"These experiments have not been published yet, but I shall assume the results."
- A.V. Hill, “The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves,” Proceedings of the Physiological Society, Jan. 22 1910, cited 1265 as of December 2011.
[Students of chemistry, computational biology, physics, and engineering] are increasingly finding themselves engages in research activities that cross traditional disciplinary lines. Successful outcomes for such projects often hinge on their ability to translate complex phenomena into simple models and develop approaches for solving these models. Because of its broad scope, statistical mechanics plays a fundamental role in this type of work and is an important part of a student’s toolbox.
- Mark E. Tuckerman, Statistical Mechanics: Theory and Molecular Simulation
A model that is wrong in some details may nevertheless be useful in guiding and interpreting experiments. For computational modeling to be useful in incompletely understood systems, we must focus not on building the final, perfect, model with all parameters precisely determined, but on building incomplete, tentative, and falsifiable models in the most expressive and predictive fashion feasible.
- Gutenkunst, et al. “Universally Sloppy Parameter Sensitivities in Systems Biology Models” PLoS Computational Biology, October 2007 | Volume 3 | Issue 10 | e189
I’ve been a big fan of physics Nobel laureate Carl Wieman’s research on science education. He focuses on evidence-based methods for teaching science (and other subjects, but primarily science). Check out these teacher resources at the Carl Wieman Science Education Initiative at the U of British Columbia here.
Anybody expecting earthshaking news from Berkeley, now that the Berkeley Earth Surface Temperature group being led by Richard Muller has released its results, had to be content with a barely perceptible quiver. As far as the basic science goes, the results could not have been less surprising if the press release had said “Man Finds Sun Rises At Dawn.” This must have been something of a disappointment for anyone hoping for something else.
For those not familiar with it, the purpose of Berkeley Earth was to create a new, independent compilation and assessment of global land surface temperature trends using new statistical methods and a wider range of source data. Expectations that the work would put teeth in accusations against CRU and GISTEMP led to a lot of early press, and an invitation to Muller to testify before Congress. However, the big news this week (e.g. this article by the BBC’s Richard Black) is that there is no discernible difference between the new results and those of CRU.
Muller says that “the biggest surprise was that the new results agreed so closely with the warming values published previously by other teams in the US and the UK.” We find this very statement surprising. As we showed two years ago, any of various simple statistical analyses of the freely available data at the time showed that it was very very unlikely that the results would change.
Richard Muller, the skeptic we’re talking about, seems to have had different motivations from many of the professional climate skeptics. He basically appears to have suffered from nothing more than characteristic physicist arrogance, the belief that people in lesser sciences just don’t know what they’re doing. (Economists experience this all the time, but we make up for it by being equally condescending to sociologists.) To his credit, he went and tried to do better — and is now being honest in revealing that what he got was pretty much the same as the results of previous research.
Harold Bloom makes Sophocles sound like H.P. Lovecraft
Oedipus, repeatedly stabbing his eyes with Jocasta’s brooches, passes judgment not so much upon the seen, as so upon the light by which we see. I interpret this as his protest against Apollo, who brings both light and the plague. The Freudian trope of blinding for castration seems to me less relevant here than the outcry against the god.
To protest Apollo is necessarily dialectical, since the pride and agility of the intellect of Oedipus, remorselessly searching out truth, in some sense is also Apollo’s. That must mean that the complaint is also against the nature of truth. In this vision of reality, you shall know the truth, and the truth will make you mad.
- Harold Bloom, Modern Critical Views: Sophocles
The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents. We live on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto learned little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age.
- H.P. Lovecraft, The Call of Cthulhu
I have to admit, the Lovecraft passage is a strikingly apt description of Oedipus Rex.
As I contemplate presenting my research plans in job talks, I’m worried about clearly conveying what we get out of quantitative models. The vast majority of biologists don’t build or use quantitative models, which I recognize is a reasonable consequence of the history of the field, but I find it shocking nonetheless. What this means is that many of these researchers don’t share my fundamental outlook, and, as good skeptical scientists, they won’t take it for granted that models are useful. In fact they’ve probably seen plenty of examples of bad models.
