cell contacts. Dynamic reorganization of adhesion receptors and the actomyosin cell cortex in space and time plays a central role in cell-cell contact formation and maturation. Nevertheless, how this process is mechanistically achieved when new contacts are formed remains unclear. Here, by building a biomimetic assay composed of progenitor cells adhering to supported lipid bilayers functionalized with E-cadherin ectodomains, we show that cortical F-actin flows, driven by the depletion of myosin-2 at the cell contact center, mediate the dynamic reorganization of adhesion receptors and cell cortex at the contact. E-cadherin-dependent downregulation of the small GTPase RhoA at the forming contact leads to both a depletion of myosin-2 and a decrease of F-actin at the contact center. At the contact rim, in contrast, myosin-2 becomes enriched by the retraction of bleb-like protrusions, resulting in a cortical tension gradient from the contact rim to its center. This tension gradient, in turn, triggers centrifugal F-actin flows, leading to further accumulation of F-actin at the contact rim and the progressive redistribution of E-cadherin from the contact center to the rim. Eventually, this combination of actomyosin downregulation and flows at the contact determines the characteristic molecular organization, with E-cadherin and F-actin accumulating at the contact rim, where they are needed to mechanically link the contractile cortices of the adhering cells.},
  author       = {Arslan, Feyza N and Hannezo, Edouard B and Merrin, Jack and Loose, Martin and Heisenberg, Carl-Philipp J},
  issn         = {1879-0445},
  journal      = {Current Biology},
  number       = {1},
  pages        = {171--182.e8},
  publisher    = {Elsevier},
  title        = {{Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts}},
  doi          = {10.1016/j.cub.2023.11.067},
  volume       = {34},
  year         = {2024},
}

@article{15315,
  abstract     = {Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.},
  author       = {Brückner, David and Broedersz, Chase P.},
  issn         = {1361-6633},
  journal      = {Reports on Progress in Physics},
  number       = {5},
  publisher    = {IOP Publishing},
  title        = {{Learning dynamical models of single and collective cell migration: a review}},
  doi          = {10.1088/1361-6633/ad36d2},
  volume       = {87},
  year         = {2024},
}

@article{15024,
  abstract     = {Electrostatic correlations between ions dissolved in water are known to impact their transport properties in numerous ways, from conductivity to ion selectivity. The effects of these correlations on the solvent itself remain, however, much less clear. In particular, the addition of salt has been consistently reported to affect the solution’s viscosity, but most modeling attempts fail to reproduce experimental data even at moderate salt concentrations. Here, we use an approach based on stochastic density functional theory, which accurately captures charge fluctuations and correlations. We derive a simple analytical expression for the viscosity correction in concentrated electrolytes, by directly linking it to the liquid’s structure factor. Our prediction compares quantitatively to experimental data at all temperatures and all salt concentrations up to the saturation limit. This universal link between the microscopic structure and viscosity allows us to shed light on the nanoscale dynamics of water and ions under highly concentrated and correlated conditions.},
  author       = {Robin, Paul},
  issn         = {1089-7690},
  journal      = {Journal of Chemical Physics},
  number       = {6},
  publisher    = {AIP Publishing},
  title        = {{Correlation-induced viscous dissipation in concentrated electrolytes}},
  doi          = {10.1063/5.0188215},
  volume       = {160},
  year         = {2024},
}

@article{17892,
  abstract     = {Enzyme-substrate kinetics form the basis of many biomolecular processes. The interplay between substrate binding and substrate geometry can give rise to long-range interactions between enzyme binding events. Here we study a general model of enzyme-substrate kinetics with restricted long-range interactions described by an exponent −𝛾. We employ a coherent-state path integral and renormalization group approach to calculate the first moment and two-point correlation function of the enzyme-binding profile. We show that starting from an empty substrate the average occupancy follows a power law with an exponent 1/(1−𝛾) over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents −𝛾 on short distances and −(2/3)⁢(2−𝛾) on long distances. The crossover between both regimes scales inversely with the average substrate occupancy. Our work allows associating experimental measurements of bound enzyme locations with their binding kinetics and the spatial conformation of the substrate.},
  author       = {Olmeda, Fabrizio and Rulands, Steffen},
  issn         = {2470-0053},
  journal      = {Physical Review E},
  number       = {2},
  publisher    = {American Physical Society},
  title        = {{Field theory of enzyme-substrate systems with restricted long-range interactions}},
  doi          = {10.1103/PhysRevE.110.024404},
  volume       = {110},
  year         = {2024},
}

