Edge effects on the invasiveness of solid tumours

MetastasisCareful readers might have noticed that, until last night’s post, the blog was silent for an atypically long 10 days (17 days since I last posted). As usual, the primary culprit is laziness, but this time it is not alone! After my fun visit to the Integrated Mathematical Oncology Department of the Moffit Cancer Research Center, I have been working closely with Jacob Scott and David Basanta to finish up our first joint paper. The last week was particularly busy as we pushed the paper out for submission and posted a draft to the ArXiv.

We look at the effect of spatial structure, in particular a spatial boundary, on the evolutionary dynamics of motility in cancer. For a tumor, one of the key steps in going from a benign to malignant is gaining the ability to spread from one organ to another non-adjacent organ. To achieve this, a cancer cell has to transition from simple proliferative cells (AG) to a motile ones (INV). However, motility usually involves a cost to the organism. We show that spatial structure can lower this cost, and smaller neighborhood size at an edge can promote motile cells at the boundary even when they are absent in the tumour body.
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Hunger Games themed semi-iterated prisoner’s dilemma tournament

hungerGamesCodeWith all the talk surrounding it, crowdsourcing science might seem like a new concept and it might be true for citizen science efforts, but it is definitely an old trick to source your research to other researchers. In fact, evolutionary game theory was born (or at least popularized) by one such crowdsourcing exercise; in 1980, Robert Axelrod wanted to find out the best strategy for iterated prisoner’s dilemma and reached out to prominent researchers for strategy submissions to a round-robin tournmanet. Tit-for-tat was the winning strategy, but the real victor was Axelrod. His 1981 paper with Hamilton analyzing the result went on to become a standard reference in applications of game theory to social questions (at least outside of economics), agent-based modeling, and — of course — evolutionary game theory. Of Axelrod’s sizeable 47,222 (at time of writing) citations, almost half (23,370) come from this single paper. The tradition of tournaments continues among researchers, I’ve even discussed an imitation tournament on imitation previously.

The cynical moral of the tale: if you want to be noticed then run a game theory tournament. The folks at Brilliant.org — a website offering weekly olympiad-style challange problems in math and physics — took this message to heart, coupled it to the tried-and-true marketing technique of linking to a popular movie/book franchise, and decided to run a Hunger Games themed semi-iterated Prisoner’s dillema tournament. Submit a quick explanation of your strategy and Python script to play the game, and you could be one of the 5 winners of the $1,000 grand prize. Hooray! The submission deadline is August 18th, 2013 and all you need is a Brilliant account and it seems that these are free. If you are a reader of TheEGG blog then I recommend submitting a strategy, and discussing it in the comments (either before or after the deadline); I am interested to see what you come up with.
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Replicator dynamics of cooperation and deception

In my last post, I mentioned how conditional behavior usually implied a transfer of information from one agent to another, and that conditional cooperation was therefore vulnerable to exploitation through misrepresentation (deception). Little did I know that an analytic treatment of that point had been published a couple of months before.

McNally & Jackson (2013), the same authors who used neural networks to study the social brain hypothesis, present a simple game theoretic model to show that the existence of cooperation creates selection for tactical deception. As other commentators have pointed out, this is a rather intuitive conclusion, but of real interest here are how this relationship is formalized and whether this model maps onto reality in any convincing way. Interestingly, the target model is reminiscent of Artem’s perception and deception models, so it’s worth bringing them up for comparison; I’ll refer to them as Model 1 and Model 2.
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Microenvironmental effects in prostate cancer dynamics

Light_blue_ribbonI am currently visiting David Basanta and Jacob G. Scott at the Moffitt Cancer Center in Tampa, FL. I primarily study evolutionary game theory for the sake of evolutionary game theory. Sometimes I am curious about how it shapes our understanding of classic game theory, and how it broadly connects to other fields. Occasionally, I wander into pondering on economic or human questions, or at least sociobiology. However, the biggest application of EGT for most people is in biology, originally in the study of fisheries of sex-allocation and now even in medical fields like cancer research. Although I have briefly alluded to work on cancer before, it took the personal encouragement of David and Jacob for me to look at the field carefully. Naturally, I started with their paper. Half a year ago, I started drafting this post — my first on cancer — but it got lost in the perpetual “I’ll finish it up tomorrow” pile.

