Thursday, November 27, 2014

Genetic Assimilation.

1. [source]
Tomatoes sporadically produce fruit with horns, fleshy extensions adjacent to the calyx. Do a web search for, "Devil Tomato" and you will find several like the one in image #1. Generally, there is no evidence for these being the result of a genetic mutation. Rather, they represent the sort of thing that can happen when the normal development program of the fruit is disrupted in some way. Seeds taken from such a horned fruit will be no more likely to produce a plant that has similar fruit than seeds taken from any other fruit on the plant.

2. [source]
 There is a related species, Solanum mammosum, that has multiple such horns (image #2). (Though, there are example plants without horns.) The fruit of S. mammosum are rather toxic, so it wouldn't be a great idea to try and make a hybrid between the species and domesticated tomatoes.

3. [source]
Because there is the developmental potential for horns to be generated in tomatoes, there is the potential for a mutation to emphasize the trait. In the Tomato-TILING project, a few such mutations turned up (image #3). I'm not a professional plant developmental biologist, so I don't expect to get access to these interesting mutant seed lines any time soon.

I like the idea of looking for something that everyone else is trying to avoid. Every tomato breeder I've come across has been trying to breed away from a horned tomato, to produce a more "perfect" fruit shape, so I instead want a tomato that is all horns. I have the mental image of a tomato covered in fleshy projections featuring on a counter in some new science fiction movie.

As the previous examples have certain difficulties as a source for this trait, I've been looking for tomato lines which show a higher rate of these "deformations" to use as starting material in a project to breed a tomato that has the trait more consistently.

A rarely studied evolutionary model called "Genetic Assimilation" describes the process where an aberrant trait produced as the result of some stress is selected for and eventually becomes genetically fixed even without the presence of the stress. This mechanism sounds like Lamarckian evolution, except that it relies on the natural selection and the developmental plasticity of organisms…  rather than the personal experiences and intention of the organism that was favored by Lamark. It works because every trait is impacted by the genetic background, the combination of many subtle influences from other genes throughout the genome.

I frequent the Tomatoville forums, including the "Crosstalk: Tomatoville Research and Development™" forum. I started doing so because people there have a tendency to post lovely photos of the interestingly colored and patterned tomatoes they have been growing. Recently, a user was posted images from the results of a complex cross (["Pink Furry Boar" x "Ananas Noir"] x "Bosque Green Cherry") that they were working with. One of the diverse progeny they grew (image #4) had horns on 4 of the 20 fruit. 20% is a far higher rate than I'd otherwise come across, so I asked for a few seeds.

In a few years, I'll have a better idea of where this project is going. The good thing is that I can eat all the rejects along the way.



References:
  1. Genetic Assimilation:
    1. http://jeb.biologists.org/content/209/12/2362.full
    2. http://en.wikipedia.org/wiki/Genetic_assimilation
    3. http://eebweb.arizona.edu/faculty/badyaev/ecol596e/assimilation.pdf
    4. In tiger snakes: http://blogs.discovermagazine.com/notrocketscience/2009/10/30/big-headed-tiger-snakes-support-long-neglected-theory-of-genetic-assimilation/
    5. In fruit flies: Waddington, C. H. (1942) Canalization of development and the inheritance of acquired characters. Nature 150:563-565.
  2. Horned Tomatoes:
    1. http://www.tomatoville.com/showthread.php?t=34162
    2. https://www.flickr.com/photos/farflung/6462879911/
  3. Solanum mammosum
    1. https://www.flickr.com/photos/30372914@N03/3895429577/
    2. https://www.flickr.com/photos/22012266@N02/7164709249/
  4. Tomato Tiling project
    1. http://tilling.ucdavis.edu/index.php/Tomato_Tilling
  5. Tomato Varieties:
    1. Pink Furry Boar
    2. Ananas Noir
    3. Bosque Green Cherry

Wednesday, November 26, 2014

A Requiem.

Jonathan Abbey, my brother.
I've never really fit in with those around me. I accept this and don't need those around me to think the way I do. All I need is for them to accept me for who I am. I have had the good fortune to find someone to share my life with who does this. Barring some unexpected misfortune, by this time next year, she and I will be married.