@article{17104,
  abstract     = {The homeostasis of epithelial tissue relies on a balance between the self-renewal of stem cell populations, cellular differentiation, and loss. Although this balance needs to be tightly regulated to avoid pathologies, such as tumor growth, the regulatory mechanisms, both cell-intrinsic and collective, which ensure tissue steady-state are still poorly understood. Here, we develop a computational model that incorporates basic assumptions of stem cell renewal into distinct populations and mechanical interactions between cells. We find that the model generates unexpected dynamic features: stem cells repel each other in the bulk tissue and are thus found rather isolated, as in a number of in vivo contexts. By mapping the system onto a gas of passive Brownian particles with effective repulsive interactions, that arise from the generated flows of differentiated cells, we show that we can quantitatively describe such stem cell distribution in tissues. The interaction potential between a pair of stem cells decays exponentially with a characteristic length that spans several cell sizes, corresponding to the volume of cells generated per stem cell division. Our findings may help understanding the dynamics of normal and cancerous epithelial tissues.},
  author       = {Krämer, Johannes C. and Hannezo, Edouard B and Gompper, Gerhard and Elgeti, Jens},
  issn         = {2542-4653},
  journal      = {SciPost Physics},
  number       = {4},
  publisher    = {SciPost Foundation},
  title        = {{Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells}},
  doi          = {10.21468/scipostphys.16.4.097},
  volume       = {16},
  year         = {2024},
}

@misc{23022,
  abstract     = {Contains animations, data, and source code used for the paper "Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells" by the same authors. 


This repository contains all the data and analysis scripts to support the manuscript
The file structure is as follows:
- "analysis" contains all the analysis scripts
- "gfx" and "animations" the figures and supporting animations
- "1-SCpair", "2-SCtissue", "3-BDsimulation", "4-tissue-stochdiv", "6-inertia-effects" all the source code and data for the respective models.},
  author       = {Elgeti, Jens and Krämer, Johannes},
  publisher    = {Zenodo},
  title        = {{Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells}},
  doi          = {10.5281/zenodo.8410956},
  year         = {2024},
}

@article{17123,
  abstract     = {A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.},
  author       = {Brückner, David and Tkačik, Gašper},
  issn         = {1091-6490},
  journal      = {Proceedings of the National Academy of Sciences of the United States of America},
  number       = {23},
  publisher    = {National Academy of Sciences},
  title        = {{Information content and optimization of self-organized developmental systems}},
  doi          = {10.1073/pnas.2322326121},
  volume       = {121},
  year         = {2024},
}

@misc{23023,
  abstract     = {In this repository, you will find the R-scripts used to generate the figures, along with the data necessary to make the plots. You will also find the Python-scripts used to compute the pair-wise distance between embryos, to estimate the surface, the volume, the α-parameter, to identify the closest rigid packing and to generate the morphomap. Additionally, you have access to the base model used with Plantseg to predict and label the cells at the 8-cell stage. Note that a more accurate and more specific model was generated for each embryo based on the curation of at least 5 timepoints.

Figures
The plots in Figure 1-7 and Figure S1-S7 can be generated with the respective files. The code is written in R and requires RStudio to work out-of-the-box. The R scripts are located in figures/. Figures and supplemental figures are generated with the same file (e.g., Figure 1.R generates all the computed panels of Figure 1 and Figure S1). Data required to generate the plots are located in figures/data/. Note that the code to work with the morphomap and the tracking data is located in local packages figures/fabreges.morphomap and figures/fabreges.tracking. An additional package figures/fabreges.generic contains utility functions.

