Many processes influence boundary sharpness and placement: gene regulatory interactions, spatial averaging, cell sorting to name a few.
Our analysis shows that cellular readout noise is the main determinant of boundary sharpness, i.e. #TZW.
Preprint: doi.org/10.64898/202...
π§΅ 6/6
11.02.2026 09:31 β π 0 π 0 π¬ 0 π 0
By matching simulations with measurement of #TZW & #positional #error, we inferred kinetic and readout noise levels - and found them in the reported range.
This further supports that reliable long-range morphogen patterning is feasible with physiological noise levels:
x.com/DagmarIber/s...
π§΅ 5/6
11.02.2026 09:31 β π 0 π 0 π¬ 1 π 0
We uncover a trade-off regarding cell size:
β’ Larger cells yield sharper boundaries (smaller #TZW)
β’ Smaller cells reduce variability in boundary position between embryos (lower positional error)
The measured cell size in the neural tube perfectly balances boundary sharpness and precision.
π§΅ 4/6
11.02.2026 09:31 β π 0 π 0 π¬ 1 π 0
Our theoretical & computational analysis shows that #TZW is primarily set by #noise in the #cellular #readout process, not by fluctuations in the morphogen gradient itself.
For exponential gradients, a noisy readout threshold naturally yields a position-independent TZW.
π§΅ 3/6
11.02.2026 09:31 β π 0 π 0 π¬ 1 π 0
According to prevailing theory, transition zones should widen exponentially with distance from the morphogen source, due to stochastic effects at low morphogen copy numbers.
#Contrary, we find that #TZW remains about #constant, independent of readout position and developmental timepoint.
π§΅ 2/6
11.02.2026 09:31 β π 0 π 0 π¬ 1 π 0
What determines the sharpness of cell fate boundaries in gradient-based patterning?
We quantified #transition #zone #widths (TZW) across seven progenitor domain boundaries spanning the entire dorsal-ventral axis of the developing #mouse #neural #tube.
Preprint: doi.org/10.64898/202...
π§΅ 1/6
11.02.2026 09:31 β π 4 π 1 π¬ 1 π 0
Many processes influence boundary sharpness and placement: gene regulatory interactions, spatial averaging, cell sorting to name a few.
Our analysis shows that cellular readout noise is the main determinant of boundary sharpness, i.e. #TZW.
Preprint: doi.org/10.64898/202...
π§΅ 6/6
11.02.2026 08:42 β π 1 π 0 π¬ 0 π 0
By matching simulations with measurement of #TZW & #positional #error, we inferred kinetic and readout noise levels - and found them in the reported range.
This further supports that reliable long-range morphogen patterning is feasible with physiological noise levels:
x.com/DagmarIber/s...
π§΅ 5/6
11.02.2026 08:42 β π 1 π 0 π¬ 2 π 0
Paper & poster are available on the COMSOL conference website: www.comsol.com/paper/direct...
05.11.2025 16:13 β π 1 π 0 π¬ 0 π 0
"Within the competition to bring this field to a new level, SimuCell3D is remarkable and will mark a clear evolution of the topic."
π₯Thatβs what the committee said about this #SIBRemarkableOutputs 2024
πDiscover the output: tinyurl.com/53arz2rz
@iberd.bsky.social
18.09.2025 14:04 β π 2 π 1 π¬ 0 π 0
The goal: make it easier for others to reproduce and extend these models within COMSOL.
We hope this serves as a generalizable reference for simulating collective cell behavior and pattern formation.
arxiv.org/pdf/2509.08930
12.09.2025 05:23 β π 1 π 0 π¬ 1 π 0
This complements our earlier work introducing the DCM model π bsky.app/profile/iber...
That previous paper focused on the biological questions and mathematical framework.
Here, we focus on the practical COMSOL #PIDE implementation: setup, BCs, 1Dβ3D, and Lagrangian reformulation for growth.
