Red King: Host/Parasite co-evolution citizen science

A new project begins, on the subject of ecology and evolution of infectious disease. This one is a little different from a lot of Foam Kernow’s citizen science projects in that the subject is theoretical research – and involves mathematical simulations of populations of co-evolving organisms, rather than the direct study of real ones in field sites etc.

The simulation, or model, we are working with is concerned with the co-evolution of parasites and their hosts. Just as in more commonly known simulations of predators and prey, there are complex relationships between hosts and parasites – for example if parasites become too successful and aggressive the hosts start to die out, in turn reducing the parasite populations. Hosts can evolve to resist infection, but this has an overhead that starts to become a disadvantage when most of a population is free of parasites again.

Example evolution processes with different host/parasite trade-offs.

Over time these relationships shift and change, and this happens in different patterns depending on the starting conditions. Little is known about the categorisation of these patterns, or even the range of relationships possible. The models used to simulate them are still a research topic in their own right, so in this project we are hoping to explore different ways people can both control a simulation (perhaps with an element of visual live programming), and also experience the results in a number of ways – via a sonifications, or game world. The eventual, ambitious aim – is to provide a way for people to feedback their discoveries into the research.


A cryptoweaving experiment

Archaeologists can read a woven artifact created thousands of years ago, and from its structure determine the actions performed in the right order by the weaver who created it. They can then recreate the weaving, following in their ancestor’s ‘footsteps’ exactly.

This is possible because a woven artifact encodes time digitally, weft by weft. In most other forms of human endeavor, reverse engineering is still possible (e.g. in a car or a cake) but instructions are not encoded in the object’s fundamental structure – they need to be inferred by experiment or indirect means. Similarly, a text does not quite represent its writing process in a time encoded manner, but the end result. Interestingly, one possible self describing artifact could be a musical performance.

Looked at this way, any woven pattern can be seen as a digital record of movement performed by the weaver. We can create the pattern with a notation that describes this series of actions (a handweaver following a lift plan), or move in the other direction like the archaeologist, recording a given notation from an existing weave.

A weaving and its executable code equivalent.

One of the potentials of weaving I’m most interested in is being able to demonstrate fundamentals of software in threads – partly to make the physical nature of computation self evident, but also as a way of designing new ways of learning and understanding what computers are.

If we take the code required to make the pattern in the weaving above:

(twist 3 4 5 14 15 16)
(weave-forward 3)
(twist 4 15)
(weave-forward 1)
(twist 4 8 11 15)

(repeat 2
 (weave-back 4)
 (twist 8 11)
 (weave-forward 2)
 (twist 9 10)
 (weave-forward 2)
 (twist 9 10)
 (weave-back 2)
 (twist 9 10)
 (weave-back 2)
 (twist 8 11)
 (weave-forward 4))

We can “compile” it into a binary form which describes each instruction – the exact process for this is irrelevant, but here it is anyway – an 8 bit encoding, packing instructions and data together:

8bit instruction encoding:

Action  Direction  Count/Tablet ID (5 bit number)
0 1         2              3 4 5 6 7 

Action types
weave:    01 (1)
rotate:   10 (2)
twist:    11 (3)

forward: 0
backward: 1

If we compile the code notation above with this binary system, we can then read the binary as a series of tablet weaving card flip rotations (I’m using 20 tablets, so we can fit in two instructions per weft):

0 1 6 7 10 11 15
0 1 5 7 10 11 14 15 16
0 1 4 5 6 7 10 11 13
1 6 7 10 11 15
0 1 5 7 11 17
0 1 5 10 11 14
0 1 4 6 7 10 11 14 15 16 17
0 1 2 3 4 5 6 7 11 12 15
0 1 4 10 11 14 16
1 6 10 11 14 17
0 1 4 6 11 16
0 1 4 7 10 11 14 16
1 2 6 10 11 14 17
0 1 4 6 11 12 16
0 1 4 7 10 11 14 16
1 5

If we actually try weaving this (by advancing two turns forward/backward at a time) we get this mess:


The point is that (assuming we’ve made no mistakes) this weave represents *exactly* the same information as the pattern does – you could extract the program from the messy encoded weave, follow it and recreate the original pattern exactly.

The messy pattern represents both an executable, as well as a compressed form of the weave – taking up less space than the original pattern, but looking a lot worse. Possibly this is a clue too, as it contains a higher density of information – higher entropy, and therefore closer to randomness than the pattern.