Can we turn biology into engineering?

Vijay Pande of Andreessen-Horowitz/Stanford has a thought-provoking piece in Scientific American in which he lays out a game plan for how we could potentially make biology more like engineering. He takes issue with what Derek (Lowe) once called the “Andy Grove fallacy”, which in a nutshell says that you can make fields like biotechnology and drug discovery as efficient as semiconductor or automobile engineering if you borrow principles from engineering.

There are parts of the piece that I resoundingly agree with; for instance, there’s little doubt that fields like automation and AI are going to have a significant impact on making biological experiments more reproducible, many of which are still more art than science and subject to the whims and sloppiness of their creators and their lab notebooks. Vijay is also optimistic about making biology more modular, so that one can string along parts of molecules, cells and organelles to enable better biological engineering of body parts, drugs and genetic systems. He also believes that bringing more quantitative measurements encoded into key performance indicators (KPI) will make the discipline more efficient and more mindful of its successes and failures. One point which I think is very important is that these kinds of approaches would allow us to gather more negative data, a data collection problem that still hobbles AI and machine learning approaches.

So far I am with him; I don’t believe that biology can’t ever benefit from such approaches, and it’s certainly true that the applications of AI, automation and other engineering-based approaches are only going to increase with time. But the article doesn’t mention some very fundamental differences between biology and engineering which I think demarcate the two substantially from each other and which make knowledge transfer between them highly problematic.

Foremost among these are non-linearity, redundancy and emergence.

Let’s take two examples which the piece talks about that illustrate all three concepts – building bridges and the Apollo project. Comparisons with the latter always make me wince a bit. Vijay is quite right that the right approach to the Apollo program was to break the problems into parts, then further break those parts up into individual steps such as building small models. The scientists and engineers working on the program gradually built up layers of complexity until the model that they tested was the moon landing itself.

Now, the fact is that we already do this in biology. For instance, when we want to understand or treat a disease, we try to break it down to simpler levels – organelles, cells, proteins, genes – and then try to understand and modulate each of these entities. We use animal models like genetically engineered mice and dogs as simpler representatives of complex human biology. But firstly - and as we keep on finding out - these models are pale shadows of true human biology; we use them because we can't do better. And secondly, even these ‘simple’ models are much more complex than we think. The reasons are non-linearity and emergence, both of which can thwart modular approaches. The sum of proteins in a cell is not the same as the cell phenotype itself, just like the sum of neurons in a human brain is not the brain itself. So modulating a protein for instance can cause complex downstream effects that depend on both the strength and nature of the modulating signal. In addition, biological pathways are redundant, so modulating one can cause another one to take over, or for the pathway to switch between complex networks. Many parts downstream, even ones that don’t seem to be directly connected, can interact with each other through complex, non-linear feedback through far-flung networks.

This is very unlike engineering. The equivalent of these unpredictable consequences in building a bridge, for example, would be for a second bridge to sprout out of nowhere when the first one is built, or the rock on the other side of the river suddenly turning from metamorphic to sedimentary, or the sum of weights of two parallel beams on the bridge being more than what simple addition would suggest. Or imagine the Apollo rocket suddenly accelerating to ten times its speed when the booster rockets fall off. Or the shape of the reentry vehicle suddenly changing through some weird feedback mechanisms as it reaches a certain temperature when it’s hurtling through the atmosphere. 

Whatever the complexities of challenging engineering projects like building rockets or bridges, they are still highly predictable compared to the effects of engineering biology. The fact of the matter is that the laws of aerodynamics and gravity were extremely well understood before the Apollo program (literally) took off the ground, so as amazing as the achievement was, it didn't involve discovering new basic scientific laws on the fly, something that we do a lot in biology. Aircraft design is decidedly not drug design. And all this is simply a product of ignorance, ignorance of the laws of biology and evolution – a clunky, suboptimal, haphazard, opportunistic process if there ever was one – relative to the laws of (largely predictable) Newtonian physics that underlie engineering problems.

The concept of modularity in biology therefore becomes very tricky compared to engineering. There is some modularity in biology for sure, but it’s not going to take you all the way. One of the reasons is that unlike modularity in engineering, biological modularity is flexible, both spatially and temporally. This is again a consequence of different levels of emergence. For instance, a long time ago we thought that the brain was modular and that the fundamental modules were neurons. This view has now changed and we think that it’s networks of neurons that are the basic modules. But we don’t even think that these modular networks are fixed through space and time; they likely form, dissolve and change members and locations in the brain according to need, much like political groups fleetingly forming and breaking apart for convenience. The problem is that we don’t know what level of modularity is relevant to addressing a particular problem. For instance, is the right ‘module’ for thinking about Alzheimer’s disease the beta-amyloid protein, or is it the mitochondria and its redox state, or is it the gut-brain axis and the microbiome? In addition, modules in biology are again non-linear, so the effects from combining two modules are not going to simply be twice the effects of one module – they can be twice or half or even zero.