With only thirty minutes to spare, ‘tomorrow’ is finally today.

Over the last two days, David and I locked ourselves in his apartment and then office in the Integrated Mathematical Oncology Department. We discussed his papers on metastasis, the Warburg effect, and tumor-stroma interaction in prostate cancer — the topic of this entry.
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Evolving useful delusions to promote cooperation

This joint work with Marcel Montrey and Thomas Shultz combines — to be consistent with the interdisciplinary theme of this symposium — ideas from biology, economics, a little bit of cognitive science, and the approach is through applied mathematics. This post is a transcript of a presentation I gave on March 27th and covers part of my presentation today at Swarmfest.
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Warburg effect and evolutionary dynamics of metastasis

Why do cancers have high aerobic glycolysis?On Friday, I introduced you guys to the importance of motility in cancer metastasis. However, motility isn’t the whole story, another important factor is the type of respiration (energy generation) that the cell uses. As I gathered from conversations with Jacob Scott (and please correct me if I am wrong), when a tumor saturates the area it is in too quickly to recruit new blood vessels then the cancer cells can become oxygen deprived and be forced to switch from the full aerobic Kreb cycle to a focus on less efficient but anaerobic glycolysis. This shift is known as the Warburg effect and is used for diagnosis and monitoring of cancer progress. For an evolutionary game theorist, this means that when we study metastasis we need to consider three strategies: autonomous growth (AG; called ‘proliferative’ in the previous post), invasive (INV; called ‘motile’ in the previous post), and glycolytic (GLY; new to this this post).
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Game theoretic analysis of motility in cancer metastasis

As I am starting to write this post, the last of the 4th of July fireworks are winding down outside. I am currently traveling through New York on my way to Swarmfest 2013 in Orlando, FL and to visit David Basanta and Jacob Scott at the Integrated Mathematical Oncology Department of Moffitt Cancer Research Institute in Tampa, FL. The weather is unbearably hot already, so I imagine I will struggle in Florida, especially since I forgot all my shorts in Montreal!

MetastasisThe more important struggle, however, will be my lack of background in biology and medicine. On Jake’s suggestion, I decided to look at a paper of David’s on an evolutionary game theoretic approach to the emergence of motility in cancer cells. As far as I understand, one of the key steps in going from a benign tumor to a malignant cancer is metastasis or the ability of a cancer to spread from one organ to another non-adjacent organ. To achieve this, a cancer cell has to transition from a simple proliferative cell to a motile one. However, motility usually involves a cost to the organism.
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Evolve ethnocentrism in your spare time

Running an agent based simulation really isn’t that complex. While there’s no shortage of ready-made software packages for ABM (like Repast and NetLogo), all you really need is a good, high-level programming language and a code editor.

As you may have noticed from other blog posts, we have spent quite a bit of time studying agent based models of ethnocentric evolution. To coincide with the publication of our paper (Hartshorn, Kaznatcheev & Shultz, 2013) on the evolution of ethnocentrism in the Journal of Artificial Societies and Social Simulation (JASSS), we thought it would be fun to provide a hands-on tutorial so you can replicate the model yourself. There’s a lot to cover here, so we won’t get into the scientific description of the model itself, but you can read a good synopsis in my executive summary, or Artem’s general overview.

This post assumes no programming background, just a computer, patience, and some curiosity. That being said, you will be compiling a small Java program and modifying its source code, so if words like “compile,” “source code,” and “Java” strike terror in your heart, consider yourself forewarned. It’s actually not that scary. In Estonia they’re teaching kids to program in first grade, and you’re smarter than a first grader…right?!
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