18.15 Cyber fungi: mathematical modelling and computer simulation of hyphal growth
We have discussed how hyphal extension growth follows a few general relationships that are conveyed in relatively simple equations (see the section entitled Growth kinetics in Chapter 4; CLICK HERE to view now) so hyphal growth kinetics are well suited to mathematical modelling:
- Ē, the mean tip extension rate, = μmax (the maximum specific growth rate) multiplied by G, the hyphal growth unit.
- G is defined as the average length of a hypha supporting a growing tip.
- G consequently = Lt, total mycelial length, divided by Nt, the total number of tips.
In a fungal colony, the hyphal growth unit is approximately equal to the width of the peripheral growth zone, which is a ring-shaped peripheral area of the mycelium that contributes to radial expansion of the colony.
- In a mycelium that is exploring the substrate, branching will be rare and so G will be large. G is therefore an indicator of branching density.
- A new branch is initiated when the capacity for a hypha to extend increases above Ē, thereby regulating G to a uniform value indicative of the characteristic branching density of that fungus under those growing conditions.
All of these features of normal filamentous hyphal growth can be expressed algebraically in a vector-based mathematical model in which the growth vector of each virtual hyphal tip is calculated at each iteration of the algorithm by reference to the surrounding virtual mycelium. The program starts with a single hyphal tip, equivalent to a fungal spore. Each time the program runs through its algorithm the tip advances by a growth vector (initially set by the user) and may branch (with an initial probability set by the user).
In the Neighbour-Sensing program each hyphal tip is an active agent, described by its three dimensional position in space, length, and growth vector, that is allowed to vector within three dimensional data space using rules of exploration that are set (initially by the experimenter) within the program. The rules are biological characteristics such as:
- the basic kinetics of in vivo hyphal growth,
- branching characteristics (frequency, angle, position),
- tropic field settings that involve interaction with the environment.
The experimenter can alter parameters to investigate their effect on form; the final geometry is reached by the program (not the experimenter) adapting the biological characteristics of the active agents during the course of their growth, as in life. This is called the Neighbour-Sensing model and it brings together the basic essentials of hyphal growth kinetics into mathematical cyberfungus that can be used for experimentation on the theoretical rules governing hyphal patterning and tissue morphogenesis (Meškauskas, McNulty & Moore, 2004; Meškauskas, Fricker & Moore, 2004).
The Neighbour-Sensing model ‘grows’ a simulated cybermycelium using realistic branching rules decided by the user. As the cyberhyphal tips grow out into the modelling space the model tracks where they have been and those tracks become the hyphal threads of the cybermycelium. All of the positioning information is stored by the model as numerical data and so the data handling work becomes more and more extensive as branching produces more hyphal tips and the cybermycelium ‘grows’ in three dimensions on the computer monitor; it is this steady growth process that generates the very large amount of data.
The process of simulation is programmed as a closed loop. This loop is performed for each currently existing hyphal tip of the mycelium and the algorithm:
- Finds the number of neighbouring segments of mycelium (N). A segment is counted as neighbouring if it is closer than the given critical distance (R). In the simplest case we did not use the concept of the density field, preferring a more general formulation about the number of the neighbouring tips.
- If N<Nbranch (the given number of neighbours required to suppress branching), there is a certain given probability (Pbranch) that the tip will branch. If the generated random number (0..1) is less than this probability, the new branch is created and the branching angle takes a random value. The location of the new tip initially coincides with the current tip. This stochastic branch generation model is similar overall to earlier ones in which distance between branches and branching angles followed experimentally measured statistical distributions.
- Initial versions of the model did not implement tropic reactions (to test the kind of morphogenesis that might arise without this component). Later versions of the model tested how autotropic reactions affected the simulation.
Most models published so far simulate growth of mycelia on a two dimensional plane; the Neighbour-Sensing model, however, whilst being as simple as possible, is able to simulate formation of a spherical, uniformly dense fungal colony in a visualisation in three dimensional space. A description of the mathematics on which the model is based can be found in Moore, McNulty & Meškauskas (2006); we will not dwell on this aspect here. The complete interactive application is available for personal experimentation at this URL: http://www.world-of-fungi.org/Models/index.htm. This model is predictive and successfully describes the growth of hyphae, so confirming its credibility and indicating plausible links between the equations and real physiology (Meškauskas, Fricker & Moore, 2004).
The Neighbour-Sensing model successfully imitates the three branching strategies of fungal mycelia illustrated by Nils Fries in 1943 (Fig. 28).
|Figs 28. Simulation of the three different colony types described by Fries (1943). A shows the Boletus type (compared with Fig. 4.3), B the Amanita type (compared with Fig. 4.5) and C the Tricholoma type (compared with Fig. 4.4). The modelling parameters used for each of these simulations are described in the text. The simulation is the upper figure in each case.|
The Neighbour-Sensing model shows that random growth and branching (i.e. a model that does not include the local hyphal tip density field effect) is sufficient to form a spherical colony. The colony formed by such a model is more densely branched in the centre and sparser at the border; a feature observed in living mycelia. Models incorporating local hyphal tip density field to affect patterning produced the most regular spherical colonies. As with the random growth models, making branching sensitive to the number of neighbouring tips forms a colony in which a near uniformly dense, essentially spherical, core is surrounded by a thin layer of slightly less dense mycelia.