12.09.2025 05:23 β π 3 π 0 π¬ 1 π 0
Out now: Simulating Organogenesis in #COMSOL: Tissue Patterning with Directed Cell Migration
We provide a detailed walkthrough of how to implement #DCM partial integro-differential equation models - enabling accessible simulations of tissue patterning and morphogenesis.
arxiv.org/pdf/2509.08930
12.09.2025 05:23 β π 18 π 4 π¬ 1 π 0
Our paper "Morphogen gradients can convey position and time in growing tissues" is now out in Newton βͺ@cp-newton.bsky.socialβ¬
Quite fitting to see this novel idea that morphogen gradients not only encode position, but can also time & synchronise development over long distances out in a new journal.
25.08.2025 10:23 β π 13 π 4 π¬ 1 π 1
π Summary:
β’ #DCM is a rapid, robust mechanism for developmental pattern formation
β’ COMSOL FEM implementation makes DCM models numerically accessible
β’ #DCM patterning parameter ranges and timeframes
β’ Pattern Orientation via attraction anisotropy or directed tissue growth
πThread π§΅(10/11)
30.07.2025 07:00 β π 2 π 2 π¬ 1 π 0
2. Dynamic #attraction #zones
Spatially varying cell attraction that changes with tissue growth can guide migrating cells, leading to precise large-scale patterning.
This mimics how tissues form rings, bands, or layered structures in vivo.
πThread π§΅(9/11)
30.07.2025 07:00 β π 3 π 0 π¬ 1 π 0
We identify two mechanisms for guiding pattern orientation:
1. #Anisotropic #attraction
Cells pulling or migrating more strongly in one direction form aligned stripe-like patternsβe.g., during directional tissue growth.
πThread π§΅(8/11)
30.07.2025 07:00 β π 2 π 0 π¬ 1 π 0
#DCM naturally leads to unoriented patternsβspots, labyrinthsβsimilar to Turing-like systems.
But biological tissues often require oriented patterns to fulfill specific functions.
Can DCM produce stripes, too?
πThread π§΅(7/11)
30.07.2025 07:00 β π 2 π 0 π¬ 1 π 0
Three key parameters drive the emergence and morphology of patterns:
β’ Initial density of motile cells
β’ Intercellular attraction strength
β’ Cell sensing radius
πThread π§΅(6/11)
30.07.2025 07:00 β π 2 π 2 π¬ 1 π 0
Simulations and linear stability analysis allowed us to find #critical #conditions for pattern formation and predict #patterning #speed.
We show under which conditions #DCM can realistically pattern tissues in development.
πThread π§΅(5/11)
30.07.2025 07:00 β π 1 π 0 π¬ 1 π 0
We developed a mathematical framework that represents a wide range of #DCM cues, e.g., chemotaxis, durotaxis, haptotaxis & a general Finite Element Method #FEM:
π 1D, 2D, 3D
π arbitrary geometries & boundary conditions
π isotropic & anisotropic interactions
π fast, large-scale simulations
π§΅(4/11)
30.07.2025 07:00 β π 2 π 0 π¬ 1 π 0
To study #DCM, both discrete and continuum models have been used.
But:
π Discrete models are computationally expensive.
π Continuum models have required custom Finite Volume Method #FVM implementationsβuntil now.
πThread π§΅(3/11)
30.07.2025 07:00 β π 2 π 0 π¬ 1 π 0
During embryonic development, cellular tissues transition from uniform starting conditions into robust spatial patterns.
#DCM offers a particularly fast and versatile route to spontaneously symmetry breaks and pattern formation without tissue buckling.
π§΅(2/11)
30.07.2025 07:00 β π 2 π 0 π¬ 1 π 0
How can cells self-organize rapidly into complex patterns during development?
Letβs explore a powerful and underappreciated mechanism: Directed Cell Migration (DCM).
Preprint @biorxivpreprint.bsky.social : doi.org/10.1101/2025...
πThread π§΅(1/11)
30.07.2025 07:00 β π 52 π 18 π¬ 3 π 4
Let me add our paper to this review of mechanical drivers of NT folding.
We showed that mammalian spinal #neuraltube folding is primarily driven by #mesoderm expansion and #zippering. #Hingepoints emerge as a result of these extrinsic forces.
www.pnas.org/doi/10.1073/...
x.com/DagmarIber/s...
