Toward a new ontology of brain dynamics: neural resonance + neuroacoustics

Part 3 of my series. I think this is an important idea.

Part 1: Neurobiology, psychology, and the missing link(s)
Part 2: Gene Expression as a comprehensive diagnostic platform
Part 3: Neural resonance + neuroacoustics
Part 4: Location, location, location!

The brain is extraordinarily complex. We are in desperate need of models that decode this complexity and allow us to speak about the brain's fundamental dynamics simply, comprehensively, and predictively. I believe I have one, and it revolves around resonance.

Neural resonance is currently an underdefined curiosity at the fringes of respectable neuroscience research. I believe that over the next 10 years it'll grow into being a central part of the vocabulary of functional neuroscience. I could be wrong- but here's the what and why.

Resonance, in a nutshell

To back up a bit and situate the concept of resonance, consider how we create music. Every single one of our non-electronic musical instruments operate via resonance-- e.g., by changing fingering on a trumpet or flute, or moving a trombone slide to a different position, we change which frequencies resonate within the instrument. And when we blow into the mouthpiece we produce a messy range of frequencies, but of those, our instrument's physical parameters amplify a very select set of frequencies and dampen the rest, and out comes a clear, musical tone. Singing works similarly: we change the physical shape of our voiceboxes, throats, and mouths in order to make certain frequencies resonate and others not.

Put simply, resonance involves the tendency of systems to emphasize certain frequencies or patterns at the expense of others, based on the system's structural properties (what we call "acoustics"). It creates a rich, mathematically elegant sort of order, from a jumbled, chaotic starting point. We model and quantify resonance and acoustics in terms of waves, frequencies, harmonics, constructive and destructive interference, and the properties of systems which support or dampen certain frequencies.

So what is neural resonance?

Literally, 'resonance which happens in the context of the brain and neurons', or the phenomenon where the brain's 'acoustics' prioritizes certain patterns, frequencies, and harmonics of neural firings over others.

Examples would include a catchy snippet of music or a striking image that gets stuck in a one's head, with the neural firing patterns that represent these snippets echoing or 'resonating' inside the brain in some fashion for hours on end.[1] Similarly, though ideas enter the brain differently, they often get stuck, or "resonate," as well-- see, for instance, Dawkins on memes. In short, neural resonance is the tendency for some patterns in the brain (ideas) to persist more strongly than others, due to the mathematical interactions between the patterns of neural firings into which perceptions and ideas are encoded, and the 'acoustic' properties of the brain itself.

But if we want to take the concept of neural resonance as more than a surface curiosity-- as I think we should-- we can make a deeper analogy to the dynamics of resonant and acoustic systems by modeling information as actually resonating in the brain. That there are deep, rich, functionally significant, and semi-literal parallels between many aspects of brain dynamics and audio theory. Just like sound resonates in and is shaped by a musical instrument, ideas enter, resonate in, are shaped by, and ultimately leave their mark on our brains.

I thought the brain was a computer, not a collection of resonant chambers?

Yes; I'm essentially arguing that the brain computes via resonance and essentially acoustical mechanics.

So what is this resonance theory, specifically?

I'm basically arguing that we should try to semi-literally adapt the equations we've developed for sound and music to the neural context, and that most neural phenomena can be explained pretty darn well in terms of these equations. In short:

The brain functions as a set of connected acoustic chambers. We can think of it as a multi-part building, with each room tuned to make a slightly different harmonies resonate, and with doors opening and closing all the time so these harmonies constantly mix. (Sometimes tones carry through the walls to adjacent rooms.) The harmonies are thoughts; the 'rooms' are brain regions.

Importantly, the transformations which brain regions apply to thoughts are akin to the transformations a specific room would apply to a certain harmony. The acoustics of the room-- i.e., the 'resonant properties' of a brain region-- profoundly influence the pattern occupying it. The essence of thinking, then, is letting these patterns enter our brain regions and resonate/refine themselves until they ring true.

My basic argument is that you can explain basically every important neural dynamic within the brain in terms of resonance, that it's a comprehensive, generative, and predictive model-- much moreso than current 'circuit' or 'voting' based analogies.

Here are some neural phenomena contextualized in terms of resonance:

- Sensory preprocessing filters: as information enters the brain, it's encoded into highly time-dependent waves of neural discharges. The 'neuroacoustic' properties of the brain, or which kinds of wave-patterns are naturally amplified (i.e., resonate) or dampened by properties of the neural networks relaying this pattern, act as a built-in, 'free' signal filter. For instance, much of the function of the visual and audio cortexes emerges from the sorts of patterns which they amplify or dampen.

- Competition for neural resources: much of the dynamics of the brain centers around thoughts and emotions competing for neural resources, and one of the central challenges of models purporting to describe neural function is to provide a well-defined fitness condition for this competition. Under the neural resonance / neuroacoustics model, this is very straightforward: patterns which resonate in the brain acquire more and better maintain resources (territory) than those that resonate less well.

- What happens when we're building an idea: certain types of deliberative or creative thinking may be analogous to tweaking a neural pattern's profile such that it resonates better.

- How ideas can literally collide: if two neural patterns converge inside a brain region, one of several overlapping things may occur: one resonates more dominantly and swamps the other, destructive interference, constructive interference, or a new idea emerges directly from the wave interference pattern.

- How ideas change us: since neural activity is highly conditioned, patterns which resonate more change more neural connections. I.e., the more a thought, emotion, or even snippet of music persists in resonating and causing neurons to fire in the same pattern, the more it leaves its mark on the brain. Presumably, having a certain type of resonance occur in the brain primes the brain's neuroacoustics to make patterns like it more likely to resonate in the future (see, for instance, sensitization aka kindling).[2] You become what resonates within you.

In short, resonance, or the tendency for certain neural firing patterns to persist due to how their frequency- and wave-related properties interact with the features of the brain and each other, is a significant factor in the dynamics of how the brain filters, processes, and combines signals. However, we should also keep in mind that:

Resonance in the brain is an inherently dynamic property because the brain actively manages its neuroacoustics!

I've argued above that our 'neuroacoustics'- that which determines what sorts of patterns resonate in our heads and get deeply ingrained in our neural nets- is important and actively shapes what goes on in our heads. But this is just half the story: we can't get from static neuroacoustic properties to a fully-functioning brain, since, if nothing else, resonant patterns would get stuck. The other, equally important half is that the brain has the ability to contextually amplify, dampen, filter, and in general manage its neural resonances, or in other words contextually shape its neuroacoustics.

Some of the logic of this management may be encoded into regional topologies and intrinsic properties of neuron activation, but I'd estimate that the majority (perhaps 80%) of neuroacoustic management occurs via the contextual release of specific neurotransmitters, and in fact this could be said to be their central task.

With regard to what manages the managers, presumably neurotransmitter release could be tightly coupled with the current resonance activity in various brain regions, but the story of serotonin, dopamine, and norepinephrine may be somewhat complicated as it's unclear how much of neurotransmitter activity is a stateless, walkforward process. The brain's metamanagement may be a phenomenon resistant to simple rules and generalities.

