Patterning precision under non-linear morphogen decay and molecular noise
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland
- Swiss Institute of Bioinformatics, Switzerland
Figures
Figure 1
Comparison of linear and non-linear morphogen gradients.
(A) According to the French flag model, morphogen gradients provide the spatial information required for tissue patterning via concentration thresholds , numbered by etc. If a cell lies above …
Figure 2
Numerical model to simulate noisy gradients.
A 1D cellular domain is constructed by drawing cell areas from log-normal distributions with mean cell area and standard deviation . Cell areas are then converted to diameters (). This …
Figure 3
Impact of non-linear decay on gradient precision.
(A) Physiological variability in the cross-sectional cell areas has no significant impact on gradient precision. The positional error is plotted in units of the mean cell diameter at different …
Figure 4
Impact of the boundary condition (BC) at the source.
(A–C) Noise-free gradient shapes when the morphogen is either secreted in a source domain at rate (Equation 6) (A), with flux BC, (B), or Dirichlet BC, (C). No-flux BC were imposed at at …
Figure 5
Impact of the morphogen source strength.
Numerically obtained spatial patterning accuracy in units of average cell diameters at different positions in the tissue (symbols) and for different degrees of non-linearity (colours). (A–C) …
Appendix 1—figure 1
Shift in morphogen gradients due to changes in morphogen production.
(A) Comparison of noise-free gradients arising from linear (blue) and non-linear (green) decay. A fold-change in the influx j0 from the source shifts the gradients by . (B) Positional shift of the …
