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Revision as of 06:35, 16 October 2018
Growth Modeling
Under the lens
Filamentous fungi have a very characteristic growth pattern on solid medium, as they appear as a large network of interwoven filaments(3). If we can accurately predict how a fungus will grow over time, it will allow us to determine the optimal growth conditions to gain the best end product. Therefore, we have investigated how the fungal morphology changes over time at two levels of details.
Microscopic view
One of our models focuses on the morphology at the hyphal level by simulating the movement of hyphal tips, branching rates, extension rates and the density levels during a growth period in two dimensions. All of the code scripts can be found on our GitHub repository.
The mycelium of a fungus consists of many interwoven hyphae, and the density depends on how many filaments there are in a given location. In figure 1 below, it is possible to see microscopic pictures of fungal mycelium. Three different levels of zoom illustrate how the network looks, where it can be observed how they interlink and how a fungal filament can branch into more.
Fig. 1: - Snapshots of mycelium development of Aspergillus oryzae. These are representative microscopic images of how a network of intertwined hyphal filaments could look in a microscope.
Simulation of the mycelium development
Fungal growth is initiated by $n$ number of spores, and a branch will start to extend from each of the spores added to the space. Following along one of these branches originating from a single spore, the hyphae will grow in a direction $\theta$ with a tip extension $r_{tip, i}$. A branching event, in which a new branch is formed from the first branch, can occur with a probability $q$.
Tip extension rate is calculated by using the equation below, which considers growth kinetics for the fungi and the amount of substrate available. It essentially outputs the accelerated growth dependent on the amount of substrate available, where the accelerated growth equation depends on fungal kinetics and branch lengths (1, 3).
\begin{equation}
r_{tip, i} = \bigg(k_{tip,1} + k_{tip,2}\cdot \frac{l_{br,i}}{l_{br, i} + K_t}\bigg)\cdot\bigg(\frac{S}{S+K_s}\bigg)
\end{equation}
$k_{tip,1}$ is the initial tip extension rate of the branch and $k_{tip,2}$ is the difference between the maximum extension rate and $k_{tip,1}$ (1, 3). $K_t$ symbolizes the time it takes to reach half of the maximum extension rate. The length of branch $i$ described by $l_{br,i}$. $S$ is the substrate concentration and $K_S$ corresponds to the substrate concentration to reach half of the maximum growth level (4).
As the starting coordinates of this simulation are $(x_0, y_0)$ and end coordinates $(x, y)$, the length of branch $i$ can be calculated as the distance between two points:
\begin{equation}
l_{br, i} = \sqrt{(x-x_0)^2 + (y-y_0)^2}
\end{equation}
By dividing the growth area into a grid of $w\cdot h$ areas, it is possible to investigate the uptake of substrate, hyphal movement and the development of biomass through the simulation.
At the start of the simulation, it can be assumed that the initial substrate concentration $S_0$ will be distributed evenly across the grid. When the spores are placed randomly, the branches start to develop by using the substrate available around the hyphal tip. So for each branch, it is checked which tip is in which grid and whether there is any substrate available. That means that one can track the substrate depletion in each grid, and when there is no more substrate in one of the areas of the grid, the hyphal tips located there can no longer grow and can be considered as inactive hyphae. If the total area around the mycelium no longer contains substrate, the growth will simply stop.
In the same way as tracking substrate depletion, biomass production can also be viewed in a grid. But instead of calculating a loss of substrate, each new branch extension is considered a gain in biomass or the density $d$. It should show the same mechanics as the substrate depletion, as these two are directly related to each other by the equation $l_{br,i}$.
As the fungi grow in size, the computational cost also increases. We were limited in running the simulation due to the power of our computers, thus resulting in us introducing two restrictions: the number of hyphae $M$ and the number of steps in the simulation $N$.
Results
The simulation ran with the parameters listed in table 1 [below], which painted a detailed picture of the time history of growth from the spore to the densely branched network of hyphal filaments. The parameters are based on growth of Aspergillus oryzae in Spohr (2).
| Parameter | Value | Source or rationale |
|---|---|---|
| $k_{tip,1}$ | 80 $\mu m\cdot tip^{-1}\cdot h^{-1}$ | Taken from Spohr (2) |
| $k_{tip,2}$ | 75 $\mu m\cdot tip^{-1}\cdot h^{-1}$ | Taken from Spohr (2) |
| $K_t$ | 5 $\mu m$ | Estimated |
| $S_0$ | 50000 mg/L | Estimated |
| $K_S$ | 200 mg | Estimated |
| $M$ | 100000 | Set as a simulation limit |
| $N$ | 5000 | Set as a simulation limit |
| $q$ | 00.05 | Estimated |
Table 1: - Parameters in a typical simulation run.