A key point regarding the brain managing its neuroacoustics is that how good the brain is at doing so likely varies significantly between individuals, and this variance may be at the core of many mental phenomena. For instance:

- That which distinguishes both gifted learners and high-IQ individuals from the general populance may be that their brains are more flexible in manipulating their neuroacoustic properties to resonate better to new concepts and abstract situations, respectively. Capacity for empathy may be shorthand for 'ability to accurately simulate or mirror other peoples' neuroacoustic properties'.

- Likewise, malfunctions and gaps in the brain’s ability to manage its neural resonance, particularly in matching up the proper neuroacoustic properties to a given situation, may be a large part of the story of mental illness and social dysfunction. Autism spectrum disorders for instance, may be almost entirely caused by malfunctions in the brain's ability to regulate its neuroacoustic properties.

One lever the brain could be using to manage its neuroacoustics is the ability to pick which regions a thought pattern is allowed to resonate in. A single region vs multiple, regions with properties of X rather than of Y, etc. Another lever is changing the neuroacoustic properties within a region. Yet another lever is changing the effective "acoustic filter" properties inherent in connections between brain regions-- thoughts will necessarily be filtered and streamlined as they leave one region and enter another, but perhaps the way they are filtered can be changed. It's unclear how the brain might use each of these neuroacoustic management techniques depending on the situation, but I would be surprised if the brain didn't utilize all three.

Further implications:

- If we can exercise and improve the brain's ability to manage its neural resonance (perhaps with neurofeedback?), all of these things (IQ, ability to learn, mental health, social dexterity) should improve.

- Mood may be another word for neuroacoustic configuration. A change in mood implies a change in which ideas resonate in ones mind. Maintaining a thought or emotion means maintaining one's neuroacoustic configuration. (See addendum on chord structures and Depression.)

- 'Prefrontal biasing', or activity in the prefrontal cortex altering competitive dynamics in the brain, may be viewed in terms of resonance: put simply, the analogy is that the PFC is located at a leveraged acoustic position (e.g., the tuning pegs of a guitar) and has a strong influence on the resonant properties of many other regions.

- Phenomena such as migraines may essentially be malfunctions in the brain's neuroacoustic management. A runaway resonance.

- I'm hopeful that we should be able to derive a priori things such as the 'Big Five' personality dimensions from simple differences in the brain's stochastic neuroacoustic properties and neuroacoustic management.

The story thus far:

So, that's an outline of a resonance / neuroacoustics model of the brain. In short, many brain phenomena are inherently based on resonance, and differences in many of the mental attributes we care about-- intelligence, empathy, mood, and so on-- are a result of the brain's ability (or lack thereof) to appropriately regulate its own neuroacoustic configuration.


Now, the natural question with a theory such as this is, 'is this a just-so story?' The evidence that would support or falsify this model is still out, and our methods of analyzing brain function in terms of frequency and firing patterns are still very rudimentary, but the model does seem to explain/predict the following:

- What cognition 'is';
- How competition for neural resources is resolved;
- How complex decision-making abilities may arise from simple neural properties;
- How ideas may interact with each other within the brain;
- That audio theory may be a rich source of starting points for equations to model information dynamics in the brain;
- What the maintenance of thought and emotion entails, and why a change in mood implies a change in thinking style;
- How subconscious thought may(?) be processed;
- What intelligence is, and how there could be different kinds of intelligence;
- How various disorders may naturally arise from a central process of the brain (and that they are linked, and perhaps can be improved by a special kind of brain exercise);
- The division of function between neurons and neurotransmitters;
- The mechanism by which memes can be 'catchy' and how being exposed to memes can create a 'resonant beachhead' for similar memes;
- The mechanism of how neurofeedback can/should be broadly effective.

There are few holistic theories of brain function which cover half this ground.

Tests which have the ability to falsify or support models of neural function (such as this one) aren't available now, but may arise as we get better at simulating brains and such. I look forward to that-- it would certainly be helpful to be able to more precisely quantify things such as neural resonance, neuroacoustics, interference patterns within the brain, and such.

Closing thoughts:

As George Box famously said, 'all models are wrong, but some are useful.' This model certainly doesn't get everything right, and to some extent (just like its competitors) it is a just-so story-- but I think it's got at least three things going for it over similar models:
1. Fundamental simplicity-- it's one of the few models of neural function which can actually provide an intuitive answer to the question of what’s going on in someone brain.
2. Emergent complexity-- from a small handful of concepts (or just one, depending on how you count it), the elegant complexity of neural dynamics emerges.
3. Ideal level of abstraction-- this is a model which we can work both downward from to e.g., use as a sanity check for neural simulation since the resonant properties of neural networks are tied to function (the Blue Brain project is doing this to some extent), and upward from to generate new explanations/predictions within psychology, since resonance appears to be a central and variable element of high-speed neural dynamics and the formation and maintenance of thought and emotion.

If it's a good, meaningful model, we should be able to generate novel hypotheses to test. I have outlined some in my description above (e.g., that many, diverse mental phenomena are based on the brain's ability to manage its neural resonance, and if we approve this in one regard it should have significant spillover). There will be more. I wish I had the resources to generate and test specific hypotheses arising from this model.

ETA 10 years.

Footnotes and musings:

[1] As a rule, music resonates very easily in brains. Moreover though, there's a great deal of variation in which types of music resonate in different peoples' brains. I have a vague but insistent suspicion that analyzing who finds which kinds of music 'catchy' can be extrapolated to understand at least some of the contours of what general types of things peoples' brains resonate to. I.e., music seems to lend itself toward 'typing' neural resonance contours.

[2] The brain's emotive and cognitive machinery are so tightly linked-- there's support from the literature to say no real distinction exists-- that a huge question mark is how the resonance of thoughts and emotions coexist and interact. It's safe to say that the brain's resources are finite such that, all being equal, the presence of strong emotions reduces capacity for abstract cognition and general processing. But does the relationship go beyond this? Can we also say that having certain emotions resonate 'sensitizes' or optimizes the brain for certain types of cognition? Music is perhaps the most powerful and consistent harbinger of emotion; does listening to music 'sensitize' or 'prime' the brain's resonant properties in the same way as raw emotions might? Are we performing a mass neurodynamics experiment on society with e.g., all the rap or emo pop music out there? How could we even attempt to characterize these hypothetical stochastic changes in average neural resonance profiles?

- Resonance is about the reinforcement of frequencies. So what specific frequencies might we be dealing with here?

It's hard to say for sure, since we have no robust (or even fragile) way of tracking information as it enters and makes its way through the brain. With no way to track or identify information, we can't give a confident answer to this (and so many other questions).

