Intermediate regime during finger formation: from instabilities to fingers

1. Intermediate regime during finger formation: from instabilities to fingers • Questions that we are interested in: • How do fingers break out of the edge? • Are there characteristic and dominant instability modes that lead to fingers? • These would be wavelengths or modes that are unstable (will propagate over time) • H ~ Σeλteikx • How might these wavelengths relate to the resulting finger widths? i

2. We are interested in the following transition: The blue line indicates the boundary or interface between the inoculation and agarose.

3. The approach: • Define the boundary as points where the gradient in image intensity is large • Apply Fourier Transform to the boundary to find the Fourier modes that can describe the shape of the boundary (I used the fft function in Matlab) • I then found the modes (excluding the first) that accounted for 90% (an arbitrary metric) of the weights, as the modes that will sufficiently describe the shape. • The weights are found by taking the cumulative sum of abs(amplitude) for all modes, and then dividing abs(amplitude) by this sum. • I then sorted the weights to find those that accounted for >= 90% of the cumulative sum.

4. A demonstration of how the chosen modes (accounting for 90% of the sum of weights) sufficiently fits the boundary:

5. I sampled the boundary at 10-min intervals • To find the fast growing modes, we can look for modes with weights increasing over time • The colors correspond to different timepoints • The weights are taken relative to the cumulative sum of amplitudes at each timepoint We are interested in growing modes so we zoom into this region with many growing modes (next slide)

6. Here I’m highlighting some modes that look like they are growing in weights over time 241.8 134.3 (grew very fast) 100.8 (grew moderately fast) 151.1 93 (grew fast) 75.56 (emerged at later time points This looks promising, but is looking at the weights the right approach?

7. Highlighting the top 10 modes (in microns), where amp(final)/amp(initial) > 15 • Alternatively, we can look for modes with amplitudes increasing over time • On the y-axis, the amplitudes were normalized to the first timepoint that they emerged • The colors represent different instability wavelengths 40.300000 31.815789 109.909091 52.565217 34.542857 48.360000 33.583333 41.689655 27.477273 50.375000 9Aug data One can observe that the amplitudes are not always exponentially increasing with time, but the general trend does

8. An alternative plot to the same data. Here I did a 3D surf plot to visualize the amplitudes of the corresponding wavelengths over time 9Aug data

9. In the next few slides I will show results from 3 more experiments 7Aug data Highlighting the top 10 modes (in microns), where amp(final)/amp(initial) > 4 151.125000 93.000000 100.750000 134.333333 109.909091 120.900000 172.714286 63.631579 67.166667

10. Highlighting the top 10 modes (in microns), where amp(final)/amp(initial) > 5.5 19Jan data 143.859375 164.410714 127.875000 41.102679 42.625000 38.362500 39.685345 44.264423 230.175000 191.812500 115.087500

11. Highlighting the top 8 modes (in microns), where amp(final)/amp(initial) > 14 60.450000 63.631579 71.117647 67.166667 37.781250 18.890625 19.500000 172.714286 4Sept data

12. Quick conclusions Future work • We seem to see some wavelengths that emerge to rapidly increase their amplitudes over time, e.g. in the range between 30-50 um (on the scale of the small finger-like protrusions), and >100 um (close to the width of the bottom of the longer fingers that already formed by the end of the time considered) • Comparison across more repeats and different starting cell densities to look for characteristic instability wavelengths that lead to finger formation • does it scale s.t.λ~ C1/3 ? • Find the eigenvalue for the wavelengths with increasing amplitudes • Relate the instability wavelengths to the widths of resulting fingers • Try to calculate the threshold cell density required for fingers to emerge

13. Redefining “weight” • Before I defined “weight” as amplitude/(sum of amplitudes) for each mode at that timestep • Now I will redefine “weight” as amplitude/(norm of amplitude vector), to convert everything into unit vectors • The new plots are in the next few slides, and we can look for modes that have non-exponential behavior (e.g. grew in weight then decreased)

14. Example of weights by wavelength From 9Aug data Colormap represents time in multiples of 10 min

15. Here I’m plotting the weight over time for each mode, on a log scale. The colormap represents the indices (not value) for each mode.

16. Here I plotted the time evolution of the normalized weight for the modes that experienced a large overall increase in weight The very small modes (30-40um) may represent the small fluctuations that are forming on this cell-dense progressing front 109um mode

17. Close to the 63um mode In the next few slides I will show results from 3 more experiments Close to the 93um mode 7Aug data

18. The modes with value ~50+ um are representative of these small new fingers This is approximately close to the 140+um mode 19Jan data Do the fluctuations in the 52um mode indicate its instability (disappearing and reappearing) at earlier times before the emergence of the smaller fingers?

19. 4Sept data The modes with value ~60+ um are representative of these small new fingers This is approximately close to the 172um mode