Chen et al
The authors argue that it is antagonistic repression of the Bcd gradient that is essential to specify and properly order posterior segment boundaries, rather than the precise levels of the Bcd morphogen itself. However, the anterior boundaries of the repressive Run and Kr gradients seem to be entirely determined by the shape of the Bcd gradient (Fig 6), therefore implying that these repressors contain little of their own intrinsic spatial information. Rather, they seem more like effectors that implement the correct boundary identity given some spatial information derived from a Bcd gradient. In light of this should we accept the author’s model as a true challenge to the classic threshold issues facing morphogen models? Why / why not?
Is there any evidence of ‘general relativistic’ or ‘regulative’ feedback (Jaeger et al., 2008) in the authors data? What kind? (It seems there is feedback onto Bcd target genes but not Bcd itself).
Could we gain any useful information from capturing the interaction dynamics between Bcd, its target enhancers, and repressor proteins? What?
Are there other systems where this sort of activation-repression antagonism is used to establish patterning?
How many mutations in the endogenous locus would it take to change the position of expression of a bicoid target gene? It could be that it is highly evolvable or very static.
The paper raises an interesting question: what is the best strategy to define many unique compartments along a given axis? In the presence of molecular noise, there is an upper limit for threshold-dependent activation. The authors suggested antagonistic repression as a possible solution. However, the net effect of an antagonistic repressor is to sharpen the morphogenrepressor boundary, so that the morphogen signal drops abruptly at the repressor boundary. Yet it does not increase the dynamic range of the morphogen. A combination of repressor at different range, such as Cic, Run, and Kr, could possibly increase the positional information. However, is the determination of morphogen-repressor boundary position a less noisy process? When the number of repressors increases, does that guarantee a corresponding increase of distinct compartments? Or is some kind of cross interaction of repressors necessary? Is this better than cross interaction of direct morphogen targets? A simple mathematical formulation of this problem should help clarify these questions.
Tostevin et al
What are some problems with their modeling approach as applied to development? Do you believe that cells are just “averaging” their inputs? or is more going on?
Why might cells want to avoid simply thresholding their position from a secreted factor? What are the advantages to more complex solutions to this problem?
Cotterell & Sharpe
The authors expect to see a frozen oscillator in nature given its modularity and robustness to noise and to mutations, but as yet this strategy has been unobserved. Are there other considerations that may explain why nature may only sample a subset of functional strategies?

How might we look for unobserved morphogen interpretation strategies (e.g., frozen oscillator) in nature?
What is the relationship between the number of genes in a network simulation and the number of topologies and mechanisms (i.e. does it increase linearly as the number of genes in a network increases, or is there another mathematical model to describe it)? Would the number of nodes in higher order gene systems make the analysis too complex to answer accurately?
Is there always a 1:1 relationship between the number of topologies and the number of mechanisms? The researchers might try a 4- and 5-gene system and follow a similar analysis.
Are there any mechanisms that show up on the topology and mechanism atlases, but are not seen in real systems? If so, why would they still be there?
Are mechanisms ever lost? How is a new one added?
It would be cool to compare the same gene network in two different organisms (related and/or completely dissimilar) and to examine the similarities and differences in the topologies/mechanisms. What do the parallels reveal?
The first extension of this work that comes to mind is to simply add the other two space dimensions. Adding degrees of freedom can drastically alter the robustness of stochastic systems
(e.g. random walk in 1D vs. 3D), and can thus alter part of the motif characterization and behavior, specially for those who rely on diffusion.
A fun extension would be to have a 2D system, redeploy this methodology and try to match the pattern of adult Jaguar spots. In theory, this has been described as a two stage Turing process of reaction/diffusion, but it would be interesting to search for GRNs generating this behavior.
As an additional extension of this work, I would like to assay gene concentration at the single cell level during comparable times of development (e.g. through Peng Yin's PAINT method), and instantiate the global GRN into cell specific possible GRNs (e.g. remove from the master GRN proteins not yet present). From this cell specific GRN, we could determine which "core topology" is effective, if any, at different times and involved in different processes.