But a priori, as a first approximation, I would suggest:
(1) The frequencies of previously-identified 'brainwaves' (alpha, delta, gamma, etc) may be relevant to information encoding mechanics (or, alternatively, to neural competition dynamics);
(2) If we model a neural region as a fairly self-contained resonant chamber (with limited but functionally significant leakage), the time it takes a neural signal, following an 'average' neural path, to get to the opposite edge of the chamber and return will be a key property of the system. (Sound travels in fairly straight lines; neural "waves" do not. This sort of analysis will be non-trivial, and will perhaps need to be divorced from a strict spacial interpretation. And we may need to account for chemical communication.) Each brain region has a slightly different profile in this regard, and this may help shape what sorts of information come to live in each brain region.

Addendum, 10-11-10: Chord Structures

Major chords are emotively associated with contentment; minor chords with tragedy. If my resonance analogy is correct, there may be a tight, deeply structural analogy between musical theory, emotion, and neural resonance. I.e., musical chords are mathematically homologous to patterns of neural resonance, wherein major and minor forms exist and are almost always associated with positive and negative affect, respectively.

Now, it's not clear whether there's an elegant, semi-literal correspondence between e.g., minor chords, "minor key" neural resonances, and negative affect. There could be three scenarios:

1. No meaningful correspondence exists.
2. There isn't an elegant mathematical parallel between e.g., the structure of minor chords and patterns of activity which produce negative affect in the brain, but within the brain we can still categorize patterns as producing positive or negative affect based on their 'chord' structure.
3. Musical chords are deeply structurally analogous to patterns of neural resonance, in that e.g., a minor chord has a certain necessary internal mathematical structure that is replicated in all neural patterns that have a negative affect.

The answer is not yet clear. But I think that the incredible sensitivity we have to minute changes in musical structure- and the ability of music to so profoundly influence our mood- is evidence of (3), that musical chords and the structure of patterns of neural impulses are deeply analogous, and knowledge from one domain may elegantly apply to the other. We're at a loss as to how and why humans invented music; it's much less puzzling if it's a relatively elegant (though simplified) expression of what's actually going on in our heads. Music may be an admittedly primitive but exceedingly well-developed expression of neuro-ontology, hiding in front of our noses.

How do we prove this?

Correlating thought structure with affect is a Hard problem, mostly because isolating a single 'thought' within the multidimensional cacophony of the brain is very difficult. There has been some limited progress with inputting a 'trackable signal' of very specific parameters (e.g., a 22hz pulsed light, or a 720hz audio wave) and tracing this through sensory circuits until it vanishes from view. There's a lot of work going on to make this an easier problem. Ultimately we'd be drawing upon the mathematical structure of musical chords and looking for abstract, structural similarities with patterns of neural firings, and attempting to correlate positive and negative affect with these patterns.

The bottom line:

If this chord structure hypothesis is even partly true, it (along with parts of music theory) could form the basis for a holy grail of neuroscience, a systems model of emotional affect. E.g., Depression could be partly but usefully characterized in terms of a brain's resonance literally being tuned to a minor key.


Prediction: Gene Expression as a comprehensive diagnostic platform

This is part 2 of my series on the brain, neuroscience, and medicine.

Part 1: Neurobiology, psychology, and the missing link(s)
Part 2: Gene Expression as a comprehensive diagnostic platform
Part 3: Neural resonance + neuroacoustics
Part 4: Location, location, location!

I have seen the future of medical diagnosis-- it's elegant, accurate, immediate, mostly doctor-less, comprehensive, and very computationally intensive. I don't know when it'll arrive, but it's racing toward us and when it hits, it'll change everything.

In short-- the future of medical diagnosis is to use a gene expression panel along with known functional and correlative connections between gene expression and pathology to perform thousands of parallel tests for every single human illness we know of-- no matter whether it's acute, chronic, pathogenic, mental, or lifestyle.

What do you mean? And how would it work?

The basis for using gene expression as a comprehensive diagnostic platform goes something like this:

- Gene expression is a measure of which (and to what extent) genes are being made into proteins and RNA. A gene expression test is much like a traditional genetic test, but since it goes beyond merely listing which genes your body has, and shows how much your body is using each one, it's a much better view of what's actually going on inside your body. Genes may be a blueprint of physiological potential-- but gene expression is a snapshot of physiological function.

- The Vast majority of illnesses leave a significant imprint on a person's gene expression. A failing kidney, an inflamed appendix, obesity, a manic episode-- these will influence which genes are activated, and in very specific ways. It's possible, and I think fairly probable, that the imprints distinct physiological insults leave on gene expression will themselves be fairly distinct, and so in theory we should be able to work backward from gene expression to physiological insult.

- Once we've gathered a Large collection of gene expression-known illness pairs (we could build this dataset by requiring e.g., hospitals to collect a gene expression sample when a diagnosis is made), we can start to train computers to identify what gene expression conditions are connected to each illness. Finding these sorts of connections is almost impossible for humans, but there exist computational approaches which in theory are fairly ideal.[1]

So in short, it won't be easy, but I think there's really nothing standing in the way of gene expression tests which use broad-spectrum correlative analysis to screen for all known illnesses at once.

I suspect the possibility of gene expression as a comprehensive diagnostic platform will start to become a "cool" thing for bioscience visionaries to yak about over the next 5 years. The large-scale data collection is, I think, the biggest hurdle, though finding solid correlations in a massive dataset against a background of variable application of diagnostic criteria is also non-trivial. But it's coming.

- Training computer programs (e.g., classification ANNs) requires a lot of good data, as does screening out false positives in a sample as wide as a full genome. Getting enough *good* samples where all the right diagnoses have been made will be challenging.
- Gene expression analysis is still having growing pains. E.g., "Protein sequencing gone awry: 1 sample, 27 labs, 20 results".
- Crunching the numbers on which of 22,000+ different genes (and potentially some non-protein-coding, RNA-producing genes) are correlated with each illness is far from a trivial problem.

- Will gene expression from multiple locations be needed to diagnose some illnesses?
- It seems fairly safe to say we'll be able to diagnose e.g., kidney failure or malaria from gene expression data. But what about internal bleeding? And what about some of the more tricky or subjective mental illnesses? This technology will have its theoretical limits: what are they?

[1] This task falls outside the scope of this writeup, but just going with what I know, I'd take a set of gene expression-known illnesses pairs, divide the gene data up into smaller, more tractable pieces (perhaps along specific gene-network faultlines, perhaps randomly?), and train a classifier neural network on the pieces, which will attempt to predict a specific illness based on what it finds to be the most significant data in the subset. Layer these subset-based classifier neural networks under a 'master' classifier neural network which gives the final yes/no prediction. Test this model on progressively larger out-of-sample data sets. Repeat for each illness. There are undoubtedly solutions orders of magnitude better than this-- but it's a baseline start.

ETA 15-20 years.

Edit, 1-22-10: Based on the available information, I think gene expression is a strongly representative abstraction level from which to draw. However, I see strong arguments for also including the metabolome and metagenome if it's feasible to do so.

Edit, 6-22-10: My ETA may even be too conservative: a collection of researchers from various California universities recently published a method for using gene expression for diagnosis by associating arbitrary gene expression profiles with clustered sets of expression profiles with known diagnoses.