Using the branching types discussed by Fries (1943) as a comparison, the morphology of virtual colonies produced when branching (but not growth vector) was made sensitive to the number of neighbouring tips was closest to the so-called Boletus type (Fig. 28). This suggests that the Boletus type branching strategy does not use tropic reactions to determine patterning, nor some pre-defined branching algorithm. Evidently, hyphal tropisms are not always required to explain ‘circular’ mycelia (that is, mycelia that are spherical in three dimensions).
When the Neighbour-Sensing model implements the negative autotropism of hyphae, a spherical, near uniformly dense colony is also formed, but the structure differs from the previously mentioned Boletus type, being more similar to the Amanita rubescens type, characterised by a certain degree of differentiation between hyphae (Fig. 28):
- first rank hyphae tending to grow away from the centre of the colony;
- second rank hyphae growing less regularly, and filling the remaining space.
In the early stages of development such a colony is more star-like than spherical. It is worth emphasising that this remarkable differentiation of hyphae emerges in the visualisation even though all virtual hyphae are driven by the same algorithm. The program does not include routines implementing differences in hyphal behaviour.
Finally, when both autotropic reaction and branching are regulated by the hyphal density field, a spherical, uniformly dense colony is also formed. However, the structure is different again, such a colony being similar to the Tricholoma type illustrated by Fries (1943) (Fig. 28). This type has the appearance of a dichotomous branching pattern, but it is not a true dichotomy. Rather the new branch, being very close, generates a strong density field that turns the growth vector of the older tip away from the new branch.
Hence the Amanita rubescens and Tricholoma branching strategies may be based on a negative autotropic reaction of the growing hyphae while the Boletus strategy may be based on the absence of such a reaction, relying only on density-dependent branching. Differences between Amanita and Tricholoma in the way that the growing tip senses its neighbours may be obscured in life. In Amanita and Boletus types, the tip may sense the number of other tips in its immediate surroundings. In the Tricholoma type, the tip may sense all other parts of the mycelium, but the local segments have the greatest impact.
This model shows that the broadly different types of branching observed in the fungal mycelium are likely to be based on differential expression of relatively simple control mechanisms. The ‘rules’ governing branch patterning (that is, the mechanisms causing the patterning) are likely to change in the life of a mycelium, as both intracellular and extracellular conditions alter. Some of these changes can be imitated by making alterations to particular model parameters during the course of a simulation. By switching between parameter sets it is possible to produce more complex structures.
Experiments with the model simulated both colonial growth of the sort that occurs in Petri dish cultures (Fig. 29), and development of a mushroom-shaped ‘fruit body’ (Fig. 30). These experiments make it evident that it is not necessary to impose complex spatial controls over development of the mycelium to achieve particular geometrical forms. Rather, geometrical form of the mycelium emerges as a consequence of the operation of specific locally-effective hyphal tip interactions.
|Fig. 29. Simulation of colonial growth of the sort that occurs in Petri dish cultures. Oblique view (top) and slice of the colony (bottom), where secondary branching was activated at the 220 time unit. The secondary branches had negative gravitropism. For both primary and secondary branches the growth was simulated assuming negative autotropic reaction and density-dependent branching. If the density allowed branching, the branching probability was 40% per iteration (per time unit). The final age of the colony was 294 time units. Secondary branches are colour-coded red, and hyphae of the primary mycelium are coloured green (oldest) to magenta (youngest), depending on the distance of the hyphal segment from the centre of the colony (modified from Meškauskas, Fricker & Moore, 2004; reproduced with permission from Elsevier).|
|Fig. 30. Simulation of a mushroom primordium. A spherical colony was first grown for 76 time units. This was converted into an organised structure, similar to the developing mushroom stem by applying the parallel galvanotropism for 250 time units. Subsequent application of a positive gravitropic reaction formed a cap-like structure (1 000 time units)(modified from Meškauskas, McNulty & Moore, 2004; reproduced with permission from Elsevier).|
These computer simulations suggest that because of the kinetics of hyphal tip growth, very little regulation of cell-to-cell interaction is required to generate the overall architecture of fungal fruit body structures or the basic patterning of the mycelium. Specifically:
- Complex fungal fruit body shapes can be simulated by applying the same regulatory functions to all of the growth points active in a structure at any specific time.
- The shape of the fruit body emerges as the entire population of hyphal tips respond together, in the same way, to the same signals.
- No global control of fruit body geometry is necessary (Meškauskas, McNulty & Moore, 2004).
Updated December 17, 2016