Python packages
For the needs of this study, we developed a series of package for Python located in python-packages/. The packages can be directly imported within your scripts. Along with the packages, you will find 6 scripts used to compute, extract or estimate the data from the source. The folder is organised as follow:

package interfaces is located in python-packages/interfaces/ and can be tested by running the scripts to extract the angles an estimate the surface of contact (compute-angles.py and estimate-contacts.py respectively) in folder python-packages,
package morphomap is located in python-packages/morphomap/ and can be tested by running the script to compute the geometrical distances between embryos, project the data in 2D and compute the volume of the cells (compute-distances.py, process-distances.py and compute-volumes.py respectively) in folder python-packages,
package splinefit is included in morphomap in python-packages/morphomap/splinefit/, but can function independently,
package rbb is located in python-packages/rbb/ and can be tested by running the script to identify the closest rigid packing of an embryo at the 8-cell stage (identify-packing.py) in folder python-packages.
We provide example of segmented data in python-packages/samples/.

CNN
We provide a convolutional neural network (CNN) used in our study, located in cnn. Please note that we optimised the CNN further for each dataset by doing manual curation of at least 5 time points and by retraining the network with those ground truth. The CNN were trained using pytorch-3dunet. The CNN can be used with Plantseg. Currently (plantseg v.1.6.0 and pytorch-3dunet v.1.6.0), the output model from pytorch-3dunet must currently be modified to work with plantseg:

# python
import torch
for model in ('best_checkpoint.pytorch', 'last_checkpoint.pytorch'):
    state = torch.load(model, map_location='cpu')
    state = state['model_state_dict']
    torch.save(state, model)

Requierements
Figures
The figures were generated with R (v. 4.4.0) and RStudio (v. 2024.04.1+748). The following packages must be installed prior to execution:

alphashape3d (v. 1.3.2)
devtools (v. 2.4.5)
ggplot2 (v. 3.5.1)
ggrepel (v. 0.9.5)
rstudioapi (v. 0.16.0)
To install all these packages, run the following command in the RStudio console:

# R
install.packages(c("alphashape3d@1.3.2", "devtools@2.4.5", "ggplot2@3.5.1", "ggrepel@0.9.5", "rstudioapi@0.16"))

More recent version of R, RStudio and the packages may also work (not tested).

Python packages
Python 3.12.3 was used to analyse the data. The scripts may work with more recent version of Python and with Python 3.8 or earlier (not tested). To run the code, you must first install the following packages:

numpy (v. 1.26.4)
imageio (v. 2.34.1)
scipy (v. 1.13.1)
scikit-image (v. 0.23.2)
scikit-learn (v. 1.5.0)
Alternatively, you can create a conda environment:

# Shell script
conda create -y --name fabreges2024 python=3.12.3
conda activate fabreges2024
python -m pip install numpy==1.26.4 imageio==2.34.1 scipy==1.13.1 scikit-image==0.23.2 scikit-learn==1.5.0

More recent version of the dependencies may work (not tested).

Usage
Figures
Open one of the figure script in RStudio and run the entire code. You may have to create a folder output in figures/ if it does not exist.

The code has local dependencies that will be installed on your computer when encountering them for the first time. They start with fabreges.* and are necessary to generate the figures.

Python packages (example)
Compute angles
# Shell script
# Compute a summary (mean, median, s.d.) of the angles in embryo C1 at time 40
python compute-angles.py -s -r 15 -R 20 samples/C1_t040.tif.gz output/C1_t040_angles.csv
# Compute a detailed list of angle values
python compute-angles.py -r 15 -R 20 samples/C1_t041.tif.gz output/C1_t041_angles.csv

Compute distances
# Shell script
# Compute the distance between two embryos
python compute-distances.py -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t041.tif.gz output/distances.csv
# Append another distance measurement
python compute-distances.py -a -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t042.tif.gz output/distances.csv

Compute volumes
# Shell script
# Compute the volume in isotropic data
python compute-volumes.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/volumes.csv

