PUBLICATION
High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals
- Authors
- Marcon, L., Diego, X., Sharpe, J., Müller, P.
- ID
- ZDB-PUB-160409-7
- Date
- 2016
- Source
- eLIFE 5: (Journal)
- Registered Authors
- Müller, Patrick
- Keywords
- S. cerevisiae, computational biology, developmental biology, mouse, stem cells, systems biology, zebrafish
- MeSH Terms
-
- Body Patterning*
- Embryology/methods*
- Models, Theoretical*
- Software
- PubMed
- 27058171 Full text @ Elife
Citation
Marcon, L., Diego, X., Sharpe, J., Müller, P. (2016) High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals. eLIFE. 5.
Abstract
The reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic reaction-diffusion networks. Our analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern for any combination of diffusion coefficients. We provide a software to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping