This page summarises a series of experiments that explore some of the capabilities of the Neighbour-Sensing model. The experiments were done by changing the parameters that the simulation uses in its mathematical computations, so that you can SEE on screen the effects of those parameters on growth of the fungus.

What follows in this document is a record of these experiments presented in tabulated form standardised so that in most cases the left cell shows the image of the mycelium generated by the parameter set, and the right cell lists the parameters used to produce that image. The definitions of the parameters are laid out so that they reflect the lay out of the parameters page of the program itself (Fig. 1).

These experiments used a version of the program in which the user interface for defining the parameters to be used in each run had the appearance shown in Fig. 1. Following this figure is a series of experimental records are presented so that accompanying the image of the mycelium is a textual definition of the parameters used to produce the image, these being laid out to reflect the lay out of the parameters interface page of the program (Fig. 1).

In the printed parameter sets a hyphen represents an unchecked box in the parameter dialogue (see Fig. 1), and the parameter(s) shown in red-bold font are the ones being illustrated. The time units shown above each parameter set should be viewed as consecutive blocks during which that parameter set has been operative. Each figure has a standard scale bar of 100 length units. Simulations can be paused, parameters changed, and the simulation resumed. In these cases several parameter sets are shown accompanying a single illustration.

Figs 2-4 show the effects of varying the autotropism setting.

Fig. 4 shows the growth pattern obtained with negative autotropism set to 0.1. This seems to produce the most realistic mycelial shapes and was the value used in most of the subsequent examples shown here.

Figs 5 and 6 illustrate growth patterns produced with different levels of control over the probability of branching (but with the same growth rules). 

Introducing a limitation on the growth of tips modifies this colony morphology further, resulting in a structure of intermediate density (Fig. 7).  However, if this parameter is implemented its value must be larger than that of the branching threshold parameter for a viable, growing mycelium to result.

Interestingly, a similar effect to decreasing the threshold for preventing branching and growth can be achieved also by increasing the radius that defines ‘neighbouring tips’.  In this scenario, a sparser structure is produced with longer segments of hyphae before branching is initiated (Fig. 8).

By switching between parameter sets like those described above, it is possible to produce more complex structures. In this example (Fig. 9), three stages were employed:

Most parameter settings generate spherical colonies, and that includes a setting in which all controls are removed and growth and branching depend on randomised decisions. In other words, the basic spherical (circular in projection) morphology of the fungal colony arises without the need for global control of that morphology. However, the program does not limit us to spherical end-points.

A thin filament (Fig. 10) can be formed by setting the parameters that prevent growth and branching to high thresholds (i.e. the growth and branching of tips is made highly probable), but then limiting the time for which the tips can grow and branch. (There are two ways to limit the length of branches- first, by limiting the time they grow for, and second, by limiting the length the segments grow to. Both have the same effect.)

To create a number of tips from where the filament can extend any parameter set can be used that generates reasonably large numbers of tips per time unit.

A more prolifically-branched filament can be produced (Fig. 11) by increasing the threshold for preventing branching and growth (i.e. increasing the density of branches) and by allowing the tips to branch and grow for longer.  The branching probability can also be increased to accentuate the effect.

Increasing the time that the initial parameter set is run at the start, and then switching to a thin filament parameter set can produce a globular structure with thin ‘exploratory’ filaments emanating from it (Fig. 12).

Implementing the density field hypothesis for branching regulation represents a slightly different way of calculating the density of tips in the locality. The main practical difference between this form of computation and that in the examples discussed above is that when the density field hypothesis is used, local differentiation becomes difficult as the regulation applies to the whole mycelium. For instance, the structure illustrated in Fig. 9 would be impossible using this method, as it cannot prevent growth and branching in the ‘parent’ mycelium while allowing it in the ‘daughters’.  Thus, structures are produced that are uniformly distributed with branches and most branching appears to be dichotomous. Fig. 13 shows three examples in which the density field threshold for branching was varied to produce mycelia with different morphologies.

If negative autotropism is set to zero at the beginning of the simulation (switching this parameter to zero during a simulation produces different results) an interesting growth process can be seen.  This can be done with both methods of branching regulation, but the density field hypothesis is supposed in the examples shown here. The interesting feature of the growth of these mycelia is that they grow in one plane at a time.  That is to say, a straight, single hypha grows first with many dormant tips along it; then branches grow perpendicularly to produce a 2-dimensional disc-type structure; and finally more branches grow out perpendicularly from that to make a 3-dimensional structure. If a relatively high density field threshold is used, an ellipsoidal morphology is produced (Fig 14).

If the density field threshold is reduced somewhat, the result is a rod-like structure.

Again, switching between parameter sets in the course of a simulation generates interesting compound morphologies. For Fig. 16, three parameter sets were used:

For the structure shown in Fig. 17, an ellipsoid mycelium, as described in Fig. 14, was grown initially for 200 time units. Then the parameters were switched to a set that produces densely branched filaments (as in Fig. 11) for a further 100 time units.

Now, why don’t you try the program for yourself? If you copy the experiments described above you will get similar results (but remember, each experiment “grows” a new and unique “colony”, so you’ll never get exactly the same).

But you could think up your own experiments, couldn’t you?

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