Estimate contacts
# Shell script
# Estimate the contacts and the free surface area
python estimate-contacts.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/contacts.csv

Identify the closest rigid packing
# Shell script
# Assuming the file `output/contacts.csv` exists and contains the output
# from command `estimate-contacts.py`.
# Identify the closest rigid packings
python identify-packing.py output/contacts.csv output/backbones.csv

Generate the seeds for Surface Evolver
# Shell script
# Assuming the file `output/contacts.csv` exists and contains the output
# from command `estimate-contacts.py`, and the file `output/volumes.csv`
# exists and contains the output from command `compute-volumes.py`
# Generate the seeds for Surface Evolver using automatic thresholding
# Automatic threshold will determine the highest cell-cell contact threshold
# that preserves rigidity of the embryo (or 0 if not rigid)
python generate-seed.py output/contacts.csv output/volumes.csv output/seed.fe
# Generate the seeds for Surface Evolver with custom threshold
python generate-seed.py --threshold 0.1 output/contacts.csv output/volumes.csv output/seed.fe

Project in 2D and generate a distance matrix
# Shell script
# Assuming the folder `foo` only contains one or more CSV
# from command `compute-distances.py`.

# Compute the tSNE 2D projection
python process-distances.py -D2 -t output/tsne_2d.csv foo/

# Compute the tSNE 3D projection
python process-distances.py -D3 -t output/tsne_2d.csv foo/

# Generate the distance matrix
python process-distances.py -m output/dismat.csv foo/},
  author       = {Corominas-Murtra, Bernat and   Moghe, Prachiti and Kickuth, Alison and Ichikawa, Takafumi and Iwatani, Chizuru and Tsukiyama, Tomoyuki and Daniel, Nathalie and Gering, Julie and Stokkermans, Anniek and Wolny, Adrian and Kreshuk, Anna and Duranthon, Véronique and Uhlmann, Virginie and Hannezo, Edouard and Hiiragi, Takashi},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Data and software from: Temporal variability and cell mechanics control robustness in mammalian embryogenesis}},
  doi          = {10.5281/zenodo.12819780},
  year         = {2024},
}

@article{17269,
  abstract     = {The directed migration of epithelial cell collectives through coordinated movements plays a crucial role in various physiological processes and is increasingly understood at the level of large confluent monolayers. However, numerous processes rely on the migration of small groups of polarized epithelial clusters in complex environments, and their responses to external geometries remain poorly understood. To address this, we cultivate primary epithelial keratocyte tissues on adhesive microstripes to create autonomous epithelial clusters with well-defined geometries. We show that their migration efficiency is strongly influenced by the contact geometry and the orientation of cell–cell contacts with respect to the direction of migration. A combination of velocity and polarity alignment with contact regulation of locomotion in an active matter model captures quantitatively the experimental data. Furthermore, we predict that this combination of rules enables efficient navigation in complex geometries, which we confirm experimentally. Altogether, our findings provide a conceptual framework for extracting the interaction rules of active systems from their interaction with physical boundaries, as well as design principles for collective navigation in complex microenvironments.},
  author       = {Vercruysse, Eléonore and Brückner, David and Gómez-González, Manuel and Remson, Alexandre and Luciano, Marine and Kalukula, Yohalie and Rossetti, Leone and Trepat, Xavier and Hannezo, Edouard B and Gabriele, Sylvain},
  issn         = {1745-2481},
  journal      = {Nature Physics},
  pages        = {1492--1500},
  publisher    = {Springer Nature},
  title        = {{Geometry-driven migration efficiency of autonomous epithelial cell clusters}},
  doi          = {10.1038/s41567-024-02532-x},
  volume       = {20},
  year         = {2024},
}