So here is how I justify my mathematical modeling work: people often justify models by saying we can use them to make predictions, or we can use them to test the completeness of our ideas, to determine whether we are missing any major system players. Models can be used to formally summarize large amounts of experimental results.
To me, these are all important but derivative reasons; they derive from what I see as the most fundamental and compelling reason to model, which is: to understand how the behavior of a system arises as a consequence of the interactions of its parts. This understanding is generally only achievable with quantitative models, because we are not dealing with simple Rube-Goldbergian linear chains of causality. In most real-world systems, we deal with large numbers of complex and often non-linear interactions, and thus we cannot understand them without quantitative models.
In statistical mechanics, at equilibrium every possible configuration of the system is just as probable as any other configuration. Rare states aren’t less probable than any other state; it’s just that they are only achieved by unique microscopic configurations - as any player of Settlers of Cataan intuitively knows. Any given roll of two six-sided dice is equally probable as any other roll, but because there are so many ways to roll a seven, seven is the most probable ‘macroscopic’ state of the dice.
This is intuitively obvious in the case of two six-sided dice, but in a complex system of particles, it’s not obvious why this assumption of equiprobable micro-states should be true. Enter Liouville’s theorem, which justifies our assumption of equal probabilities. I can’t explain Liouville’s theorem here (or anywhere really… someone else would need to explain it) but one of the consequences is this interesting idea:
Transient states of an active, dissipative system that approaches steady state are not the same as fluctuations of a system at equilibrium - this is one of the profound differences between a steady-state system and an equilibrium system:
There are no attractors [in equilibrium phase space]. In other dynamical systems, most states of the system are usually transient, and the system settles down onto a small set of states called the attractor. A damped pendulum will stop moving; the attractor has zero velocity and vertical angle. A forced, damped pendulum will settle down to oscillate with a particular amplitude; the attractor is a circle in phase space. The decay of these transients in dissipative systems would seem closely related to equilibration in statistical mechanics, where at long times all initial states of the system will settle down into a static equilibrium behavior. Perversely, we have just proven [earlier in the text] that equilibration in statistical mechanics happens by a completely different mechanism! In equilibrium statistical mechanics all states are created equal; transient states are temporary only insofar as they are very unusual, so as time evolves they disappear, to arise again only as rare fluctuations.
- James Sethna, Entropy, Order Parameters, and Complexity, p. 65
Interestingly, these two viewpoints tend to split somewhat cleanly between those who came into biology as computational people, and those who came in as experimentalists. (The split’s not perfect but the trend is there, and you can see it in the authorship of the two papers above.) Computational people (or, at least those who came in as computational people - I’m not making judgments about anyone’s experimental skills) are more likely to believe in pervasive transcription, while others are more likely see it as experimental and biological noise.
Why we wouldn’t just calculate the future using the laws of physics and the positions and momenta of every particle, even if we could:
- Most systems of interest exhibit chaotic motion, where the time evolution depends with ever increasing sensitivity on the initial conditions - you cannot know enough about the current state to predict the future.
- Even if it were possible to evolve our trajectory, knowing the solution would for most purposes be useless; we are far more interested in the typical number of atoms striking a wall of a box, say, than the precise time a particle hits.
- James Sethna, Entropy, Order Parameters, and Complexity, p. 38
“Let’s start on safe ground. We all agree, surely, that theory — the formulation of hypotheses — is important in biology. Techniques are essential, as is the careful collection of quantitative data. But without ideas to give them shape and meaning, those endless successions of base sequences, expression profiles, electrical recordings and confocal images are as featureless as a plate of tofu. All really big discoveries are the result of thought, in biology as in any other discipline. Allostery, genes, DNA structure, chemi-osmosis, immunological memory, ion channels were all once just a twinkle in someone’s eye. And the work of most contemporary research laboratories still takes place within a framework of hypothesis, although practitioners may not always recognize this fact. As Charles Darwin once remarked: “How odd it is that anyone should not see that all observation should be for or against some view if it is to be of any service.”—Denis Bray, Reasoning for Results, Nature 412, 863 (30 August 2001)
Carlos Fuentes’ latest, Destiny and Desire sounds potentially amazing: a reflection on Mexico’s history and current troubles by looking at the parallel lives of two youths growing up in modern-day Mexico City.