Now, having noted all these problems, I certainly don’t think that biology cannot benefit at all from the principles of engineering. For one thing, I have always thought that biologists should really take the “move fast and break things” philosophy of software engineering to heart; we simply don’t spend enough time trying to break and falsify hypotheses, and this leads to a lot of attrition and time chasing ghosts down rabbit holes. More importantly though, as a big fan of tool-driven scientific revolutions, I do believe that inventing tools like CRISPR and sequencing will allow us to study biological systems at an increasingly fine-grained level. They will allow us to gather more measurements that would allow better AL/machine learning models, and I am all for this.

But all this will work as far as we realize that the real problem is not improving the measurements, it’s knowing what measurements to make in the first place. Otherwise we find ourselves in the classic position of the drunkard trying to find his keys below the lamp, because that’s where the light is. Inventing better lamps, or a metal detector for that matter, is not going to help if we are looking in the wrong place for the keys. Or looking for keys when we should really be looking for a muffin.

Psychiatry and neuroscience: Don't sacrifice proven emergence at the altar of unproven reductionism

John Markowitz who is a clinical psychiatrist at the NIH has a cogent column in the New York Times in which he argues that an excessive focus on neuroscience translational research is stifling useful and proven research in psychiatry. His main point is that the neuroscience research is unproven and long term, and while it may promise attractive dividends, there are many patients who need good psychiatric treatment now, patients who cannot work along the timelines promised by cutting edge neuroscience work.

I think in general he's right. Neuroscience seeks to find out the basic mechanisms governing neural health and disease by way of genes, receptors and small molecule drugs. Psychiatry and especially psychotherapy takes a more empirical and holistic approach, trying various combinations of talk therapy and drugs to treat mental illness. Even psychiatry itself has suffered from the kind of crisis that the author talks about. For instance, it is now increasingly clear that talk therapy (especially CBT) works at least as well as psychiatric drugs like antidepressants.

To me, at least part of the debate seems to be about a topic that I have often explored on this blog: emergence vs reductionism. Generally speaking, the goals of neuroscience are reductionist, seeking to modulate mental processes in health and disease by understanding and engineering interactions between genes, proteins and drugs at the molecular and network levels. The goals of psychiatry are emergent and empirical. Psychiatry does not care about the underlying molecular mechanisms of mental health; instead its goal is to work at a higher and more holistic level, empirically trying out different approaches until a particular combination of methods seems to show efficacy. It is not surprising that drugs like antidepressants which aim to interact with specific protein receptors in the brain are often found wanting because they target only part of a much larger system.

This philosophical difference between neuroscience and psychotherapy also strikes me as being a bit similar to the philosophical difference between chemistry and physics which I have often talked about here. Physics may want to find out how the world works by tracing the interactions between elementary particles like quarks, but chemists have little use for this information, benefiting tremendously instead by understanding semi-empirical concepts like hydrogen bonds and hydrophobic effects. The time discrepancy that the author points out regarding the fruits of neuroscience research and psychiatry also applies to physics and chemistry; if chemists waited long enough to be able to use physics and understand every complex molecular system from first principles, we would still be living in the age of alchemy.

The NYT article ends by appealing to the NIH to not sacrifice proven empirical psychiatry research at the altar of long-term translational research in neuroscience, and this underscores yet another one of the more general problems with translational research that I and others have pointed out. It is why the much celebrated and publicized Brain Initiative troubles me; I fear that it will detract from more mundane but effective psychiatric research. Far flung reductionist research may well promise and eventually bring great insights, but it should not be pursued at the cost of immediately workable emergent research whose very lack of precision makes it so useful.

What makes me human? On personhood and levels of emergence

Ce n'est pas une machine

There is a wonderful episode of Star Trek (The Next Generation) titled “The Measure of a Man” which tackles an issue that all of us take for granted. The episode asks if Data – the highly intelligent and indispensable android on the USS Enterprise – has self-determination. In fact, Data faces an even greater challenge: he has to prove that he is equivalent to a person. If he cannot do this he faces a grim fate.