Edit, 6-12-11: This idea depends on the mid-term availability of incredibly cheap gene expression sequencing. I don't think this is unrealistic, given these sorts of trends (courtesy of genome.gov).

Edit, 6-29-11: Gene expression includes an incredible amount of context and nuance, which provides it with a significant advantage over the current (very imperfect) practice of using simple biomarkers.


Neuro musings, part 1: neurobiology, psychology, and the missing link(s)

I'm flying out to Salt Lake City tomorrow for a month of thinking about neuroscience; I process ideas by writing, so I'm kicking off an open-ended series of pieces dealing with the stuff I'm thinking about.

Part 1: Neurobiology, psychology, and the missing link(s)
Part 2: Gene Expression as a comprehensive diagnostic platform
Part 3: Neural resonance + neuroacoustics
Part 4: Location, location, location!

The central problem of neuroscience is that despite all the advancements happening in medical science, we have embarrassingly few ways to quantify, or talk quantitatively about, mid-level functional differences between peoples' brains.

It's not that we have no tools at all for quantifying function and individual differences: we can draw correlations between specific genes and certain behavioral traits or neurophysiological features. We have the DSM IV (and soon, DSM V) as a sort of handbook on the symptoms of common brain-related problems. We have the Myers-Briggs and related personality-typing tests, we have psychometric tests, we have various scans that pick up gross neuroanatomy (and we can sometimes correlate this with behavioral deficits), and we have the fMRI, which can measure raw neural activity through the proxy of where blood flows in the brain.

The problem is that these methods of understanding brains are heavily clustered in two opposite areas: the reductionist neuroanatomical approach, which is great as far as it goes, but doesn't go far enough up the ladder of abstraction to explain much about everyday behavior, and the symptom-centric psychological approach, which may be a great description of how various people behave, or some common neural equilibria, but really explains very little.[1][2] There's a great deal of room in neuroscience for an ontology with which to talk about, and mid-level tools which attempt to measure and correlate things with, this underserved middle-level of brain function.[3]

Of course, the natural question regarding these mid-level approaches to understanding the brain is whether we can find ontologies and tools which can be said to "carve reality at its joints," or not be based on a terribly leaky level of abstraction (as, for example, the DSM IV fails at), yet have direct relevance to psychological events as we experience them in ourselves and in others (as, for example, the DSM IV does). I don't have any answers! But I do have ideas.

[1] To paraphrase Sir Karl Popper, implicit in any true explanation of a phenomenon is a prediction, and implicit in any prediction about a phenomenon is an explanation. So a good way to figure out how much of a field is true scientific explanation vs. 'mere stamp-collecting' is to check how much it deals with predictions, whether explicit or implicit. Psychology seems to be a primarily descriptive field that's attempting to translate its rich (yet predictively shallow) descriptive ontology into a more prediction-based science.

[2] This point on the fuzziness of psychiatry was made rather eloquently in an Op-Ed by Simon Baron-Cohen (the famous autism researcher, and the first cousin of British comedian Sasha Baron-Cohen) in this week's New York Times:
This history reminds us that psychiatric diagnoses are not set in stone. They are “manmade,” and different generations of doctors sit around the committee table and change how we think about “mental disorders.”

This in turn reminds us to set aside any assumption that the diagnostic manual is a taxonomic system. Maybe one day it will achieve this scientific value, but a classification system that can be changed so freely and so frequently can’t be close to following Plato’s recommendation of “carving nature at its joints.”

Part of the reason the diagnostic manual can move the boundaries and add or remove “mental disorders” so easily is that it focuses on surface appearances or behavior (symptoms) and is silent about causes. Symptoms can be arranged into groups in many ways, and there is no single right way to cluster them. Psychiatry is not at the stage of other branches of medicine, where a diagnostic category depends on a known biological mechanism. An example of where this does occur is Down syndrome, where surface appearances are irrelevant. Instead the cause — an extra copy of Chromosome 21 — is the sole determinant to obtain a diagnosis. Psychiatry, in contrast, does not yet have any diagnostic blood tests with which to reveal a biological mechanism.

[3] I realize this is somewhat vague. I plan to expand this description of what I think of as "mid-level functional attributes" and the sorts of concepts and tools I think may be useful for dealing with them. One example of a mid-level measurement that struck me as promising was a work correlating lack of microstructural integrity in the uncinate fasciculus with psychopathy.

Edit, 6-29-10: Recent work from UMN and Yale has correlated brain region size differentials to Big 5 personality traits with some success.



The new site redesign is now live! Thanks to some beautiful artwork by my friend Corby, and some ugly html hacks by me, Modern Dragons now features a dragon. Speaking of which, it's high time to answer the question:

What's a Modern Dragon anyway?

Back in the Middle Ages, cartographers used to (anecdotally, at least) mark unknown or dangerous territories on their maps with the Latin phrase, HIC SVNT DRACONES-- literally, "Here be Dragons". By metaphor, then, the purpose of this blog is to locate, explore, and perhaps take a swing at the analogous dragons in our modern age-- the puzzles, frontiers, and dangerous elements within science, culture, and this terribly uncertain future of ours.


Quote: on the evolution of reading

Here, I am reminded not of the recent past but of a huge change that occurred in the middle-ages when humans transformed their cognitive lives by learning to read silently. Originally, people could only read books by reading each page out loud. Monks would whisper, of course, but the dedicated reading by so many in an enclosed space must have been an highly distracting affair. It was St Aquinas who amazed his fellow believers by demonstrating that without pronouncing words he could retain the information he found on the page. At the time, his skill was seen as a miracle, but gradually human readers learned to read by keeping things inside and not saying the words they were reading out loud. From this simple adjustment, seemingly miraculous at the time, a great transformation of the human mind took place, and so began the age of intense private study so familiar to us now; whose universities where ideas could turn silently in large minds.
Dr. Barry Smith, University of London, while discussing Edge Magazine's 2009 question, What will change everything?

Edit: a commenter has suggested it was actually St. Ambrose, not St. Aquinas, who first broke this ground.


A simple and cheap proposal for improving American health

Earlier this summer a pediatrician friend of mine was asking about ideas for health care reform since Olympia Snowe was going to stop by her hospital and talk with the doctors there. Unfortunately Snowe cut her visit short, but this is what I came up with:

A simple and cheap proposal for improving American health:

In short, I'd like to see a federally-funded, state-by-state performance-based incentive program to improve public health. Specifically, the federal government sets aside a decent chunk of money and sets targets for curbing health problems: e.g., "Reduce the growth of childhood diabetes in your state by 50% by 2012" or "Reduce the growth of cardiovascular disease in your state by 40% by 2013." If state A meets the target, they get generous federal funds for doing so. If state B fails to meet the target, they don't. Ideally, this would generate a lot of creativity in actually solving the targeted problems (since real money for the state would be on the line), but states would also have incentive to copy what works.