@article{18446,
  abstract     = {How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell–mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.},
  author       = {Fabrèges, Dimitri and Corominas-Murtra, Bernat and Moghe, Prachiti and Kickuth, Alison and Ichikawa, Takafumi and Iwatani, Chizuru and Tsukiyama, Tomoyuki and Daniel, Nathalie and Gering, Julie and Stokkermans, Anniek and Wolny, Adrian and Kreshuk, Anna and Duranthon, Véronique and Uhlman, Virginie and Hannezo, Edouard B and Hiiragi, Takashi},
  issn         = {1095-9203},
  journal      = {Science},
  number       = {6718},
  publisher    = {AAAS},
  title        = {{Temporal variability and cell mechanics control robustness in mammalian embryogenesis}},
  doi          = {10.1126/science.adh1145},
  volume       = {386},
  year         = {2024},
}

@article{12162,
  abstract     = {Homeostatic balance in the intestinal epithelium relies on a fast cellular turnover, which is coordinated by an intricate interplay between biochemical signalling, mechanical forces and organ geometry. We review recent modelling approaches that have been developed to understand different facets of this remarkable homeostatic equilibrium. Existing models offer different, albeit complementary, perspectives on the problem. First, biomechanical models aim to explain the local and global mechanical stresses driving cell renewal as well as tissue shape maintenance. Second, compartmental models provide insights into the conditions necessary to keep a constant flow of cells with well-defined ratios of cell types, and how perturbations can lead to an unbalance of relative compartment sizes. A third family of models address, at the cellular level, the nature and regulation of stem fate choices that are necessary to fuel cellular turnover. We also review how these different approaches are starting to be integrated together across scales, to provide quantitative predictions and new conceptual frameworks to think about the dynamics of cell renewal in complex tissues.},
  author       = {Corominas-Murtra, Bernat and Hannezo, Edouard B},
  issn         = {1084-9521},
  journal      = {Seminars in Cell & Developmental Biology},
  keywords     = {Cell Biology, Developmental Biology},
  pages        = {58--65},
  publisher    = {Elsevier},
  title        = {{Modelling the dynamics of mammalian gut homeostasis}},
  doi          = {10.1016/j.semcdb.2022.11.005},
  volume       = {150-151},
  year         = {2023},
}

@inbook{12428,
  abstract     = {The mammary gland consists of a bilayered epithelial structure with an extensively branched morphology. The majority of this epithelial tree is laid down during puberty, during which actively proliferating terminal end buds repeatedly elongate and bifurcate to form the basic structure of the ductal tree. Mammary ducts consist of a basal and luminal cell layer with a multitude of identified sub-lineages within both layers. The understanding of how these different cell lineages are cooperatively driving branching morphogenesis is a problem of crossing multiple scales, as this requires information on the macroscopic branched structure of the gland, as well as data on single-cell dynamics driving the morphogenic program. Here we describe a method to combine genetic lineage tracing with whole-gland branching analysis. Quantitative data on the global organ structure can be used to derive a model for mammary gland branching morphogenesis and provide a backbone on which the dynamics of individual cell lineages can be simulated and compared to lineage-tracing approaches. Eventually, these quantitative models and experiments allow to understand the couplings between the macroscopic shape of the mammary gland and the underlying single-cell dynamics driving branching morphogenesis.},
  author       = {Hannezo, Edouard B and Scheele, Colinda L.G.J.},
  booktitle    = {Cell Migration in Three Dimensions},
  editor       = {Margadant, Coert},
  isbn         = {9781071628867},
  issn         = {1940-6029},
  pages        = {183--205},
  publisher    = {Springer Nature},
  title        = {{A Guide Toward Multi-scale and Quantitative Branching Analysis in the Mammary Gland}},
  doi          = {10.1007/978-1-0716-2887-4_12},
  volume       = {2608},
  year         = {2023},
}

