ArticleNPJ systems biology and applications2024
Automatic design of gene regulatory mechanisms for spatial pattern formation.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 9 citations in OpenAlex.
- Emergent stable tissue shapes from the regulatory feedback between morphogens and cell growth.Journal of theoretical biology · 2026Article
- LimbNET: collaborative platform for simulating spatial patterns of gene networks in limb development.Molecular systems biology · 2026Article
- Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data.Journal of medical systems · 2025Article
- Emergent Stable Tissue Shapes from the Regulatory Feedback between Morphogens and Cell Growth.bioRxiv : the preprint server for biology · 2025Article
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2 authors at 2 institutions in 1 country.
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Abstract
Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.
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