arXiv subjects

Dagmar Iber

Publications and source records attributed to Dagmar Iber.

At least 19 recordsLinked to original sources

arXiv2025

Simulating Organogenesis in COMSOL Multiphysics: Tissue Patterning with Directed Cell Migration

We present a COMSOL Multiphysics implementation of a continuum model for directed cell migration, a key mechanism underlying tissue self-organization and morphogenesis. The model is formulated as a partial integro-differential equation (PIDE), combining random motility with non-local, density-dependent guidance cues to capture phenomena such as cell sorting and aggregation. Our framework supports simulations in one, two, and three dimensions, with both zero-flux and periodic boundary conditions, and can be reformulated in a Lagrangian setting to efficiently handle tissue growth and domain deformation. We demonstrate that COMSOL Multiphysics enables a flexible and accessible implementation of PIDEs, providing a generalizable platform for studying collective cell behavior and pattern formation in complex biological contexts.

physics.bio-ph↗

arXiv2025

PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks

Parameter estimation for differential equations from measured data is an inverse problem prevalent across quantitative sciences. Physics-Informed Neural Networks (PINNs) have emerged as effective tools for solving such problems, especially with sparse measurements and incomplete system information. However, PINNs face convergence issues, stability problems, overfitting, and complex loss function design. Here we introduce PINNverse, a training paradigm that addresses these limitations by reformulating the learning process as a constrained differential optimization problem. This approach achieves a dynamic balance between data loss and differential equation residual loss during training while preventing overfitting. PINNverse combines the advantages of PINNs with the Modified Differential Method of Multipliers to enable convergence on any point on the Pareto front. We demonstrate robust and accurate parameter estimation from noisy data in four classical ODE and PDE models from physics and biology. Our method enables accurate parameter inference also when the forward problem is expensive to solve.

cs.LG↗

arXiv2024

Reply to: Assessing the precision of morphogen gradients in neural tube development

In a recent article [Vetter and Iber, Nat. Commun. 13, 1145 (2022)], we demonstrated that single morphogen gradients in the developing mouse neural tube can carry sufficient positional accuracy to explain the patterning precision of progenitor domain boundaries. Zagorski et al. had previously concluded otherwise [Zagorski et al., Science 356, 1379-1383 (2017)], based on methodological inconsistencies that we have revealed. The authors now comment on our work with a Matters Arising letter. We rebut their criticism point by point in the Supplement, and summarize the main aspects here.

physics.bio-ph↗

arXiv2023

PolyHoop: Soft particle and tissue dynamics with topological transitions

We present PolyHoop, a lightweight standalone C++ implementation of a mechanical model to simulate the dynamics of soft particles and cellular tissues in two dimensions. With only few geometrical and physical parameters, PolyHoop is capable of simulating a wide range of particulate soft matter systems: from biological cells and tissues to vesicles, bubbles, foams, emulsions, and other amorphous materials. The soft particles or cells are represented by continuously remodeling, non-convex, high-resolution polygons that can undergo growth, division, fusion, aggregation, and separation. With PolyHoop, a tissue or foam consisting of a million cells with high spatial resolution can be simulated on conventional laptop computers.

cond-mat.soft↗

arXiv2021

Organ-specific Branching Morphogenesis

A common developmental process, called branching morphogenesis, generates the epithelial trees in a variety of organs, including the lungs, kidneys, and glands. How branching morphogenesis can create epithelial architectures of very different shapes and functions remains elusive. In this review, we compare branching morphogenesis and its regulation in lungs and kidneys and discuss the role of signaling pathways, the mesenchyme, the extracellular matrix, and the cytoskeleton as potential organ-specific determinants of branch position, orientation, and shape. Identifying the determinants of branch and organ shape and their adaptation in different organs may reveal how a highly conserved developmental process can be adapted to different structural and functional frameworks and should provide important insights into epithelial morphogenesis and developmental disorders.

q-bio.TO↗

arXiv2020

The Control of Lung Branching Morphogenesis

Branching morphogenesis generates epithelial trees which facilitate gas exchange, filtering, as well as secretion processes with their large surface to volume ratio. In this review, we focus on the developmental mechanisms that control the early stages of lung branching morphogenesis. Lung branching morphogenesis involves the stereotypic, recurrent definition of new branch points, subsequent epithelial budding, and lung tube elongation. We discuss current models and experimental evidence for each of these steps. Finally, we discuss the role of the mesenchyme in determining the organ-specific shape.

q-bio.TO↗

arXiv2018

Global optimization using Gaussian Processes to estimate biological parameters from image data

Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is typically noisy and non-quantitative. In addition, these models can have a high computational cost, limiting the number of feasible simulations, and therefore rendering most traditional parameter estimation methods unsuitable. In this paper, we present a pipeline that uses Gaussian process learning to estimate biological parameters from noisy, non-quantitative image data when the model has a high computational cost. This approach is first successfully tested on a parametric function with the goal of retrieving the original parameters. We then apply it to estimating parameters in a biological setting by fitting artificial in-situ hybridization (ISH) data of the developing murine limb bud. We expect that this method will be of use in a variety of modeling scenarios where quantitative data is missing and the use of standard parameter estimation approaches in biological modeling is prohibited by the computational cost of the model.