@article{12710,
  abstract     = {Surface curvature both emerges from, and influences the behavior of, living objects at length scales ranging from cell membranes to single cells to tissues and organs. The relevance of surface curvature in biology is supported by numerous experimental and theoretical investigations in recent years. In this review, first, a brief introduction to the key ideas of surface curvature in the context of biological systems is given and the challenges that arise when measuring surface curvature are discussed. Giving an overview of the emergence of curvature in biological systems, its significance at different length scales becomes apparent. On the other hand, summarizing current findings also shows that both single cells and entire cell sheets, tissues or organisms respond to curvature by modulating their shape and their migration behavior. Finally, the interplay between the distribution of morphogens or micro-organisms and the emergence of curvature across length scales is addressed with examples demonstrating these key mechanistic principles of morphogenesis. Overall, this review highlights that curved interfaces are not merely a passive by-product of the chemical, biological, and mechanical processes but that curvature acts also as a signal that co-determines these processes.},
  author       = {Schamberger, Barbara and Ziege, Ricardo and Anselme, Karine and Ben Amar, Martine and Bykowski, Michał and Castro, André P.G. and Cipitria, Amaia and Coles, Rhoslyn A. and Dimova, Rumiana and Eder, Michaela and Ehrig, Sebastian and Escudero, Luis M. and Evans, Myfanwy E. and Fernandes, Paulo R. and Fratzl, Peter and Geris, Liesbet and Gierlinger, Notburga and Hannezo, Edouard B and Iglič, Aleš and Kirkensgaard, Jacob J.K. and Kollmannsberger, Philip and Kowalewska, Łucja and Kurniawan, Nicholas A. and Papantoniou, Ioannis and Pieuchot, Laurent and Pires, Tiago H.V. and Renner, Lars D. and Sageman-Furnas, Andrew O. and Schröder-Turk, Gerd E. and Sengupta, Anupam and Sharma, Vikas R. and Tagua, Antonio and Tomba, Caterina and Trepat, Xavier and Waters, Sarah L. and Yeo, Edwina F. and Roschger, Andreas and Bidan, Cécile M. and Dunlop, John W.C.},
  issn         = {1521-4095},
  journal      = {Advanced Materials},
  number       = {13},
  publisher    = {Wiley},
  title        = {{Curvature in biological systems: Its quantification, emergence, and implications across the scales}},
  doi          = {10.1002/adma.202206110},
  volume       = {35},
  year         = {2023},
}

@article{12818,
  abstract     = {The multicellular organization of diverse systems, including embryos, intestines, and tumors relies on coordinated cell migration in curved environments. In these settings, cells establish supracellular patterns of motion, including collective rotation and invasion. While such collective modes have been studied extensively in flat systems, the consequences of geometrical and topological constraints on collective migration in curved systems are largely unknown. Here, we discover a collective mode of cell migration in rotating spherical tissues manifesting as a propagating single-wavelength velocity wave. This wave is accompanied by an apparently incompressible supracellular flow pattern featuring topological defects as dictated by the spherical topology. Using a minimal active particle model, we reveal that this collective mode arises from the effect of curvature on the active flocking behavior of a cell layer confined to a spherical surface. Our results thus identify curvature-induced velocity waves as a mode of collective cell migration, impacting the dynamical organization of 3D curved tissues.},
  author       = {Brandstätter, Tom and Brückner, David and Han, Yu Long and Alert, Ricard and Guo, Ming and Broedersz, Chase P.},
  issn         = {2041-1723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{Curvature induces active velocity waves in rotating spherical tissues}},
  doi          = {10.1038/s41467-023-37054-2},
  volume       = {14},
  year         = {2023},
}

@article{13261,
  abstract     = {Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the pairwise motion of distal chromosomal elements such as enhancers and promoters is essential and necessitates dynamic fluidity. Here, we used a live-imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output while systematically varying the genomic separation between these two DNA loci. Our analysis reveals the coexistence of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation leading to long-ranged correlations. Thus, encounter times of DNA loci are much less dependent on genomic distance than predicted by existing polymer models, with potential consequences for eukaryotic gene expression.},
  author       = {Brückner, David and Chen, Hongtao and Barinov, Lev and Zoller, Benjamin and Gregor, Thomas},
  issn         = {1095-9203},
  journal      = {Science},
  number       = {6652},
  pages        = {1357--1362},
  publisher    = {American Association for the Advancement of Science},
  title        = {{Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome}},
  doi          = {10.1126/science.adf5568},
  volume       = {380},
  year         = {2023},
}