21.06.2025 09:41 β π 3 π 0 π¬ 0 π 0
Epithelial tissues are well-known to invade free space and stop proliferation at confluency.
Do we know what regulates cell proliferation spatially and temporally?
π§΅by @dedenonmathieu.bsky.social
15.06.2025 13:24 β π 28 π 10 π¬ 2 π 2
PhD Position - 3D Cell-Based Tissue Simulation Framework
Are you a C++ expert and interested in making 3D cell-based tissue simulations with #SimuCell3D ever more powerful? Please apply and join us as #Master student, #PhD student or #Post-Doc!!
jobs.ethz.ch/job/view/JOP...
jobs.ethz.ch/job/view/JOP...
x.com/DagmarIber/s...
11.06.2025 11:30 β π 11 π 3 π¬ 0 π 0
SNSF Ambizione junior group leader @UZH - fascinated by how embryos get their shapes and patterns π
Postdoc @ Pelkmans lab | PhD @ Heisenberg lab
Biophysics of cells and tissues
PhD student in Statistical Bioinformatics at the University of Zurich and the Swiss Institue of Bioinformatics
Bioinformatics Scientist / Next Generation Sequencing, Single Cell and Spatial Biology, Next Generation Proteomics, Liquid Biopsy, SynBio, AI/ML in biotech // http://albertvilella.substack.com
Biologist | Bookworm | Cycloholic | Birder | Trying to understand the design principles of Life using Mathematics and Physics |
https://youtube.com/@sysbio
https://fac.iitg.ac.in/biplabbose
Molecular cell biology lab exploring the role of mechanical forces in epithelial homeostasis and cancer @ Center for Molecular Medicine UMCU
Stem cell biologist wanting build an artificial kidney and understand development. My lab is at https://lindstromlab.usc.edu/
Director of ASCEND
See our spatial transcriptomic, organoid, and stem cell program https://ascend.usc.edu
Doctoral Student (Comp Modelling / Dev Biology) in the Iber Lab @ETH Zurich / D-BSSE
A platform for life sciences. Publications, research protocols, news, events, jobs and more. Sign up at https://www.lifescience.net.
We are interested in the mechanobiology of the nervous system. @cam.ac.uk @fau.de @mpi-scienceoflight.bsky.social (MPZPM)
Official account of the American Physical Society (APS) Division on Statistical & Nonlinear Physics (DSNP).
Advancing interdisciplinary research in non-equilibrium and statistical physics.
Join now: https://go.aps.org/4eCIyvD
Neuroscience, Machine Learning, Computer Vision β’ Scientist at @unigoettingen.bsky.social & MPI-DS β’ https://eckerlab.org β’ Co-founder of https://maddox.ai β’ Dad of three β’ All things outdoor
Researcher and professor interested in how organs are formed. Neurobiology, morphogenesis, zebrafish and more. Working at UPF
Research laboratory of PI Adrienne Roeder in the Weill Institute of Cell and Molecular Biology and the School of Integrative Plant Science at Cornell Univ. We study morphogenesis and pattern formation in Arabidopsis and diatoms.
Modeling and Dynamics of Biological Systems
https://modelinginbiology.github.io
https://en.wikipedia.org/wiki/Alan_Garfinkel
https://profiles.ucla.edu/alan.garfinkel
Computational Biologist.
Mathematical models, omics methods and data analysis for immunology and infection research at CharitΓ© Berlin.
https://buchauer-lab.eu
CharitΓ© and Humboldt University Berlin, Computational Modelling, Cancer, Signaling
PhD student at KU Leuven | Computational Oncology
Biological theory | βCell learningβ | Function spaces | Projection operators | Manifolds | Dynamics | Behavior | Evolution
livingphysics.org
Cambridge, UK
Living Physics Feed: https://tinyurl.com/living-physics
Dev bio, cell bio, stem cells, regen med, all things kidney. Punk rock and whisky enthusiast. Personal account and opinions. #firstgen