q-bio.QM↗

arXiv2017

Simulating Organogenesis in COMSOL: Comparison Of Methods For Simulating Branching Morphogenesis

During organogenesis tissue grows and deforms. The growth processes are controlled by diffusible proteins, so-called morphogens. Many different patterning mechanisms have been proposed. The stereotypic branching program during lung development can be recapitulated by a receptor-ligand based Turing model. Our group has previously used the Arbitrary Lagrangian-Eulerian (ALE) framework for solving the receptor-ligand Turing model on growing lung domains. However, complex mesh deformations which occur during lung growth severely limit the number of branch generations that can be simulated. A new Phase-Field implementation avoids mesh deformations by considering the surface of the modelling domains as interfaces between phases, and by coupling the reaction-diffusion framework to these surfaces. In this paper, we present a rigorous comparison between the Phase-Field approach and the ALE-based simulation.

q-bio.QM↗

arXiv2016

Simulating Organogenesis in COMSOL: Phase-Field Based Simulations of Embryonic Lung Branching Morphogenesis

Organogenesis has been studied for decades, but fundamental questions regarding the control of growth and shape remain unsolved. We have recently shown that of all proposed mathematical models only ligand-receptor based Turing models successfully reproduce the experimentally determined growth fields of the embryonic lung and thus provide a mechanism for growth control during embryonic lung development. Turing models are based on at least two coupled non-linear reaction-diffusion equations. In case of the lung model, at least two distinct layers (mesenchyme and epithelium) need to be considered that express different components (ligand and receptor, respectively). The Arbitrary Lagrangian-Eulerian (ALE) method has previously been used to solve this Turing system on growing and deforming (branching) domains, where outgrowth occurs proportional to the strength of ligand-receptor signalling. However, the ALE method requires mesh deformations that eventually limit its use. Therefore, we incorporate the phase field method to simulate 3D embryonic lung branching with COMSOL.

q-bio.QM↗

arXiv2015

Image-based Modelling of Organogenesis

One of the major challenges in biology concerns the integration of data across length and time scales into a consistent framework: how do macroscopic properties and functionalities arise from the molecular regulatory networks - and how can they change as a result of mutations? Morphogenesis provides an excellent model system to study how simple molecular networks robustly control complex processes on the macroscopic scale in spite of molecular noise, and how important functional variants can emerge from small genetic changes. Recent advancements in 3D imaging technologies, computer algorithms, and computer power now allow us to develop and analyse increasingly realistic models of biological control. Here we present our pipeline for image-based modeling that includes the segmentation of images, the determination of displacement fields, and the solution of systems of partial differential equations (PDEs) on the growing, embryonic domains. The development of suitable mathematical models, the data-based inference of parameter sets, and the evaluation of competing models are still challenging, and current approaches are discussed.

cs.CE↗

arXiv2015

LBIBCell: A Cell-Based Simulation Environment for Morphogenetic Problems

The simulation of morphogenetic problems requires the simultaneous and coupled simulation of signalling and tissue dynamics. A cellular resolution of the tissue domain is important to adequately describe the impact of cell-based events, such as cell division, cell-cell interactions, and spatially restricted signalling events. A tightly coupled cell-based mechano-regulatory simulation tool is therefore required. We developed an open-source software framework for morphogenetic problems. The environment offers core functionalities for the tissue and signalling models. In addition, the software offers great flexibility to add custom extensions and biologically motivated processes. Cells are represented as highly resolved, massless elastic polygons; the viscous properties of the tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used to model the interaction between the viscous and elastic properties of the cells, thus extending on the IBCell model. The fluid and signalling processes are solved using the Lattice Boltzmann method. As application examples we simulate signalling-dependent tissue dynamics.

q-bio.QM↗

arXiv2014

Simulating Organogenesis: Algorithms for the Image-based Determination of Displacement Fields

Recent advances in imaging technology now provide us with 3D images of developing organs. These can be used to extract 3D geometries for simulations of organ development. To solve models on growing domains, the displacement fields between consecutive image frames need to be determined. Here we develop and evaluate different landmark-free algorithms for the determination of such displacement fields from image data. In particular, we examine minimal distance, normal distance, diffusion-based and uniform mapping algorithms and test these algorithms with both synthetic and real data in 2D and 3D. We conclude that in most cases the normal distance algorithm is the method of choice and wherever it fails, diffusion-based mapping provides a good alternative.

