Digital Biological X

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The control panel	Functionality
	Start and Stop the simulation. It is stopped initially
	Move the viewport
	Zoom and un-zoom the viewport.
	Toggle on and off ant's path visibility
	Speed up the simulation (and suppress display actualization)

The participating Ants

Name	Description	Typical path
BrontoAnt	BrontoAnt is a cellular ant made of only five neurons and three mediators. Even though it is simple, it can behave in our artificial world relying on the basics instincts: avoid obstacles, stay on path and do not accumulate in the corners. More than that, the ant can recognize sugar, anthill and create green paths leading from sugar. However, the ant creates the same paths everywhere, therefore the area is soon covered equally regardless anthill or sugar.
Tara	Tara is still pretty simple algorithmical ant, which leaves diminishing paths leading back home. Other than basic instincts it tends to stick close to the anthill. So the ant does not get lost out of path that often, however it tends to make circular paths and the ant walks on it until it meets another ant. Tara is not sensitive to green paths or presence of sugar. Tara's main task is not to find sugar or bring it home; it is rather focused on keeping the anthill well visible and accessible via red paths.
Thaya	The most advanced algorithmical ant. It leaves red paths leading back to the anthill and green paths leading back to the sugar. The ant remembers whether it is seeking anthill after visiting sugar (following the red path), or if it is seeking sugar (following the green path). Ideally, the paths overlay. However, when walking outside paths and after some time the ant reaches a point, when it does not create path any longer. Instead of returning, the ant usually gets lost.
Thuringia	Our first cellular ant. The ant can perform basic instincts such as avoid obstacles, stay on path and do not accumulate in the corners. The main idea was to slowly extend the path from anthill (red paths) and sugar (green paths). However, unlike Thrakia the ant does not know how to turn, and when he ceases making the path, he usually gets lost and returns home randomly.
Thrakia	The ant is implemented cellurarilly - eleven neurons and five mediators. The ant behaves according to the basic instincts and follows the red path, whenever it sees it. At the end of the path the ant tends to extend it and then after a while it turns and goes back. Thus the paths are extended each time and still lead back home, so the ants do not get lost. The ant does not recognize whether it is in the anthill neither it recognizes sugar or green paths. The paths are wider, so the ant does not have difficulties following it.
Theresia	Theresia is built on top of Thrakia as the most advanced cellular ant. The main intention was to cover the full functionality of Thaya. Therefore Theresia, in addition to Thrakia, follows green paths (precedence over red paths) and once it reaches sugar, it turns and goes back leaving green path as a trace to sugar behind it. At the time, when Theresia reaches the anthill, the trip is considered completed and the ant makes red path only.

Each ant in the simulation was designed to achieve its goal alone or in a group of ants of the same type (breed). However, the simulation allows putting all different ants together and simulating how they influence each other. It can easily happen, that one type is better at making paths to sugar, while another is much more efficient in extending the paths or keeping them strong and wide. Such a symbiosis delivers very different results than if we would simulate each breed separately. This is another argument, when simulation on lower level with multiple types of units (cells, organisms) can bring surprising results on higher level (organisms for cells, ecosystem for organisms). In this example, we even run a kind of an ecosystem on the cellular level (for BrontoAnt, Thrakia, Thuringia and Theresia).

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