The way I think about the world is very rarely linear. This has caused conflict between me and my academic advisor, as she wants me to construct lists of what I am working on and how I will set about completing then. I generally think in images, patterns, and relationships. When I am working hard on a puzzle, I tend to see my thought processes as some form of abstract math, even though I don't always have the vocabulary to convey that math to those around me. There are conceptual problems that I've thought about for a while and came to solutions that I'm absolutely certain are true, but I don't yet know how to show them to anyone else. Sometimes, I don't even have a glimpse of how to explain.

There have only ever been a few people that I looked to as role models, for inspiration. Athletes, artists, politicians, and other people who arguably have large positive (or negative) impacts on the people of the world have never felt like role models to me. The people I have ever felt this sort of connection with, that remind me of how I see and want to see the world around me, I can count (in no particular order) on one hand.
  1. Albert Einstein.
  2. Richard Feynman.
  3. Stephen Hawking.
  4. Jonathan Abbey.
None of them were biologists. Perhaps this shouldn't be a surprise, as I often don't fit the standard model of a biologist all that well. They all shared a clarity and depth of thought that I aspired to.

The first three are names you are probably familiar with. Well, you're probably familiar with them if you've had a long-running interest in science and how the universe works. Einstein and Feynman died before I became aware of them and I don't expect to ever meet Stephen Hawking. (I wouldn't know what to do or say if I did.) It was only when I started learning about how they came to the discoveries they're known for that I started looking to them as role models.

Jonathan Abbey was the older of my two older brothers, my parents' first child. A few weeks ago, he died unexpectedly. The proximate cause of his death was cardiac disease, atherosclerosis. This is what is colloquially referred to as "hardening of the arteries". The ultimate cause of his death was his inability or refusal to keep to the schedule for his medication. He had type-1 diabetes and ankylosing spondylitis, two auto-immune diseases which amplify the effect of high blood-pressure on the damage to cardiac arteries which causes atherosclerosis. He went to the emergency room in the week before with chest pain. They gave his heart a clean bill of health and sent him home.

He spent a great deal of time thinking about thinking (meta-cognition). He encouraged me to pursue a PhD and was very proud of the work I have been doing when I last visited with him. He lamented his own choice of not pursuing a higher academic degree for himself. His professional work involved designing and managing very complex systems. He liked video games, music, and poetry as hobbies. He spent time thinking very deeply about people and how the world works. He pursued knowledge and argued vehemently against "belief". He strongly felt that what was real, what was verifiable, was most important. He was a good father, but maybe not so good of a husband or boyfriend. Many people who knew him thought he was a genius. He was my brother and I'm having a hard time dealing with his passing.

I've gotten past the shock. I've gotten past the sporadic moments of denial. I've even gotten past the moments of anger. I never really went through a bargaining stage. Now, I mostly just feel old. I think this is a mix of depression and acceptance.

I don't believe in a soul or an afterlife and neither did he. Attempts to comfort me by saying, "he's in a better place", in any form or variation are misplaced. Such efforts will anger me, even if not obviously so. If I know you, they will discourage me from interacting with you in the future. If I don't know you, I'll just delete your comment and maybe ban you.

I'm in the very final stages of completing my PhD in the department of Genetics at the University of Minnesota. By the time I post this, I'll have handed off my written thesis to my committee for review. In another two weeks, I'll defend my thesis and be done with it.

I'm sad that my brother won't get to know.

Monday, November 17, 2014

What is a chicken?


We refer to them by the species name Gallus gallus domesticus, but there was a time before they had any connection to us. The wild species is Gallus gallus, also known as the Red Jungle Fowl, and it can still be found running around the wilds of south-east Asia.

There is genetic evidence that modern chickens arose from multiple independent domestication events. The diversity of alleles found in domestic chickens encompasses those found in wild populations of G. gallus spread through India (G. g. murghi), Burma (G. g. spadiceus), and Tailand (G. g. gallus). This is best explained by the early incorporation of Red Jungle Fowl from different regions into the common pool of chickens being cared for by people.

It turns out that there are three other related species of jungle fowl (grey, Ceylon, and green) roaming the area of south-east Asia. A trait found in domesticated chickens that causes yellow skin on the legs and feet is due to an allele which shows most similarity to an allele found in the Grey Jungle Fowl.

