Microscopic computing in cells and with self-assembling DNA tiles

One of the three goals of natural algorithms is to implement computers in non-electronic media. In cases like quantum computing, the goal is to achieve a qualitatively different form of computing, but other times (as with most biological computing) the goal is just to recreate normal computation (or a subset of it) at a different scale or in more natural ways. Of course, these two approaches aren’t mutually exclusive! Imagine how great it would be if we could grow computers on the level of cells, or smaller. For starters, this approach could revolutionize health-care: you could program some of your own cells to sense and record your internal environment and release drugs only when necessary. It could also alter how we manufacture things; if you throught 3D printers are cool, what if you could program nanoscale assemblies?
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EGT Reading Group 41 – 45 and a photo

In recent months, TheEGG blog has morphed into a medium for me to share cool articles and quick (and sometimes overly snarky) reviews. However, I still remember its original purpose to accompany the EGT Reading Group that I launched at McGill University in 2010. Next week, we will have our 46th meeting, and so I am taking a short break from reviewing the 2nd workshop on Natural Algorithms and the Sciences to give you a quick recap of what we’ve read since the last update:
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Mathematical models of running cockroaches and scale-invariance in cells

I often think of myself as an applied mathematician — I even spent a year of grad school in a math department (although it was “Combinatorics and Optimization” not “Applied Math”) — but when the giant systems of ODEs or PDEs come a-knocking, I run and hide. I confine myself to abstract or heuristic models, and for the questions I tend to ask these are the models people often find interesting. These models are built to be as simple as possible, and often are used to prove a general statement (if it is an abstraction) that will hold for any more detailed model, or to serve as an intuition pump (if it is a heuristic). If there are more than a handful of coupled equations or if a simple symmetry (or Mathematica) doesn’t solve them, then I call it quits or simplify.

However, there is a third type of model — an insilication. These mathematical or computational models are so realistic that their parameters can be set directly by experimental observations (not merely optimized based on model output) and the outputs they generate can be directly tested against experiment or used to generate quantitative predictions. These are the domain of mathematical engineers and applied mathematicians, and some — usually experimentalists, but sometimes even computer scientists — consider these to be the only real scientific models. As a prototypical example of an insilication, think of the folks at NASA numerically solving the gravitational model of our solar system to figure out how to aim the next mission to Mars. These models often have dozens or hundreds (or sometimes more!) coupled equations, where every part is known to perform to an extreme level of accuracy.
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Distributed computation in foraging desert ants

For computer scientists, ants are most familiar from ant colony optimization. These algorithms rely on simulating how ants lay, follow, and modify pheromone trails to find efficient paths from their hives to food sources. Hence, it might come as a surprise that this is not a universal feature of ants. The cataglyphis niger desert ant makes its home in the deserts of the middle east where the constantly shifting terrain makes pheromone trails ineffective outside of the nest. As such, all communication is done inside the hive with the ants being almost completely autonomous once they wander into the outside world. This makes them a perfect animal for looking at distributed computing and the problem of coordinating action in a noisy environment with a limited amount of computation.
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Computer science on prediction and the edge of chaos

With the development of statistical mechanics, physicists became the first agent-based modellers. Since the scientists of the 19th century didn’t have super-computers, they couldn’t succumb to the curse of computing and had to come up with analytic treatments of their “agent-based models”. These analytic treatments were often not rigorous, and only a heuristic correspondence was established between the dynamics of macro-variables and the underlying microdynamical implementation. Right before lunch on the second day of the Natural Algorithms and the Sciences workshop, Joel Lebowitz sketched how — for some models — mathematical physicists still continue their quest to rigorously show that macrodynamics fatefully reproduce the aggregate behavior of the microstates. In this way, they continue to ask the question: “when can we trust our analytic theory and when do we have to simulate the agents?”
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Quasi-delusions and inequality aversion

Patient M: It’s impossible —- no one could urinate into that bottle -— at least no woman could. I’m furious with her [these are the patient’s emphases] and I’m damned if I am going to do it unless she gives me another kind of bottle. It’s just impossible to use that little thing.