But the book fell flat. It just wasn’t compelling. The characters were boring. The prose, with some notable exceptions, was unremarkable, especially during sections of long philosophical reflection.
The conception was great, but there was just no power in the execution.
The editor of the journal Remote Sensing just resigned over the fact that his journal published a paper that should never have been published. Real Climate explains what that means - being controversial or eventually shown wrong is *not* an indication that a paper shouldn’t have been published. This is what makes a paper bad:
But what makes a paper ‘bad’ though? It is certainly not a paper that simply comes to a conclusion that is controversial or that goes against the mainstream, and it isn’t that the paper’s conclusions are unethical or immoral. Instead, a ‘bad’ paper is one that fails to acknowledge or deal with prior work, or that makes substantive errors in the analysis, or that draws conclusions that do not logically follow from the results, or that fails to deal fairly with alternative explanations (or all of the above). Of course, papers can be mistaken or come to invalid conclusions for many innocent reasons and that doesn’t necessarily make them ‘bad’ in this sense.
A major goal in all sciences is to be able to explain large-scale phenomena as consequences of the interactions of small-scale components. This is what drives me to study what I’m studying - in my case, the large scale-phenomena are patterns of gene expression, and the small-scale components are transcription factors and DNA binding sites.
Biologists do a lot of measuring of large-scale phenomena, via genomics or classical genetic phenotying. Biologists also spend a lot of time discovering what the small-scale components and interactions are. But they don’t really spend enough effort trying to understand how it is that large-scale phenomena are consequences of the interactions of small-scale components.
Just to be clear: your typical blob-and-arrow pathway diagram featured in Figure 7 of nearly every Cell paper (Fred Cross calls these ‘Figure 7 models’) is not the answer to this question, because it is essentially impossible, in nearly all cases, to predict the large-scale behavior just by looking over one of those diagrams.
I was going to mention something about Kakutani’s piece in the New York Times, Outdone by Reality, arguing that art and literature hasn’t really changed in response to 9-11. But I don’t really have much to say, so instead I’ll direct you to a better response, over at Conversational Reading:
And while I think that Kakutani is right that no single great work of art came out of 9/11 (the day itself) in the way that monumental books and movies were set during the Vietnam War, I think she’s absolutely wrong that literature of the era has not been written in the 10 years since. I also don’t know where in the world she gets the misguided notion that “9/11 did not really change daily life for much of the country,” seeing as it has been used to justify everything from war to torture to tax cuts to surveillance.
There are some goods that the market on its own will undersupply. These include public goods, the benefits of which can be enjoyed by all members of society - and among these are certain key innovations. America’s third president, Thomas Jefferson, pointed out that knowledge was like a candle: as one candle lights another, its own light is not diminished. It follows that it is inefficient to restrict the use of knowledge. The costs of such restrictions are particularly strong in the case of basic science. But if knowledge is to be freely disseminated, government must assume responsibility for financing its production. That is why the government takes on a crucial role in the promotion of knowledge and innovation.
- Joseph Stiglitz, Freefall p. 202
He goes on to note the role of patents:
It is possible to induce the private sector innovation [in basic sciences] by restricting the use of knowledge through the patent system, though in thus enhancing private returns, social returns are diminished. A well-designed patent system tries to get the right balance, providing incentives for innovation without unduly restricting the use of knowledge.
If on a winter’s night a traveler… it is interesting reading this after Ovid, because Calvino shares Ovid’s narrative skill, piling on one compelling story after another, transitioning from one to the next abruptly but deftly.