This program might cost some money-- but we'd be paying for results: if it flopped and nobody hit these targets, well, it'd have cost nothing. On the other hand, if this program got results, even if we consider the money going to states to be 'wasted' the program would still be a net financial gain from perspective of decreased strain on our health systems. In other words, with a results-based incentive system, we have nothing to lose if it flops and plenty to gain if it works.

Now, I'm sure the devil would be in the details. We'd need to pick targets that are easy to representatively measure and hard to game. It also seems like we could have a yearly governors' conference revolving around this incentive program for states to share tips on what strategies are working and which aren't. Make this conference (and the incentive program in general) a big deal, and make it competitive-- make states proud of their successes and ashamed of their failures.

In general, it seems to me that this sort of grand state-by-state competition for funds could be extended to a lot of social problems. Since it incentivizes results instead of naive/bureaucratic thinking, it might encourage some smart, actionable analysis about the roots of various social problems. But that's something to explore another time. My point is, I think this would work really well for improving public health, and we should do it.

p.s. Anyone have a good way of getting this idea into the hands of some congressperson?


Quote: China on China

Via a NY Times article on the US-China financial relationship:
Deng Xiaoping, the Chinese leader who ushered in its market reforms starting in the late 1970s, famously gave his country the following advice: “Observe calmly; secure our position; cope with affairs calmly; hide our capacities and bide our time; be good at maintaining a low profile; and never claim leadership.”
This seems to be the general trend in Chinese foreign policy; if the Chinese leadership decide this is no longer necessary or desirable, we could suddenly live in a very different world.

I think a particularly interesting and volatile element to this is that the Chinese Government has a relatively solid hold on power, but this hold is largely tied to the year-over-year economic growth China has been experiencing for decades. The Chinese are content to tolerate their government because life is getting better, and looks to get better still. Should this growth dry up, there's no telling what may happen domestically, or what nationalistic conflicts the Chinese Government may enter into as a ploy to unify their people.


On writing, and the beauty of archive.org

If I had to put together a list of the 7 Wonders of the Internet, archive.org would most certainly be on it. It's the website of a non-profit organization which runs a huge server farm that tirelessly crawls the internet and saves what it finds. On the website, you can use their Wayback Machine to essentially turn back the clock and experience the internet frozen at a particular instant. The NY Times' website as of December, 1998? Check. Yahoo.com as of January, 2001? Check. That Geocities blog you started as an angsty teenager and later deleted in shame? Yes, probably that too.

My latest archive.org-assisted rediscovery is of a wonderful little essay on the difficulty of writing vs programming by Paul Graham. Archive.org isn't google-searchable, and so when Graham deleted his infogami blog this gem vanished down the memory hole. I'll quote it in full for your pleasure and to get it back in circulation.

Paul Graham

Why Writing is Harder than Programming

3 Oct 06

I spent most of this summer hacking a new version of Arc. That's why I haven't written any new essays lately. But I had to start writing again a few days ago because I have to give a talk at MIT tomorrow.

Switching back to writing has confirmed something I've always suspected: writing is harder than hacking. They're both hard to do well, but writing has an additonal element of panic that isn't there in hacking.

With hacking, you never have to worry how something is going to come out. Software doesn't "come out." If there's something you don't like, you change it. So programming has the same relaxing quality as building stuff out of Lego. You know you're going to win in the end. Succeeding is simply a matter of defining what winning is, and possibly spending a lot of time getting there. Those can be hard, but not frightening.

Whereas writing is like painting. You don't have the same total control over the medium. In fact, you probably wouldn't want it. When it's going well, painting from life is something you do in hardware. There are stretches where perception flows in through your eye and out through your hand, with no conscious intervention. If you tried to think consciously about each motion your hand was making, you'd just produce something stilted.

The result is that writing and painting have an ingredient that's missing in hacking and Lego: suspense. An essay can come out badly. Or at least, you worry it can.

I think good writers can push writing in the direction of Lego. As you get more willing to discard and rewrite stuff, you approach that feeling of total control you get with Lego and hacking. If there's something you don't like, you change it. At least, as I've gotten better at writing that's what's happened to me. I've become much more willing to throw stuff away.

But though you get closer to the calmness of hacking, you never get there. What a difference it is walking into the Square to get a cup of tea with a half-finished essay in my head, rather than a half-finished program. A half-finished program is a pleasing diversion-- a puzzle. A half-finished essay is mostly just a worry. What if you don't think of the other half?

It's possible that hacking is only easy because we have poor tools (and low expectations to match). Maybe if you had really powerful tools you'd tell a computer what to do in a way that was more like writing or painting. Lego, pleasing as it is, can't do what oil paint can. That would be an alarming variant of hundred year language: one that was as powerful and as frightening as prose. But that's exactly the sort of trick the future tends to play on you.


Quote: on the economic situation

The biggest problem today [in our economic situation] is that nobody really knows what the value of anything is.
- Kermit Johnson

(Why yes, Dad, I *do* listen!)


Our broken grant system

The New York Times has a piece up highlighting some of the fundamental flaws in the cancer research grant system. In short, they find that it tends to fund unambitious, incremental research proposals that are unlikely to fail, yet also unlikely to result in significant progress toward curing cancer. I thought this passage was particularly poignant:
“Scientists don’t like talking about it publicly,” because they worry that their remarks will be viewed as lashing out at the health institutes, which supports them, said Dr. Richard D. Klausner, a former director of the National Cancer Institute.

But, Dr. Klausner added: “There is no conversation that I have ever had about the grant system that doesn’t have an incredible sense of consensus that it is not working. That is a terrible wasted opportunity for the scientists, patients, the nation and the world.”
John Hawks has some clever and good commentary on the situation, bringing in some evolutionary theory about search space and fitness peaks to support the point that yes, we're funding the wrong sorts of grant proposals when we go for timid, incremental projects given our current state of knowledge.

A pie-in-the-sky idea

As sort of an ideal-world scenario, instead of routing all proposals through the most established and senior of scientists, I'd like to see a modest amount of future NIH funding be set aside and overseen by graduate students in seminars across the country. Essentially, students could sign up for a seminar where their coursework would be to analyze a set of grant applications pertaining to their field, learn about the science in each grant and about the grant system, and finally select the top 1-2 grants to be funded. The professor teaching the class would be in charge of the syllabus, but with the following three guidelines:

1. Attempt to choose the best grant proposals;
2. The students, not the professor, have the final say in which proposals get funded;
3. Use the class as a teaching tool for both the science involved in the grants, and the grant system itself.

The set of grant applications to evaluate could be drawn from the pool of applications the NIH has rejected, but still deems interesting and not based on bad science.

There would be a million details to fill in, but I guarantee this system would be consistently fresh and open to new ideas (I don't know if anyone has noticed this, but grad students are really smart and creative!), yet would still be grounded in science and experience. It'd also be a fantastic teaching tool.


Now leaving Era of the Mystery. All aboard for Era of the Tool.

Historically, there have been three ways to make progress within a scientific paradigm:
- Solve an outstanding mystery;
- Gather and publishing new data;
- Construct a new tool.

