January 23, 2011

arXiv paper of the week (Symmetry invariance in adaptability)


I found this paper on arXiv recently, cross-listed between the cs.SY and q-bio.QM categories:



The idea is brilliant, even if a bit unclear. The authors are interested in adaptation, and how an organism adapts to environmental change. To do this (theoretically), they create a mathematical model of several known signaling pathways. These pathways are then "perturbed" using a series of input transformations, and evaluated using a series of outputs. This has been done by Uri Alon before to look at the phenomenon of "switching" in biological systems.

The main premise is that the fold-change of inputs, and not the change in their absolute value per se, provide a signal to the organism about its environment. The organism adapts to this response, either through accommodation (learning to incorporate the signal into its behavioral repertoire) or through a robustness mechanism (learning to respond to the signal without disturbance). Models of signaling pathways were used to illustrate how adaptation proceeds given different types of network organization (feedforward vs. feedback).

The most interesting contribution of this paper involves their interpretation of adaptation as being a form of disturbance rejection. Disturbance rejection is a form of control in which certain parts of a signal are rejected as noise. Thus, one of the hypotheses presented here is that adaptation is a form of noise reduction. This may or may not be true, considering that adaptation can be a very general process. Noise might be minimized as a result of adaptation, but noise may also play a role in allowing further adaptation. As you read the paper, keep in mind that the results are meant to represent bacteria, and not necessarily plants or animals.

January 21, 2011

Is my Research Proposal Bull**it, Part II

It's time to ask for research money once again, and so the topic of this post is: what makes for a compelling (and ultimately fundable) research proposal? I haven't done too many of these yet, so I'm not sure. Fortunately, PLoS Computational Biology has provided a "10 simple rules" list (one of many available in their archive):

PLoS Article

The reference list of that article has a link to another resource on the "art" of grantsmanship (is this an actual word?):

Art of Grantsmanship

In any case, this should be a good start for anyone who is interested.

January 10, 2011

Is my research proposal bulls**t?

I try not to be too partisan on this blog, but the short version is this: elements of the new Republican Congress have attempted to "crowdsource" policy, and it seems to have redefined the term "know-nothings". Recently, Republican lawmakers used a crowdsourcing tool (YouCut Review) to nominate those NSF projects that were the biggest waste of government money.

The verdict: apparently, there weren't enough jeers to go around. Hence, the NSF must not be doing a very good job. Two stand out as project I've seen (excellent) presentations on:

Sound Physics in Virtual Worlds

Movement and information (Neuromechanics) in electric fish

And the outcome of said projects? Quite a bit of knowledge (e.g. research papers), and even a few very useful engineering artifacts. Hardly a "waste" of money. In fact, it's a bargain when you consider other things we waste money on:

Celebrity gossip (from what I've seen, the Paparazzi do more harm than good)

Lotteries (if the money spent here went to a tax increase, you'd be more likely to see a payoff)

Smear campaigns (politics - the 2010 election cycle in the US was burdened with around $4bn in attack ads, mostly by 3rd parties. It got to the point of being pure noise, at which point the parties in question kept on spending)

The point is that these NSF-funded projects were picked because they "sounded" like a waste of time, not because they actually are (my second list is probably closer to the truth, but they probably serve some valid social function). Please join me in outing this cynical strategy.

"Reining" in Diabetes

Interesting article from IEEE Spectrum on the application of control theory to diabetes treatment. This is a good example of rein control (even through they don't name it as such in the article), which is a form of feedforward control broadly applicable to physiological systems.

The concept of rein control dates to pre-automotive when a driver would control their team of horses (or some other pack animal) in order to get wagons or other payloads from place to place. The driver would use two reins (or guidewires) that were independently coupled to the team of animals in front. Pulling on the reins in different ways would result in a very crude form of steering (controlling direction
or speed).

Rein control in physiology exists when two entities A and B (which could be gene products, paracrine signaling, or hormonal release) act upon the same target. This is fundamentally different from linear feedback, where the action of one entity is reinforced. In the article, the mode of action is different, which contributes to the following:

* a compensatory effect when both entities (A and B) act upon the same target.

* a loss of function when one of the source entities (A or B) fail (as in diabetes, where the beta cells shut down).

In cases where the number of entities is more than two, we will see much more complex dynamics. In particular, we should see many synthetic (A + B > C) and subadditive effects (A + B < C).

For more information about applications of control theory to disease and physiological function, please see the following academic references:

[1] Saunders et.al (1998). Integral Rein Control in Physiology. Journal of Theoretical Biology 194, 163.

[2] Cobelli et.al (2009). Diabetes: models, signals, and control. IEEE Reviews in Biomedical Engineering, 2, 54.

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