Cortázar structures his book around two images of the natural world, and two contrasting images of science dealing with the natural world.
There is the story of the migration of the eels from the ocean, up into European rivers and lakes, traveling in vast numbers until they disperse into small lakes, rivers, etc., where they wait eighteen years, until maturity calls them back to the ocean, when they again come together in huge numbers and return to their deep-sea spawning grounds. The scientists who study this, who name and classify the various stages of the eel, are portrayed by Corázar as unimaginative agents of ‘Lady Science’ and shackling social institutions, reductionists who in their tallying and fine divisions can’t see the eel life cycle for the awesome phenomenon that it is.
I’ll admit that the first time I read this it rankled a bit, Cortázar’s scientists felt like straw men, because good scientists are as awed by the whole of nature as anyone. I thought of this great Feynman quote (click and watch the video):
I have a friend who’s an artist, and he sometimes takes a view which I don’t agree with. He’ll hold up a flower and say, “Look how beautiful it is,” and I’ll agree. But then he’ll say, “I, as an artist, can see how beautiful a flower is. But you, as a scientist, take it all apart and it becomes dull.” …There are all kinds of interesting questions that come from a knowledge of science, which only adds to the excitement and mystery and awe of a flower. It only adds. I don’t understand how it subtracts.
But I felt better when I realized how Cortázar contrasts one set of scientists, who are maybe fairly conventional, very proper, part of the conformity-promoting institutions of society, against a very different scientist, attempting to comprehend another great image of nature.
The image complementary to that of the eels is the vast panorama of the night sky, as seen from the towers of an 18th century observatory in Jaipur, India, built by the Prince Jai Singh. This sky, “an unconquerable space,” “a studded incomprehensibility,” is tackled by Jai Singh, who tries to “tear a shred of its code” and “gather into one mental fist the reins of that multitude of twinkling…” Jai Singh is the Actaeon who survives Diana’s “traditional dogs”, and returns “to the hunt until the day he finds Diana and possesses her beneath the foliage, takes her virginity that no clamor now defends”; he will “tear her despotic maidenhood in order to rise up naked and free and peer up at the open…”
Uh, wow. It’s not clear to me whether here Diana is incomprehensible nature or the weight of societal constraints, or both, but in any case, the book ends with a view of the scientist trying to comprehend the whole, but he has to do so in a violent manner.
For this man who rises up “naked and free,” “a drawing of reality climbs up the Jaipur stairway.” At the top of that observatory tower, there is “the image of the world as Jai Singh might have sensed it.” There, Jai Singh rebels against the restricting myths and conventions of past science, he is
A guerrilla of the absolute against the astrological fatality that guided his lineage, that decided births and deflowerings and wars… his stone and bronze devices were the machine guns of real science, the great reply to a total image facing the tyranny of planets and conjunctions and ascendants; the man, Jai Singh… answered human fatality as a mortal provoking the cosmic bull, decided to channel the astral light, trap it in retorts and spirals and ramps, clipped the nails that bled his species; and all that he measured and classified and named, all his astronomy on illustrated parchments was an astronomy of the image, a science of the total image, a leap from the brink to the present, or the astrological slave to the man who stands in dialogue with the stars.
The effort to confront the total image then has political implications - by comprehending nature in its totality (or at least attempting to), by entering into a true dialogue with the stars, we free ourselves from societal constraints that damage us and prevent us from living as we should, as autonomous beings. This is an image of science as a revolutionary power, which stands in contrast to science as a means of tyranny. And clearly that is a very relevant contrast to draw, both when Cortázar wrote this book and now.