Gathering and publishing new data has constituted, and will constitute for the forseeable future, the majority of scientific publication. Science has a healthy and voracious appetite for data, and this isn't likely to change anytime soon. The interesting thing about progress in science today though, and the topic of this post, is the balance between the first and third sort of approach, mystery vs tool.

Era of the Scientific Mystery

By and large, the emphasis in science used to be on solving mysteries. Discovering the mechanism of genetic inheritance; decoding the structure of DNA; deciphering how viruses take over cells. Scientists were billed as detectives, and the height of scientific achievement was to find an "aha" insight that solved an outstanding mystery. But- though some scientists may voraciously deny this- we've been so successful at solving the fundamental mysteries out there that we're running out of this kind of mystery in many branches of science. In turn, science is gradually becoming less about solving foundational unknowns (like decoding the structure of DNA) and more about creating tools by which to more richly and more quantifiedly understand what is no longer mysterious but too complex to trust to our intuitions and simple equations.

Era of the Scientific Tool

Scientific progress has always had a strong tool component. Grind a better lens, see the stars better, and create a more accurate description of the galaxy; build a free-swinging pendulum, observe the shifting plane of motion, and conclude the Earth is not fixed but rotates. These sort of things were not uncommon in the history of science. But there seems to be a sea change happening that modern scientific publication is beginning to center around devising and applying tools that in turn generate interesting results.

Two examples of this from my own experience are the recent publications of a couple friends who are scientists, John Hawks (UW Madison) and Bryan W. Jones (U Utah).

Hawks made waves with a recent publication, Recent Acceleration of Human Adaptive Evolution, which applied an established genetics tool (linkage disequilibrium) to the context of the human genome and came to the conclusion that not only did human evolution not stop with the advent of civilization, but that it actually sped up over a hundredfold.

Jones just published A Computational Framework for Ultrastructural Mapping of Neural Circuitry, a work which defined a new integrative workflow which enabled, for the first time, the mapping of a large-scale neural connectome, and offered the first product of this workflow, a connectome map of a rabbit's retina.

Tools are absolutely central to both publications: the first is based on the novel application of an existing tool to a context it hadn't been applied in, and the second involved inventing a new tool to enable the generation of new datasets.

These examples are anecdotal, to be sure-- but it seems that although the meme of the scientific mystery will be with us for a long time, and though there are sporadic fundamental unknowns yet to discover, increasingly the really sexy, generative results in science involve creating or repurposing a tool to shed new light on some data, or generate data at an exponentially faster rate.

In short? Science is no longer about mysteries but about problems. And given the right tool, problems solve themselves.


- Kevin Kelly's Speculations on the Future of Science is an interesting survey of possible tools science may grow into.

Edit, 5-13-11: Bryan W. Jones has a nice description of the problem his lab faced in building a connectome, and the tools they built to solve it. The research method and goal were less about solving a well-defined mystery, but more about building tools, datasets, and models that allow more useful ways of understanding what happens in retinal tissue under various scenarios.


Brainstorm: Logarithmic Evolution Distance

(This piece is sort of a continuation of a previous brainstorm on evolution and phylogeny- it was lots of fun to think through and write, and I hope it's fun to read even if a bit jargon-heavy.)

Exponential advances in gene sequencing technology have produced an embarrassment of riches: we're now able to almost trivially sequence an organism's DNA, yet sifting meaning from these genomes is still an incredibly intensive and haphazard task. For instance, consider the following simple questions:

How close are the genetics of dogs and humans? How does this compare to cats and humans? What about mice and cats? How different, genetically, are mice and corn?

We have all of these genomes sequenced, but we don't have particularly good and intuitive ways to answer these sorts of questions.

Whenever we can ask simple questions about empirical phenomena that don't seem to have elegant answers, it's often a sign there's a niche for a new conceptual tool. This is a stab at a tool that I believe could deal with these questions more cogently and intelligently than current approaches.

Logarithmic Evolution Distance: an intuitive approach to quantifying difference between genomes.

How do we currently compare two genomes and put a figure on how close they are? The current fashionable metrics seem to be:

- Raw % similarity in genetic code-- e.g., "Humans and dogs share 85% of their genetic sequence." Or 70%. Or 98%, depending on whom you ask. However, what does this really say? There are many ways to calculate the figure for this, depending on how one evaluates CNVs and functional parity in sequences. And this tends to grossly understate the importance of regulatory elements.

- Gene homologue analysis-- e.g., "The dog genome has gene homologues for ~99.8% of the human genome." However, this metric also involves subjectivity-- depending on how you count them, apes might have the same number of human gene homologues as dogs. This approach also involves deep ambiguities in assuming homologue function, in assessing what constitutes a similar-enough homologue, and in dealing with CNVs-- and this 'roll up your sleeves and compare the functional nuts and bolts of two genomes' approach is also extremely labor-intensive.

- Time since evolutionary divergence-- e.g., "The latest common ancestor of dogs and cats lived 60 MYA, vs that of dogs and humans, which lived 95 MYA." However, though time seems a relatively good proxy for estimating how far apart two genomes are, there are many examples of false positives and false negatives for this heuristic. Selection strength and rate of genetic change can vary widely in different circumstances, and thus there are reasons to believe this heuristic is often deeply and systemically biased as a proxy for genome difference, and it breaks down very quickly for organisms with significant horizontal or inter-species gene transfer.

None of these approaches really give wrong answers to the questions I posed, but neither do they always, or often, give helpful and intuitive answers. They fail the ok, but what does it mean? test.

I think it's important to note, first, that all of life is connected. And as evolution creates gaps, it could also bridge them. I say we use these facts to build an intuitive, computational metric for quantifying how far apart two genomes are.

So here's my suggestion for a new approach
'Evolution Distance' - a rough computational/simulation estimate (useful in a relative sense) of the average number of generations of artificial selection it would take to evolve organism X into organism Y under standardized conditions, given a set of allowed types of mutations.

To back up a bit, a (rough) way to explain what this idea is about is, let's imagine we have some cats. We can breed our cats, and every generation we can take the most genetically doglike cats, and breed them together. Eventually (although it'll take a while!) you'll get a dog. What this tool would do, essentially, is computationally estimate how many generations worth of mutations it would take to go from genome A (a cat) to genome B (a dog). The number of generations is this 'evolution distance' between the genomes. You can apply it to any two genomes under the sun and get an intuitive, fairly consistent answer.

Details, details...
Now, what makes a dog a dog? We can identify several different thresholds for success-- an exact DNA match would be the gold standard, followed by a match of the DNA that codes for proteins, followed by estimated reproductive compatibility, followed by specific subsystem similarities, and so forth. The answer would be in terms of X to Y generations, 95% Confidence Interval, in log notation like the Ricter Scale, as it could vary so widely between organisms... let's call it LED for Logarithmic Evolution Distance. Arbitrarily, an LED of 1 might be 1k generations, an LED of 2 would be 10k generations, etc.