A. Green stars indicate putative domestications.
B. Domesticated chicken.
C. Red Jungle Fowl. (Range in red in A.)
D. Grey Jungle Fowl. (Range in grey in A.)
At least four different populations across two (of what we consider) separate species contributed to modern domesticated chickens.

How could the process of domestication start in multiple places at the same time? Well... it can't, but it can happen close enough in time to be indistinguishable to modern researchers.

It is a common pattern in domestication for the idea of domesticating an animal or plant to spread faster than the newly domesticated organism can spread. This results in multiple independent domestication of a single species, or of similar species, found across a wide area.

Cattle appear to have been domesticated two or three times (from Bos tauros, B. indicus, and possibly B. africanus). Sheep and goats appear quite distinct to us now, but when they were domesticated, they were very similar creatures.

Chile peppers have been domesticated at least five times (Capsicum annum, C. chinense, C. frutsecens, C. bacatum, C. pubescens). Squash were domesticated at least five times (Curcurbita pepo, C. moschata, C. maxima, C. mixta, C. ficifolia). Carrots (Daucus carota), parsnips (Pastinaca sativa), celery (Apium graveolens), parsley (Petroselinum crispum), Dill (Anethum graveolens), and chervil (Anthriscus cerefolium) all belong to the family Apiaceae and look very similar in their wild state.

So.  What is a chicken?

It is an example of how the rapid spread of ideas through human culture impacts the process of wild things becoming integral to our civilization.



References
  1. http://en.wikipedia.org/wiki/Red_junglefowl
  2. Multiple domestication : http://www.biomedcentral.com/1471-2148/8/174
  3. Hybrid between red and grey jungle fowl : http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000010 
  4. Cattle : http://archaeology.about.com/od/domestications/qt/cattle.htm
  5. Chile peppers : http://archaeology.about.com/od/cbthroughch/qt/Chili-Peppers.htm
  6. Squash : http://en.wikipedia.org/wiki/List_of_gourds_and_squashes
  7. Apiaceae : http://science.jrank.org/pages/1240/Carrot-Family-Apiaceae-Edible-species-in-carrot-family.html

Tuesday, November 11, 2014

Evolution

While thinking about the evolvability of different artificial life simulations, as discussed some in my last posting, I realized that it would be helpful to talk about what is required for a system to evolve. It comes down to four basic traits.

1. Reproduction: Some unit in the system has to reproduce. This unit could be bacterial cells in your gut, or it could be numerical representations in a computer. (Even fire can be described as reproducing when it spreads through a house or forest.)

2. Inheritance: During reproduction, each new unit in the system has to gain traits from its parent(s). The traits could be hidden, as in recessive alleles, or it could be obvious, as in dominant alleles. The number of parents can be one or more than one. (We have two, but maybe some aliens have three or more.)

3. Mutation: At some point in the reproductive cycle, there has to be the potential for changes in the traits (mutations) that are inherited.

4. Death: Death is generally required to remove individuals from a population, thus freeing up room for the next generation. However, there are scenarios where death isn't required. If the population is continuously expanding into new territory, the front-line sub-population can evolve over time without individual death. In this case, the older organisms being left behind fills the same role of actual death.



It is relatively easy to prove mathematically that a system with these four traits will experience evolution.

Lets give it a go in a simulation that has a maximum population of four organisms represented by letters and driven by the following rules.
  1. Reproduction with inheritance: A -> AA; B -> BB
    • A or B can duplicate.
  2. Mutation: A -> B.
    • A can mutate into B.
  3. Death: A -> A 
    • Only A can die.
We start the simulation with "A" .

"A" -> "AA" -> "AAAA" -> "AAAB" -> "AAAB" -> "AB" -> "AABB" -> "AABB" -> "ABB" -> "ABBB" -> "ABBB" -> "BBBB"

This may not look like the sort of math you are familiar with, but it is math nonetheless. Math is the manipulation of abstract symbols that represent precise concepts with the extremely rigid rules of logic. 2+2 always equals 4. A system with the described traits will always experience evolution.

Now, this little toy system I've described has an extremely low evolvability. The starting state of the system ("A") does meet the four requirements and thus evolves. However, once the system has reached the final state ("BBBB"), it no longer meets the four requirements and thus cannot evolve further.



