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<p>Successful computational modelling of complex biological phenomena will depend on the seamless sharing of models and hypotheses between researchers of all backgrounds—experimental and theoretical. LimbNET, a new online tool for modelling, simulating and visualising spatiotemporal patterning in limb development, aims to facilitate this process within the limb development community. LimbNET enables remote users to define and simulate arbitrary gene regulatory network (GRN) models of 2D spatiotemporal developmental patterning processes. Researchers can test and compare each others’ hypotheses within a common framework. A database of previously created models empowers users to simulate, explore, and extend each others’ work. Spatiotemporally varying gene expression intensities, derived from image-based data, are mapped into a standardised computational description of limb growth, integrated within our modelling framework. This enables direct comparison not only between datasets but between data and simulation outputs, closing the feedback loop between experiments and simulation via parameter optimisation. All functionality is accessible through a web browser (<a href="https://limbnet.embl.es">https://limbnet.embl.es</a>), requiring no special software, and opening the field of image-driven modelling to the full scientific community.</p>

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LimbNET: collaborative platform for simulating spatial patterns of gene networks in limb development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Antoni Matyjaszkiewicz,
  • James Sharpe

摘要

Successful computational modelling of complex biological phenomena will depend on the seamless sharing of models and hypotheses between researchers of all backgrounds—experimental and theoretical. LimbNET, a new online tool for modelling, simulating and visualising spatiotemporal patterning in limb development, aims to facilitate this process within the limb development community. LimbNET enables remote users to define and simulate arbitrary gene regulatory network (GRN) models of 2D spatiotemporal developmental patterning processes. Researchers can test and compare each others’ hypotheses within a common framework. A database of previously created models empowers users to simulate, explore, and extend each others’ work. Spatiotemporally varying gene expression intensities, derived from image-based data, are mapped into a standardised computational description of limb growth, integrated within our modelling framework. This enables direct comparison not only between datasets but between data and simulation outputs, closing the feedback loop between experiments and simulation via parameter optimisation. All functionality is accessible through a web browser (https://limbnet.embl.es), requiring no special software, and opening the field of image-driven modelling to the full scientific community.