E.g., the LED of a babboon and a chimpanzee might be 1.8-1.9 (meaning it would take ~8000 generations of selective breeding to turn a babboon into a chimpanzee);
A giraffe and a hippo might be 3.4-3.6;
A starfish and a particular strain of e. coli might be 10.2-10.4. (That's a lot!)
I'm just throwing out some numbers, and may not be in the right ballpark... but the point is this is an intuitive, quantitative metric that can scale from comparing the genetics of parent and offspring all the way to comparing opposite branches of the tree of life.

This is intrinsically a quick and dirty estimate, very difficult to get (& prove) 'correct', but given that, it is
1. potentially very useful as a relative, quantitative metric,
2. intuitive in a way current measures of genetic similarity aren't,
3. fully computational with a relatively straightforward interpretation-- you'd set up a model, put in two genomes, and get an answer.

This estimate could, and would need to, operate with a significantly simplified model of selection. Later, the approach could slowly add in gene pools, simulation & function-aware aspects, mutation mechanics, the geometry of mutation hotspots, mutations incompatible with life, gene patterns that protect against mutations, HGT, etc. But it would start as, and be most helpful as, a very rough metric.

Selection-centric or mutation-centric?
Thus far I've used 'selection' and 'mutation' somewhat interchangeably. But I think the ideal way to set up the model is to stay away from random mutations and pruning. Instead, I would suggest setting up an algorithm to map out a shortest mutational path from genome A to genome B, given a certain amount of allowed mutation per generation. This would be less indicative of the randomness of evolution, but perhaps a tighter, more tractable, and more realistic estimate of the number of generations' worth of distance.

Practical applications (why would this be useful?):
In general, I see this as an intuitive metric to compare any two genomes that could see wide use-- after the general model is built, the beauty of this approach is that it's automated and quantitative. Just input any two arbitrary genomes, input some mutational parameters, and you get an answer. Biology is coming into an embarrassment of riches in terms of sequencing genomes. This is a tool that can hopefully help people, both scientists and laymen, make better intuitive sense of all this data.

E.g., If I wanted to compare the genomes of two prairie grasses with each other, and with corn, this tool would give me a reasonably intuitive answer to how closely each was related to the others.

A specific scientific use for this would be to compare the ratio of calculated LED to the time since evolutionary divergence while controlling for time between generations. This would presumably be a reasonable (and easy-to-do) measure to detect and compare strength of selection, perhaps helpful as a supplement to e.g., metrics such as linkage disequilibrium analysis. E.g., if the genome of two organisms' last common ancestor can be inferred, the LED of LCA's genome->genome A vs the LED of LCA's genome->genome B would presumably be an excellent quantitative indicator of relative strength of selection.

This metric is by no means limited to comparisons between species; comparing Great Danes to Pitbulls with this tool, or even two Pitbulls to each other, would generate interesting results.

This tool would also be helpful in an educational context, to drive home the point that everything living really is connected to everything else, and evolution is the web that connects them. It's also educational in the sense that it'd actually simulate a simplified form of genetic evolution, and we may learn a great deal from rolling up our sleeves and seeing how well our answers compare to nature's.

The nitty gritty...

Open questions:
- This comparison as explained does not deal with the complexity of sexual recombination or of horizontal gene transfer (though to be fair, none of its competitors do either). Or, to dig a little deeper, evolution happens on gene pools, whereas this tool only treats evolution as mutation on single genomes. Does it still produce a usably unbiased result in most comparisons? (My intuition is if we're going for an absolute estimate of an 'evolution distance', no; a relative comparison, yes.)

- Would direction matter? It depends on how simple the model is, but realistically, it's very likely. E.g., the LED of a dog -> cat might be significantly different than cat -> dog. Presumably it'd matter the most in deep, structural changes such as prokaryote <-> eukaryote evolution. Loss of function/structure is always easier to evolve than function/structure.

- How realistically could one model the conditions that these evolutionary simulations would operate under? E.g., would the number of offspring need to be arbitrary for each simulation? Would the rate of mutation vary between dogs and cats? How could the model be responsive to operation under different ecosystems? How to deal with many changes in these quantities over time, if you're charting a large LED (e.g., bacteria->cat)? I guess the answer to this is, you could make things as complicated as you wanted. But you wouldn't have to.

- In theory, the impact of genetic differences between arbitrary members of the same species would be minimized by the logarithmic nature of the metric. Would this usually be the case? Presumably LED could be used to explore variation pertaining to this: e.g., species X has a mean LED of 1.4, whereas species Y has a mean LED of 1.6.

- Depending on the progress of tissue and functional domain gene expression analysis and what inherent and epistemological messiness lies therein, this could be applied to subsets of organisms: finding a provisional sort of evolution distance between organism X's immune system and organism Y's immune system, or limbs, or heart, etc. Much less conceptually elegant, but perhaps still useful.

Anyway, this is a different way of looking at the differences between genomes. Not more or less correct than others-- but, at least in some cases, I think more helpful.

Edit, 9/27/09: Just read an important paper on the difficulty of reversing some types of molecular evolution, since neutral genetic drift accumulated after a shift in function may not be neutral in the original functional context. In the context of Logarithmic Evolution Distance, I think it underscores the point that LED can't be taken literally, since it doesn't take function or fitness into account. But then again, neither do the other tools it's competing against, and this doesn't impact its core function as an estimation-based tool with which to make relative comparisons.


Brainstorm: An alternative to the tree of life

One of the greatest insights of modern biology is the Tree of Life metaphor-- that all organisms share common ancestors if we go back far enough, and that we can understand a great deal about an organism based on which evolutionary forks it and its ancestors have taken.

This has been and continues to be a profoundly useful tool in nearly all subfields of biology. But it was created before we knew anything about genetics, and it's starting to show its age-- especially in the context of single-cell organisms, whose cellular machinery and evolutionary history allow organisms very far apart in the 'tree' to readily swap significant amounts of genetic material.[1] This sort of gene swapping, or Horizontal Gene Transfer, as it's called, happens in plants and animals as well-- think of mitochondria and chloroplasts, once organisms in their own right, now mere cellular power plants with much of their original genetic code shuffled into their hosts' genomes.[2] But as a rule, most HGT happens in the contexts of bacteria and viruses. And HGT is extremely common there.

So we have these concepts of distinct species and this branching tree of life, and they're incredibly useful when talking about plants and animals, but in the contexts of bacteria and viruses they become rather strained when organisms from very distant branches constantly share lots of genetic code. The core organizing assumption which gives the tree metaphor and our current phylogenic system meaning, that once organisms branch off sufficiently far from each other they can no longer share genetic code, is often false in these contexts.[3] And under many metrics, most of life's genetic diversity is contained in the bacterial and viral domains, so this is not a trivial problem.

So we can keep trying to extent the current tree metaphor, or we can start looking around for a new model.[4] I think both are worth doing.

So what would an alternative to the Tree of Life look like?

I don't have an answer to this per se, but it seems to me the way forward is to recognize the core insight of the tree metaphor- to group things that have more shared evolutionary history closer together- but to apply this insight at the level of the gene rather than the organism. Essentially, I think a new system could be built by sequencing everything and having computers crunch the numbers, identify co-evolved gene clouds, highlight the genetic links between organisms, and sort organisms based on these links.