The end results can be viewed in figure 2 below, where three animations are shown of the hyphal development: hyphal movement and locations, biomass development and substrate depletion. It is the same mycelium simulation in all three animations, where we can observe that the density increases as the substrate level decreases.
Fig. 2a: - Hyphal development over time from 10 spores.
Fig. 2b: - Density development as the hyphae grows. The darker the color the higher the density.
Fig. 2c: - The depletion of substrate over time, as the fungus uses the substrate to extend its hyphal network. The lighter color indicates lower levels of substrate.
Figure 3 shows how that the hyphal lengths in the beginning of the growth simulation are increasing, whereas there is initially not that many hyphal tips. But as time progresses, the hyphal tips start to increase drastically due to there being more and more hyphae and the original branches being longer. The frequencies of hyphal lengths and branching events can be seen in figure 4, where the majority of the hyphal elements are very short and haven’t branched that much. Those that are longer are also those which have branched more often than the rest. The substrate level decreases as the amount of biomass increases, where this pattern can be observed in figure 5. All of the substrate is not gone, but that is due to the fact that the mycelium growth was not that efficient in using all the substrate in the given simulation time.
Fig. 3: - The number of hyphal tips (red) and total length of all hyphae (black) over time in the simulation.
Fig. 4a: - The frequencies of hyphal lengths, where it can be seen that the majority of the lengths are lower than $\mu$m.
Fig. 4b: - The frequencies of the number of branching events per hyphae, where most of the branches only have less than 5 events and those that have more are the oldest branches in the simulation.
Fig. 2b: - The increase in mycelium biomass over time, where the curve follows an exponential growth mechanism.
Fig. 2c: - The substrate level over time decreases as the mycelium develops.
Substrate dependent growth
The fungus can grow and fill out the space since the substrate concentration is sufficient to support this pattern. If there is not enough substrate available, it is impossible for the fungus to fill out the space. This can be illustrated in the simulation if there is not enough substrate available, for example in figure 3 the substrate concentration is a fifth of the level in figure 6. As can be observed, most of the hyphae uses up all the substrate available in their grid area, but they do not grow into another grid area due to them being locked in.
Fig. 6: - An example of substrate limiting growth, where the hyphae uses all available substrate in their squares and are therefore unable to escape to areas with more substrate.
Future development
The model shows how the fungi grows in 2D, so it can be interpreted to work as looking at a petri dish in detail. If the model were expanded into 3D, the result could be related to filling out a form and still studying the hyphal interactions at a very detailed level. 3D models of fungal growth do already exist, for instance see Lejeune (3) that simulates the growth of the filamentous fungi Trichoderma reesei.
There are many different factors that influence the growth kinetics and this model only includes a few of them. Parameters such as temperature or oxygen levels could be implemented to get the simulation to work more realistically.
(1) Lejeune, R., Nielsen, J. og Baron, G. V. (1995) “Morphology of Trichoderma reesei QM 9414 in submerged cultures”, Biotechnology and Bioengineering, 47(5), s. 609–615. doi: 10.1002/bit.260470513.
(2) Spohr, A., Dam-Mikkelsen, C., Carlsen, M., Nielsen, J. og Villadsen, J. (1998) “On-line study of fungal morphology during submerged growth in a small flow-through cell”, Biotechnology and Bioengineering, 58(5), s. 541–553. doi: 10.1002/(SICI)1097-0290(19980605)58:5<541::AID-BIT11>3.0.CO;2-E.Lejeune, R.
(3) Lejeune, R. og Baron, G. V. (1996) “Simulation of growth of a filamentous fungus in 3 dimensions.”, Biotechnology and bioengineering, 53(2), s. 139–50. doi: 10.1002/(SICI)1097-0290(19970120)53:2<139::AID-BIT3>3.0.CO;2-P.
(4) Monod, J. (1949) “The Growth of Bacterial Cultures”, Annual Review of Microbiology. Annual Reviews 4139 El Camino Way, P.O. Box 10139, Palo Alto, CA 94303-0139, USA , 3(1), s. 371–394. doi: 10.1146/annurev.mi.03.100149.002103.