Analyst: It sounds as if a few minutes of communication with the nurse could clear up the realistic part of the difficulty—is there some need to be angry with the nurse and keep the feeling that she has done something to you?

Patient M: The ‘impossibility’ of using the bottle could be gotten over by using another—or I could use a funnel or a plastic cup and pour it into the bottle. But I just won’t. It makes me so mad. If she wants that sample, she is going to have to solve that problem. [Sheepishly] I know how irrational all this is. The nurse is really a very nice person. I could easily talk to her about this, and/or just bring in my own container. But I am really so furious about it that I put all my logic and knowledge aside and I feel stubborn—I just won’t do it. She [back to the emphasis] can’t make me use that bottle. She gave it to me and it’s up to her to solve the problem.

The above is an excerpt from a session between psychoanalyst Leonard Shengold (1988) and his patient. The focus is on the contrast between M’s awareness of her delusion, and yet her continued anger and frustration. Rationally and consciously she knows that there is no reason to be angry at the nurse, but yet some unconscious, emotional impulse pushes her to feel externalities that produce a behavior that she can recognize as irrational. This is a quasi-delusion.
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Algorithmic view of historicity and separation of scales in biology

A Science publications is one of the best ways to launch your career, especially if it is based on your undergraduate work, part of which you carried out with makeshift equipment in your dorm! That is the story of Thomas M.S. Chang, who in 1956 started experiments (partially carried out in his residence room in McGill’s Douglas Hall) that lead to the creation of the first artificial cell (Chang, 1964). This was — in the words of the 1989 New Scientists — an “elegantly simple and intellectually ambitious” idea that “has grown into a dynamic field of biomedical research and development.” A field that promises to connect biology and computer science by physically realizing John von Neumann’s dream of a self-replication machine.

makingBilayer
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Natural algorithms and the sciences

Today, I am passing through New York City on my way to Princeton’s Center for Computational Intractability for a workshop on Natural Algorithms and the Sciences (NA&S). The two day meeting will cover everything from molecular algorithms for learning and experiments on artificial cells to bounded rationality in decision-making and the effects of network topology on the evolution of collective migration. In other words, right at home for TheEGG blog. The full mission statement:

The workshop will bring together researchers from computer science, mathematics, physics, biology, and engineering to explore interactions among algorithms, dynamical systems, statistical physics, and complexity theory (in all senses of the term).

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Ethnocentrism, religion, and austerity: a science poster for the humanities

Artem Kaznatcheev and I presented a poster on May 4th at the University of British Columbia to a highly interdisciplinary conference on religion. The conference acronym is CERC, which translates as Cultural Evolution of Religion Research Consortium. Most of the 60-some attendees are religion scholars and social scientists from North American and European universities. Many are also participants in a large partnership grant from the Social Sciences and Humanities Research Council of Canada (SSHRC), spearheaded by Ted Slingerland, an East Asian scholar at UBC. Some preliminary conversations with attendees indicated considerable apprehension about how researchers from the humanities and sciences would get on. Many of us are familiar with collaborative difficulties even in our own narrow domains. Skepticism was fairly common.

As far as I know, our poster was the only computer simulation presented at the meeting. We titled it Agent-based modeling of the evolution of “religion”, with scare quotes around religion because of the superficial and off-hand way we treated it. Because we know from experience that simulations can be a tough sell even at a scientific psychology conference, we were curious about whether and how this poster would fly in this broader meeting.
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Did group selection play a role in the evolution of plasmid endosymbiosis?

plasmidBacterial plasmids are nucleotide sequences floating in the cytoplasm of bacteria. These molecules replicate independently from the main chromosomal DNA and are not essential to the survival or replication of their host. Plasmids are thought to be part of the bacterial domain’s mobilome (for overview, see Siefert, 2009), a sort of genetic commonwealth which most, if not all, bacterial cells can pull from, incorporate and express. Plasmids can replicate inside a host and then move to another cell via horizontal genetic transfer (HGT), a term denoting various mechanism of incorporation of exogenous genetic material.
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