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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... 🧵 5/6

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We uncover a trade-off regarding cell size: • Larger cells yield sharper boundaries (smaller #TZW) • 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. 🧵 4/6

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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. 🧵 3/6

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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. 🧵 2/6

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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... 🧵 1/6

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Dagmar Iber & CoBi @iberd.bsky.social · 05/11/2025

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Paper & poster are available on the COMSOL conference website: www.comsol.com/paper/direct...

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Dagmar Iber & CoBi @iberd.bsky.social · 12/09/2025

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

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Dagmar Iber & CoBi @iberd.bsky.social · 25/08/2025

Our paper "Morphogen gradients can convey position and time in growing tissues" is now out in Newton ‪@cp-newton.bsky.social‬ 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.

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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. 👇Thread 🧵(9/11)

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We identify two mechanisms for guiding pattern orientation: 1. #Anisotropic #attraction Cells pulling or migrating more strongly in one direction form aligned stripe-like patterns—e.g., during directional tissue growth. 👇Thread 🧵(8/11)

#DCM naturally leads to unoriented patterns—spots, labyrinths—similar to Turing-like systems. But biological tissues often require oriented patterns to fulfill specific functions. Can DCM produce stripes, too? 👇Thread 🧵(7/11)

Three key parameters drive the emergence and morphology of patterns: • Initial density of motile cells • Intercellular attraction strength • Cell sensing radius 👇Thread 🧵(6/11)

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We developed a mathematical framework that represents a wide range of #DCM cues, e.g., chemotaxis, durotaxis, haptotaxis & a general Finite Element Method #FEM: 👉 1D, 2D, 3D 👉 arbitrary geometries & boundary conditions 👉 isotropic & anisotropic interactions 👉 fast, large-scale simulations 🧵(4/11)

To study #DCM, both discrete and continuum models have been used. But: 👉 Discrete models are computationally expensive. 👉 Continuum models have required custom Finite Volume Method #FVM implementations—until now. 👇Thread 🧵(3/11)

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. 🧵(2/11)

How can cells self-organize rapidly into complex patterns during development? Let’s explore a powerful and underappreciated mechanism: Directed Cell Migration (DCM). Preprint @biorxivpreprint.bsky.social : doi.org/10.1101/2025... 👇Thread 🧵(1/11)

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⚡ No plateaus here! Classical PINNs stall. PINNverse keeps improving physics loss ∼ epoch^(-1.4) for Fisher’s equation. Algebraic decay >> stagnation!

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📉 #PINNverse crushes parameter error — even with: - High noise - Terrible initial guesses Stable & accurate where others fail (e.g., Fisher-KPP).

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💥 Why is this a breakthrough? Standard PINNs miss non-convex Pareto fronts → overfit. #PINNverse captures the entire Pareto front → balances physics + data perfectly.

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🔑 The big idea: Classical PINNs use weighted-sum loss → often fails. #PINNverse reframes it as constrained optimization → unlocks better solutions. Small change, huge impact!

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📊 How does #PINNverse stack up? ✅ beats Nelder-Mead & classical PINNs ✅ handles noisy data & bad initial guesses ✅ tested on 4 tough benchmarks: - Kinetic reaction ODE - FitzHugh–Nagumo - Fisher–KPP PDE - Burgers’ PDE

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Dagmar Iber & CoBi @iberd.bsky.social · 20/02/2025

🚀 Introducing #PINNverse — a game-changer for parameter estimation in differential equations! 🧠💡 No forward solves. Better accuracy. Robust to noise. Preprint: doi.org/10.48550/arX... #SciComm #MachineLearning #InverseProblems #PINNs

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What a beautiful cover for our #mechanobiology paper "Morphometry and mechanical instability at the onset of epithelial bladder cancer": doi.org/10.1038/s415... - thx @naturephysics.bsky.social !! News & Views "Tissue wrinkles foreshadow cancer" doi.org/10.1038/s415...

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Dagmar Iber & CoBi @iberd.bsky.social · 31/01/2025

Pleased to share our latest review "Coordination of nephrogenesis with branching of the urinary collecting system, the vasculature and the nervous system" authors.elsevier.com/a/1kXJ6Fzn7S...

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We present a simple yet comprehensive model integrating multiple mechanical forces to guide lung development. These principles operate during both development and disease, providing a dynamic framework to understand and predict lung remodeling processes. 👇Thread 🧵(9/9)

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In #COPD patients, destruction of #lung #tissue leads to #remodelling of the lung tree. Our biophysical model explains the thickening of the airway walls, potentially helping to localize affected areas, and yields quantitative #biomarkers for #PersonalizedHealth. 👇Thread 🧵(8/9)

After the right lung is surgically removed, bronchial trees exhibit enormous plasticity, reorganizing for energy efficiency while regenerating lost tissue volume. ⇨ Fluid–structure interactions act as powerful mechanisms for transmitting information continuously across scales. 👇Thread 🧵(7/9)

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Using #SkelePlex, we show that the fractalization of the lung happens on the go during the pseudoglandular stage of lung development. Branch morphology adapts plastically depending on tip number, illustrating how local cues drive efficient lung formation. 👇Thread 🧵(6/9)

Tracking lung remodeling has been a challenge—until now. We developed #SkelePlex, an #opensource tool for tracking bronchial tree growth & regeneration. Validated on: 🐭 Mouse development 🐶 Dog lung regeneration post-surgery 🧑‍⚕️ #COPD patient cohorts 👇Thread 🧵(5/9)

As flow rate Q depends on tip number, diameter and wall thickness adjust naturally across scales! This universal, #scaleinvariant mechanism enables lungs to function efficiently across species—whether in mice 🐭, dogs 🐶, or humans 🧑‍⚕️. 👇Thread 🧵(4/9)

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We identify key #mechanical #forces that shape the lung’s non-self-similar #fractal structure. Uniform shear stress sets inner diameters D, uniform hoop stress defines wall thickness , while the length-to-diameter ratio L/D leads to equal pressure drops, ∆p, over each branch. 👇Thread 🧵(3/9)

Decades of research uncovered global principles of optimal #lung #morphology: 🔹 Murray’s law balances airflow resistance & dead volume. 🔹 Branch asymmetry enables rapid ventilation. But lungs aren’t designed. What local #selforganizing cues create these #patterns? 🤔 👇Thread 🧵(2/9)