This approach could simplify down into or replicate most of our current phylogeny in organisms with low HGT (eucaryotic organisms are mostly isolated co-evolved gene clouds which should be grouped together, and grouped near the other eucaryotic organisms they share recent history with) while leaving the door open to a more elegant treatment of the edge cases of e.g., bacteria and viruses, which may be amalgamations of distinct co-evolved gene clouds with separate evolutionary histories.

But the devil will be in the details, and creating a new phylogeny is particularly tricky in that any sorting algorithm includes contingent assumptions about what sort of answer we want when asking, what is the nature of the relation between organism X and organism Y?

It's interesting to think about. Realistically speaking, our current phylogeny is much too fundamental to most of modern biology to be replaced anytime soon. But it'll be interesting to see if and how people attempt to apply the gene-level shared history idea to patch up our current organism-level shared history phylogeny.


[1] This rampant HGT happens in multiple ways: bacteria can share plasmids, which are sort of modular pieces of genetic function able to be easily swapped in and out. If one strain of bacteria develops resistance to a drug, it may share that resistance to other strains through a plasmid. Bacterial DNA is also less isolated and protected than eucaryotic DNA, so 'free floating' DNA is much more likely to be integrated into the cell.

Viruses, on the other hand, exist by hijacking existing cellular machinery to splice themselves into genomes then copy themselves, and evolve resistance by being extremely sloppy in their duplication methods, both of which can lead to significant HGT. As well, viruses are hardly limited to infecting plants and animals; those which infect bacteria and other viruses (bacteriophages and virophages, respectively) can also be vehicles for HGT.

[2] Our genomes are filled with ancient, defunct viruses who spliced themselves into our genes but then couldn't get out. Recent surveys of the human genome indicate that these defunct viruses take up more space in our genome (2%) than do actual protein-coding genes (1.4%).

Recent research indicates that this has been a useful source of genetic diversity: the mammalian placenta, for instance, repurposes genes originally from an ancient retrovirus to protect itself from being attacked by the mother's immune system.

[3] That's the conceptual argument for a new type of phylogeny. The pragmatic argument is that an infectious bacteria or virus's position on the tree of life does not tell us much about how it spreads, where in the body it can thrive, or how to treat it. It would be nice to have a phylogeny that would naturally indicate such things.

[4] A possible extension of the tree metaphor is put forth by Frederik Cohan of Wesleyan University, who suggests adding an 'ecovar' notation (short for "ecological variant") to bacteria and viruses. As Carl Zimmer so succinctly puts it, "The bacterial strain that caused the first recorded outbreak of Legionnaires’ disease in Philadelphia, for example, should be called Legionella pneumophila ecovar Philadelphia."


It may be neither here nor there, but in writing out a wishlist of the perfect phylogenic system, I came up with that it should deal with the following:
- Common descent, evolution of major function, and speciation (as the tree metaphor currently does);
- HGT (specific gene chunks that were transfered, and past lineages & other signifying metadata of those genes);
- Phenotype & function: cellular mechanics/architecture and proteomic profile (trying to classify organisms in terms of what goes on 'under the hood');
- Current ecological niche (e.g., Cohan's 'ecovar' notation).

Others' lists may differ.


How processed foods, pesticides, and pollution are bad for us (aka, the "twinkies are like smallpox blankets" hypothesis)

Last summer I wrote about a potential link between high fructose corn syrup and some of the malaise of modern society. Here's a more general argument- which I suspect is significantly true- for how and why things like HFCS are likely bad for us.

I believe in the next ten years we'll increasingly learn that many of the corrosive effects of eating poorly aren't due to an overabundance of sugar, fat, or carbohydrates; they're due to the chemicals in processed foods and our environment interacting with the body's epigenetic machinery in unpredictable and unhealthy ways.

Now, it should be noted that our cells are, as a rule, marvels of self-regulation: it's downright hard to get them 'out of whack'. We see this in the amount of biological redundancy in many cellular processes, in the relative infrequency of obvious dysregulation (e.g., the chance of a cell turning cancerous), and in the layered conditionals for cell suicide should something go seriously wrong.

But in eating Western food, and living in a Western environment, we're continuously flooded with a menagerie of biologically active chemicals that evolution hasn't had a chance to foolproof our cell machinery against.[1] Biology is filled with examples of how evolution often does not protect against that which it hasn't been exposed to-- for a slightly crass illustrative analogy, consider that smallpox blankets may be to native americans what twinkies are to us. It takes time and, yes, often selection to get used to new things. Certainly, following conventional wisdom, some health problems may be caused by an imbalance in our intake of macronutrients-- too much fat or sugar, for instance-- but our ancestors didn't eat perfect diets and evolution has had some time to work on protecting us against these. The chemicals in processed foods, pesticides, and such, however, are an entirely new enemy, and known to affect epigenetics[2], a regulatory context in which hidden maladaptive changes can accumulate and affect one's phenotype.

This epigenetic dysregulation via processed foods, pesticides, and pollution I'm hypothesizing might happen directly, with these synthetic chemicals interacting with chromosomes, methyl groups, and such to push and prod gene regulation in unnatural ways, or indirectly via metagenetics (changing the constitution of our gut flora, which in turn influences our gene expression and epigenetics. Alternatively, following Michael Pollan, perhaps the absence of natural enzymes in highly processed foods could lead to these epigenetic outcomes.

How much damage did smallpox do to the Native Americans? Quite a lot. How much damage has eating Twinkies done to us? I fear the answer here will also turn out to be, Quite a lot.

At any rate, eating organic and staying away from processed foods is currently considered more of a philosophical lifestyle choice than one with definite health consequences. This very well may change over the next few years as we start to learn more about the intersection of food and epigenetics.

[1]. Most synthetic pesticides are, in fact, used because of their demonstrable ability to circumvent their targets' cellular safeguards and cause malfunctions.

[2]. Olaharski AJ, Rine J, Marshall BL, Babiarz J, Zhang L, et al. 2005 The Flavoring Agent Dihydrocoumarin Reverses Epigenetic Silencing and Inhibits Sirtuin Deacetylases. PLoS Genet 1(6): e77. doi:10.1371/journal.pgen.0010077

- Unfortunately, this is a difficult hypothesis to test. The gold standard would be to compare the epigenetic configurations of identical twins raised apart, in similar demographics but eating different diets and surrounded by different pollution levels. But there are only so many identical twins available for study.

- This hypothesized connection can be taken in either direction: one, that the sea of chemicals which surrounds us in modern life has detrimental epigenetic effects; and two, that a significant amount of the physiological (and perhaps social) malaise of Western societies can be traced back to epigenetic changes induced by our chemical environment.

Edit, 5-12-09: Nicholas Wade of the NYT has a great piece on epigenetics up-- From One Genome, Many Types of Cells. But How?


Hiatus over

The title says it all. Expect more posts soon.