@article{13971,
  abstract     = {When in equilibrium, thermal forces agitate molecules, which then diffuse, collide and bind to form materials. However, the space of accessible structures in which micron-scale particles can be organized by thermal forces is limited, owing to the slow dynamics and metastable states. Active agents in a passive fluid generate forces and flows, forming a bath with active fluctuations. Two unanswered questions are whether those active agents can drive the assembly of passive components into unconventional states and which material properties they will exhibit. Here we show that passive, sticky beads immersed in a bath of swimming Escherichia coli bacteria aggregate into unconventional clusters and gels that are controlled by the activity of the bath. We observe a slow but persistent rotation of the aggregates that originates in the chirality of the E. coli flagella and directs aggregation into structures that are not accessible thermally. We elucidate the aggregation mechanism with a numerical model of spinning, sticky beads and reproduce quantitatively the experimental results. We show that internal activity controls the phase diagram and the structure of the aggregates. Overall, our results highlight the promising role of active baths in designing the structural and mechanical properties of materials with unconventional phases.},
  author       = {Grober, Daniel and Palaia, Ivan and Ucar, Mehmet C and Hannezo, Edouard B and Šarić, Anđela and Palacci, Jérémie A},
  issn         = {1745-2481},
  journal      = {Nature Physics},
  pages        = {1680--1688},
  publisher    = {Springer Nature},
  title        = {{Unconventional colloidal aggregation in chiral bacterial baths}},
  doi          = {10.1038/s41567-023-02136-x},
  volume       = {19},
  year         = {2023},
}

@article{14277,
  abstract     = {Living tissues are characterized by an intrinsically mechanochemical interplay of active physical forces and complex biochemical signaling pathways. Either feature alone can give rise to complex emergent phenomena, for example, mechanically driven glassy dynamics and rigidity transitions, or chemically driven reaction-diffusion instabilities. An important question is how to quantitatively assess the contribution of these different cues to the large-scale dynamics of biological materials. We address this in Madin-Darby canine kidney (MDCK) monolayers, considering both mechanochemical feedback between extracellular signal-regulated kinase (ERK) signaling activity and cellular density as well as a mechanically active tissue rheology via a self-propelled vertex model. We show that the relative strength of active migration forces to mechanochemical couplings controls a transition from a uniform active glass to periodic spatiotemporal waves. We parametrize the model from published experimental data sets on MDCK monolayers and use it to make new predictions on the correlation functions of cellular dynamics and the dynamics of topological defects associated with the oscillatory phase of cells. Interestingly, MDCK monolayers are best described by an intermediary parameter region in which both mechanochemical couplings and noisy active propulsion have a strong influence on the dynamics. Finally, we study how tissue rheology and ERK waves produce feedback on one another and uncover a mechanism via which tissue fluidity can be controlled by mechanochemical waves at both the local and global levels.},
  author       = {Boocock, Daniel R and Hirashima, Tsuyoshi and Hannezo, Edouard B},
  issn         = {2835-8279},
  journal      = {PRX Life},
  number       = {1},
  publisher    = {American Physical Society},
  title        = {{Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers}},
  doi          = {10.1103/prxlife.1.013001},
  volume       = {1},
  year         = {2023},
}

@misc{14279,
  abstract     = {The zip file includes source data used in the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration", as well as a representative Jupyter notebook to reproduce the main figures. Please see the preprint on bioRxiv and the DOI link there to access the final published version. Note the title change between the preprint and the published manuscript.
A sample script for particle-based simulations of collective chemotaxis by self-generated gradients is also included (see Self-generated_chemotaxis_sample_script.ipynb) to generate exemplary cell trajectories. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt.},
  author       = {Ucar, Mehmet C},
  publisher    = {Zenodo},
  title        = {{Source data for the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration"}},
  doi          = {10.5281/ZENODO.8133960},
  year         = {2023},
}