Image-based modelling of organogenesis

Iber, Dagmar; Karimaddini, Zahra; Ünal, Erkan

doi:10.1093/bib/bbv093

Cited by 12 publications

(12 citation statements)

References 77 publications

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“…The model-based analysis of imaging data facilitates the unraveling of novel mechanisms and the comparison of competing hypotheses (Uzkudun et al, 2015;Iber et al, 2015;Jagiella et al, 2017), this is often too demanding and error-prone if structured noise and outliers are present. To address this problem, we introduced an integrated statistical approach for the statistical and mechanistic modelling of imaging data.…”

Section: Discussionmentioning

confidence: 99%

“…Model-based approaches have been introduced to unravel the mechanisms underlying the spatio-temporal organisation of tissues (Iber et al, 2015;Uzkudun et al, 2015). Partial differential equation (PDE) models and agent-based models which capture static and dynamic properties of tissue-scale images have been developed (see (Menshykau et al, 2014;Uzkudun et al, 2015;Hersch et al, 2015;Jagiella et al, 2017) and references therein).…”

Section: Introductionmentioning

confidence: 99%

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Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

Hroß

Theis

Sixt

et al. 2018

Preprint

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Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems, however, mechanisms causing spatial patterning often remain illusive. In recent years, spatio-temporal mathematical modelling helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parameterisation and subsequent refinement of the model using additional mechanisms. Among others, model-based hypothesis testing predicted lymphatic vessel dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

Hroß

Theis

Sixt

et al. 2018

Preprint

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“…How spatial patterning and the geometry of branched networks are generated and to what extent are the construction principles shared between branched organs remain outstanding questions in the field. One appealing approach to tackle this question is computational modeling [47,48]. Recently, branch pattern formation in the mammary gland, kidney and prostate was proposed to be governed by simple generic rules involving the collective dynamics of progenitors present at ductal tips that drive ductal elongation and stochastic tip bifurcation [2,49].…”

Section: Patterningmentioning

confidence: 99%

Inductive signals in branching morphogenesis – lessons from mammary and salivary glands

Myllymäki

Mikkola

2019

Current Opinion in Cell Biology

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Branching morphogenesis is a fundamental developmental program that generates large epithelial surfaces in a limited three-dimensional space. It is regulated by inductive tissue interactions whose effects are mediated by soluble signaling molecules, and cell-cell and cell-extracellular matrix interactions. Here, we will review recent studies on inductive signaling interactions governing branching morphogenesis in light of phenotypes of mouse mutants and ex vivo organ culture studies with emphasis on developing mammary and salivary glands. We will highlight advances in understanding how cell fate decisions are intimately linked with branching morphogenesis. We will also discuss novel insights into the molecular control of cellular mechanisms driving the formation of these arborized ductal structures and reflect upon how distinct spatial patterns are generated.

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“…Consequently, a pipeline has been established that allows to test models on physiological geometries from cultured embryonic kidney and lung explants (Adivarahan et al, 2013; Menshykau and Iber, 2013; Iber et al, 2015, 2016; Gómez et al, 2017). The obtained 2D time-lapse movies are segmented to obtain the epithelial boundary for each time frame and to calculate the growth fields between consecutive time frames.…”

Section: Geometry Effect and Image-based Modellingmentioning

confidence: 99%

Mathematical Approaches of Branching Morphogenesis

2018

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Many organs require a high surface to volume ratio to properly function. Lungs and kidneys, for example, achieve this by creating highly branched tubular structures during a developmental process called branching morphogenesis. The genes that control lung and kidney branching share a similar network structure that is based on ligand-receptor reciprocal signalling interactions between the epithelium and the surrounding mesenchyme. Nevertheless, the temporal and spatial development of the branched epithelial trees differs, resulting in organs of distinct shape and size. In the embryonic lung, branching morphogenesis highly depends on FGF10 signalling, whereas GDNF is the driving morphogen in the kidney. Knockout of Fgf10 and Gdnf leads to lung and kidney agenesis, respectively. However, FGF10 plays a significant role during kidney branching and both the FGF10 and GDNF pathway converge on the transcription factors ETV4/5. Although the involved signalling proteins have been defined, the underlying mechanism that controls lung and kidney branching morphogenesis is still elusive. A wide range of modelling approaches exists that differ not only in the mathematical framework (e.g., stochastic or deterministic) but also in the spatial scale (e.g., cell or tissue level). Due to advancing imaging techniques, image-based modelling approaches have proven to be a valuable method for investigating the control of branching events with respect to organ-specific properties. Here, we review several mathematical models on lung and kidney branching morphogenesis and suggest that a ligand-receptor-based Turing model represents a potential candidate for a general but also adaptive mechanism to control branching morphogenesis during development.

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Image-based modelling of organogenesis

Cited by 12 publications

References 77 publications

Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

Inductive signals in branching morphogenesis – lessons from mammary and salivary glands

Mathematical Approaches of Branching Morphogenesis

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