More Cortázar… this passage on ocean tides reminds me of some of the most remarkable landscape passages in literature, Melville’s description of the gnarled hillsides of the Galapagos in The Encantadas, Lem’s passages on the alien landscapes of the oceans of Solaris, and McCarthy’s barren southwest mountains and desert flats in Blood Meridian:
…how could he not have known that the animal Earth would suffocate in a slow stillness if it had not always been in the lungs of the astral steel, the sneaky traction of the moon and the sun drawing and repelling the green breast of the waters. Inspired, expired by some other power, by the grace of a swaying that utterly unimaginable springs make measurable and as if within reach of a marble tower and insomniac eyes, the ocean breathes and dilates its alveoli, sets its renewed blood off to break furiously on the crags, draws its spirals of spindle-shaped matter, concentrates and disperses the swells…
One of my favorite writers is the Argentine Julio Cortazar, and three years ago I had never heard of him.
He died in 1984, but some of his books are still coming out in English. The most recent is From The Observatory, and I’ll let Scott Esposito in his excellent review tell you what it’s about:
His images attempt to put us in touch with a cosmos that is fundamentally a mystery, and also to show us that this cosmos very much includes humans – particularly their artefacts and their languages – as a part of this “primordial poem”. From the Observatory imagines how we can at once be part of it and respond to it.
It begins with Cortázar calling forth an hour outside of the flow of time. From here, the book abruptly swerves into a forcefully holistic image of the Earth, with the author introducing his two protagonists: on the one side is Jai’s observatory, which comes to embody the scientific world of the human, and on the other side are seafaring eels, which are agents of the natural world.
So you can understand why a scientist ought to be interested in this. It begins with Cortazar’s brilliant description of slipping into a daydream, during which different ideas about nature begin to associate in his mind:
This hour that can arrive sometimes outside of all hours, a hole in the net of time…and without notice, without any unnecessary warnings of transition, in a Latin Quarter café or in the last scene of a film by Pabst, an approach to what no longer follows the order god meant it to, access between two activities installed in the niche of their hours, in the beehive day, like this or in another way (in the shower, in the middle of the street, in a sonata, in a telegram) touching on something that doesn’t rest on the senses, a breach in succession, and so like this, so slipping, the eels for example…
I do my best to avoid getting embroiled fruitless political debates online, but today I am particularly depressed about the general lack of analytical intelligence in the population. So I am engaging in what John Baez calls nasty stuff.
As I develop my research, I occasionally suffer angst over the risk that many biologists will be bothered by the fact that I’m not heavily invested in the details of one particular system, that I focus on very general themes related to transcriptional regulation and that my research is to some degree, organism and pathway agnostic.
My response is, take a look at our best-characterized model organisms, especially the simple critters like brewer’s yeast and E. coli. Are there any poorly characterized pathways or processes in these guys that are worth spending the next 10-15 years on, if you’re a young investigator starting out?
No, absolutely not. So what most people are doing is moving on to other organisms with less well-characterized processes, especially those directly connected with human disease, which we can now spend the next 10-15 years characterizing in detail, with the aid of some amazing molecular tools that make it possible to do things that were previously only possible in yeast.
But my view is this: take a look at yeast, and all we know about it. Is that really what it means to understand an organism? We have a parts list and a bunch of phenomenological narratives that tell stories about how things work, but, with very few exceptions, we have essentially no predictive or engineering power (except trial and error engineering).
Can’t we do better than that? Is this going to be our limit when we’ve characterized humans as well as we’ve now got yeast characterized?
We can do better. To do better than that, we need a quantitative, framework for understanding how processes work, and we need to understand these processes in terms principles.
“My interest in science was always essentially limited to the study of principles, which best explains my conduct in its entirety. That I have published so little is attributable to the same circumstance, for the burning desire to grasp principles has caused me to spend most of my time on fruitless endeavors.”
- Albert Einstein, quoted in Peter Galison’s Einstein’s Clocks, Poincaré’s Maps, p. 241
To live in the world of creation - to get into it and stay in it - to frequent it and haunt it - to think intensely and fruitfully - to woo combinations and inspirations into being by a depth and continuity of attention and meditation - this is the only thing.
Henry James, quoted by Wallace Stevens in Letters of Wallace Stevens, p. 506 (letter June 20 1945)