If you argue that life doesn't evolve, then you are logically arguing that life does not meet one of the four requirements discussed above. Unequivocally, life meets the four requirements.

Life evolves. The math doesn't provide any other possibility.

Wednesday, November 5, 2014

Artificial Life

Way back in 1989, I read an article in Scientific American ("Mathematical Recreations") which described a simulation which showed evolution of a very simple "bug".

The genome of the bugs consisted of six numbers that weighted a random selection of which direction each bug would go in the next time step (forward, left, hard-left, backward, hard-right, and right). The bugs would eat "bacteria" which rained down around them, reproducing if they ate enough, but starving to death if they didn't.

Along the way, bugs would evolve different strategies depending on the environment they found themselves in. If they found themselves in a highly food-rich environment, it was more advantageous for them to stay in the same place. If they found themselves in food-poor environments, it was more advantageous for them to keep moving whatever direction they were going.

I thought this was a really cool idea and set about writing my own version of the program. My bugs had a genome of eight numbers, but otherwise they were identical to the original. I worked on the project periodically over several years, eventually producing the version seen at left in 2006. Thousands of bugs (green) cruising around a screen full of bacteria/food (blue) that rains evenly over the screen (at a higher rate in gardens) can be seen at left.

In the later versions of the program, I took some effort at visualizing how the bugs were evolving. At first, I simply plotted the population size over time and compared the resulting curves across different runs of the program. I figured out that the system would consistently support a higher number of bugs if they were allowed to mutate instead of just replicate. The bugs reached population densities about 10% higher than in the no-mutation control.

I then realized I could convert the genome into a 2D coordinate, describing the propensity of each bug to go in each direction. This would let me observe the behavior of the population at a glance.


The video at right starts with the population of genomes clustered around the center. They have no initial tendency to go any where and walk around randomly.

The distribution soon widens to the right and left, as many of the bugs replicating in the gardens are turning in tight circles.

By about 0:02, the population has split into three groups. The left and right have moved downwards as the garden-bugs are increasingly turning around during each time step, going nowhere at all. The center region has started to move upward, showing the specialization of the wide-open-bugs for moving forward at increased rates.

 The three populations continue moving through the remainder of the video. The wide-open-bugs experience large periodic population cycles as they deplete their food source and die back, while the garden-bugs with their much more rich food source show a more constant population over time.



The video above shows a version where mutations are introduced every time a bug divides. If mutations are introduced only when a bug is starving, which potentially allows a single bug to mutate into a better strategy and so keep on living, a much tighter population distribution results. In the figure at left, three separate runs have been overlaid (in red, green, and blue, respectively). In these simulations, six gardens were available, but only a limited number were colonized. Each colony is represented by a single cluster of genomes in the lower half of the image.



Being able to experiment with evolutionary concepts on my computer helped me learn about biology and represented a stepping stone on my way to my current approaches to understanding biology. This system is limited, it can only evolve to a few end points.

Other systems can evolve in a much more complicated way. In Tierra, the evolution of short computer programs develops into a rich ecosystem of interacting organisms. Parasites evolve, followed by hosts that are resistant to those parasites.

The term "evolvability" is used to describe these differences in how different systems can evolve. Our biology has a very high evolvability, while my little simulation has a very low evolvability. What allows a system to have a higher evolvability seems to be related to how complicated the interaction of an "organism" is to its environment, as well as how complicated its genome is.

My bugs can only interact with the density of food and their genome is eight numbers. The bacteria living in my gut can interact with me, my food, other bacteria, radiation from the sun, etc. and their genome can grow or shrink as needed. Simulations with higher evolvability invariably show more of the features that we see in living things and so are more useful/interesting for studying real living things

Systems that show any evolvability at all are interesting and included in the subject of "artificial life". Hypothetically, you could be a "biologist" and never look at a messy living thing. I like studying the messy living things too.  ;-)



Links:
  1. http://martin-gardner.org/MGSAindex.html
  2. http://lifesciassoc.home.pipeline.com/instruct/evolution/
  3. Tierra :
  4. Critters :
  5. https://www.youtube.com/watch?v=ZpW_ojpmTWk
  6. Polyworld : https://www.youtube.com/watch?v=_m97_kL4ox0