q-bio.QM↗

arXiv2014

Simulating Organogenesis in COMSOL: Image-based Modeling

Mathematical Modelling has a long history in developmental biology. Advances in experimental techniques and computational algorithms now permit the development of increasingly more realistic models of organogenesis. In particular, 3D geometries of developing organs have recently become available. In this paper, we show how to use image-based data for simulations of organogenesis in COMSOL Multiphysics. As an example, we use limb bud development, a classical model system in mouse developmental biology. We discuss how embryonic geometries with several subdomains can be read into COMSOL using the Matlab LiveLink, and how these can be used to simulate models on growing embryonic domains. The ALE method is used to solve signaling models even on strongly deforming domains.

cs.CE↗

arXiv2014

Feedbacks, Receptor Clustering, and Receptor Restriction to Single Cells yield large Turing Spaces for Ligand-receptor based Turing Models

Turing mechanisms can yield a large variety of patterns from noisy, homogenous initial conditions and have been proposed as patterning mechanism for many developmental processes. However, the molecular components that give rise to Turing patterns have remained elusive, and the small size of the parameter space that permits Turing patterns to emerge makes it difficult to explain how Turing patterns could evolve. We have recently shown that Turing patterns can be obtained with a single ligand if the ligand-receptor interaction is taken into account. Here we show that the general properties of ligand-receptor systems result in very large Turing spaces. Thus, the restriction of receptors to single cells, negative feedbacks, regulatory interactions between different ligand-receptor systems, and the clustering of receptors on the cell surface all greatly enlarge the Turing space. We further show that the feedbacks that occur in the FGF10/SHH network that controls lung branching morphogenesis are sufficient to result in large Turing spaces. We conclude that the cellular restriction of receptors provides a mechanism to sufficiently increase the size of the Turing space to make the evolution of Turing patterns likely. Additional feedbacks may then have further enlarged the Turing space. Given their robustness and flexibility, we propose that receptor-ligand based Turing mechanisms present a general mechanism for patterning in biology.

q-bio.MN↗

arXiv2014

Image-based Parameter Inference for Spatio-temporal models of Organogenesis

Advances in imaging technology now provide us with detailed 3D data on gene expression patterns in developing embryos. This information can be used to build predictive mathematical models of embryogenesis. Current modelling approaches are, however, limited by lack of methods to automatically infer the regulatory networks and the parameter values from the image-based information. Here we make a first step to the development of such methods. We use limb bud development as a model system. For a given regulatory network we developed a decision tree based algorithm to automatically determine parameter values for which the model reproduces the expression patterns. Starting from this parameter set, local optimization was performed to further reduce the chosen goodness-of-fit measure. This approach allowed us to recover the target expression patterns, as judged by eye, and thus provides a first step towards the automated inference of parameter values for a given regulatory network.

q-bio.QM↗

arXiv2014

How do digits emerge? - Mathematical Models of Limb Development

The mechanism that controls digit formation has long intrigued developmental and theoretical biologists, and many different models and mechanisms have been proposed. Here we review models of limb development with a specific focus on digit and long bone formation. Decades of experiments have revealed the basic signalling circuits that control limb development, and recent advances in imaging and molecular technologies provide us with unprecedented spatial detail and a broader view on the regulatory networks. Computational approaches are important to integrate the available information into a consistent framework that will allow us to achieve a deeper level of understanding and that will help with the future planning and interpretation of complex experiments, paving the way to in silico genetics. Previous models of development had to be focused on very few, simple regulatory interactions. Algorithmic developments and increasing computing power now enable the generation and validation of increasingly realistic models that can be used to test old theories and uncover new mechanisms.

q-bio.TO↗

arXiv2013

Simulating Organogenesis in COMSOL: Cell-based Signaling Models

Most models of biological pattern formation are simulated on continuous domains even though cells are discrete objects that provide internal boundaries to the diffusion of regulatory components. In our previous papers on simulating organogenesis in COMSOL (Germann et al COMSOL Conf Procedings 2011; Menshykau and Iber, COMSOL Conf Proceedings 2012) we discussed methods to efficiently solve signaling models on static and growing continuous domains. Here we discuss COMSOL-based methods to study spatio-temporal signaling models at cellular resolution with subcellular compartments, i.e. cell membrane, cytoplasm, and nucleus.

q-bio.QM↗

arXiv2013

Simulating Organogenesis in COMSOL: Parameter Optimization for PDE-based models

Morphogenesis is a tightly regulated process that has been studied for decades. We are developing data-based and image-basd mechanistic models for a range of developmental processes with a view to integrate the available knowledge and to better understand the underlying regulatory logic. In our previous papers on simulating organogenesis with COMSOL (German et al COMSOL Conf Procedings 2011; Menshykau and Iber, COMSOL Conf Proceedings 2012) we discussed methods to efficiently solve such models on static and growing domains. A further challenge in modeling morphogenesis is the parameterization of such models. Here we discuss COMSOL-based methods for parameter optimization. These routines can be used to determine parameter sets, for which the simulations reproduce experimental data and constraints. Such data is often image based, but may also come from classical biochemical or genetic experiments.

q-bio.QM↗