Biological systems involving short-range activators and long-range inhibitors can generate complex patterns. Reaction-diffusion models postulate that differences in signaling range are caused by differential diffusivity of inhibitor and activator. Other models suggest that differential clearance underlies different signaling ranges. To test these models, we measured the biophysical properties of the Nodal/Lefty activator/inhibitor system during zebrafish embryogenesis. Analysis of Nodal and Lefty gradients reveals that Nodals have a shorter range than Lefty proteins. Pulse-labelinganalysis indicates that Nodals and Leftys have similar clearance kinetics, whereas fluorescence recovery assays reveal that Leftys have a higher effective diffusion coefficient than Nodals. These results indicate that differential diffusivity is the major determinant of the differences in Nodal/Lefty range and provide biophysical support for reaction-diffusion models of activator/inhibitor-mediated patterning.

The graded distribution of morphogens underlies many of the tissue patterns that form during development. How morphogens disperse from a localized source and how gradients in the target tissue form has been under debate for decades. Recent imaging studies and biophysical measurements have provided evidence for various morphogen transport models ranging from passive mechanisms, such as free or hindered extracellular diffusion, to cell-based dispersal by transcytosis or cytonemes. Here, we analyze these transport models using the morphogens Nodal, fibroblast growth factor and Decapentaplegic as case studies. We propose that most of the available data support the idea that morphogen gradients form by diffusion that is hindered by tortuosity and binding to extracellular molecules.

Signalling pathways mediating the transduction of information between cells are essential for development, cellular differentiation and homeostasis. Their dysregulation is also frequently associated with human malignancies. The Janus tyrosine kinase/signal transducer and activator of transcription (JAK/STAT) pathway represents one such signalling cascade whose evolutionarily conserved roles include cell proliferation and haematopoiesis. Here we describe a systematic genome-wide survey for genes required for JAK/STAT pathway activity. Analysis of 20,026 RNA interference (RNAi)-induced phenotypes in cultured Drosophila melanogaster haemocyte-like cells identified interacting genes encoding 4 known and 86 previously uncharacterized proteins. Subsequently, cell-based epistasis experiments were used to classify these proteins on the basis of their interaction with known components of the signalling cascade. In addition to multiple human disease gene homologues, we have found the tyrosine phosphatase Ptp61F and the Drosophila homologue of BRWD3, a bromo-domain-containing protein disrupted in leukaemia. Moreover, in vivo analysis demonstrates that disrupted dBRWD3 and overexpressed Ptp61F function as suppressors of leukaemia-like blood cell tumours. This screen represents a comprehensive identification of novel loci required for JAK/STAT signalling and provides molecular insights into an important pathway relevant for human cancer. Human homologues of identified pathway modifiers may constitute targets for therapeutic interventions.

The Turing 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 Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) 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 Turing 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.DOI: http://dx.doi.org/10.7554/eLife.14022.001