We might think it’s easy enough to decide if an android is a person or not, partly because we think we know how we define ourselves as persons. But is it really that simple? Can we actually ascribe unique, well-defined qualities to ourselves that lend themselves to a singular definition of “personhood”? Can we be sure that these qualities distinguish us from cats and jellyfish? Or mountains and computers for that matter?

Let’s throw down the gauntlet right away in the form of a question: A tree and I are both composed of the same kinds of atoms – carbon, hydrogen, oxygen and a handful of others. So what makes me a person and the tree a mere tree, a non-person?

To a simple first approximation, the difference lies in the arrangement of those atoms. Clearly the arrangement is different in a tree, in a lion and in me. But it’s not just the arrangement of the parts, it’s the connections between them. If you delete enough connections between the neurons in a human brain, at some point the person possessing that brain would clearly cease to be a sentient human being. The same goes for the connections between the cells in a tree.

Thus, simply from a physical standpoint, a person is an object that presents an arrangement of atoms in a particular configuration. But that definition comes no closer to telling us exactly what makes that particular arrangement special. To get some insights into these reasons, it’s worth thinking about the concept of emergence.

Emergence is a very common phenomenon, and in its basic incarnation it simply means that the whole is different from the sum of the parts; or, as the physicist Philip Anderson put it in a seminal article in 1972, “More is Different”. Going back to our example, the human brain may be composed of the same atoms as a tree, but because of the unique arrangement of these atoms and the connections between them, the sum total of these atoms possesses properties that are very different from those of the individual atoms. Just as an example, a carbon atom in our brain can be uniquely defined by what are called quantum numbers – atomic parameters related to properties like the spin of the atom’s electrons, the energy levels on which the electrons lie and their angular momentum. And yet it’s downright absurd to talk about these properties in the context of the brain which these atoms make up. Thus it’s the emergent properties of carbon and other atoms that contribute to the structure and function of the human brain.

Emergent properties of individual atoms don’t uniquely make a human person, however, since even the brains of cats and dogs exhibit these properties. We don’t yet have a perfect understanding of all the qualities that distinguish a cat’s brain from a human’s, but we do know for certain that at least some of those qualities pertain to the size and shape of parts of the brains in the two species and the exact nature of the connections between their neurons. For instance, the cerebral cortex in a human brain is bigger and far more convoluted than in a cat. In addition the human brain has a much greater density of neurons. The layering of neurons is also different.

Taking our idea of emergence further, each one of these qualities is an emergent property that distinguishes cat brains from human brains. There are thus different levels of emergence. Let’s call the emergence of properties arising when individual atoms coalesce into neurons Level I emergence. This level is very similar for humans and cats. However, the Level II emergence which arises when these neurons connect differently in humans and cats is very different in the two species. The Level III emergence that arises when these connections give rise to modules of a particular size and shape is even more different. The interactions of these modules with themselves and with the environment presumably give rise to the unique phenomenon of human consciousness: Level IV emergence. And finally, the connections that different human brains form with each other, giving rise to networks of families, friends, communities and societies, constitute an overarching Level V emergence that truly distinguishes persons not just from cats but also from every other creature that we can imagine.

This idea of thinking in terms of different levels of emergence is useful because it captures both similarities and differences between persons and non-persons, emphasizing the common as well as the distinct evolutionary roots of the two kinds of entities. Cats and human beings are similar when defined in terms of certain levels of emergence, but very different when defined in terms of ‘higher order’ levels.

The foregoing discussion makes it sounds as if a simple way to distinguish persons from non-persons would be to map different levels of emergence on each other and declare something to be a non-person if we are successfully able to map the lower levels of emergence but not the higher ones. I think this is generally true if we are comparing animals with human beings. But the analogy is actually turned on its head when we start to compare humans with a very important and presumably non-person object: a sophisticated computer. In that case it’s actually the higher order emergent functions that can be mapped on to each other, but not the lower order ones. 

A computer is built up of silicon rather than carbon, and silicon and carbon are different emergent entities. But as we proceed further up the hierarchy, we start to find that we can actually simulate primitive forms of human thinking by connecting silicon atoms to each other in specific ways. For instance, we can teach the silicon-based circuitry in a computer to play chess. Chess is presumably a very high level (Level VIIXXI?) emergent human property, and yet we can simulate this common higher order property from very different lower order emergent properties. Today computers can translate languages, solve complex mathematical puzzles and defeat Go champions. All of these accomplishments constitute higher levels of emergent behavior similar to human behavior, arising from lower levels that are very different from those in human.

In fact it is precisely this kind of comparison that allowed Captain Jean-Luc Picard to secure personhood for Data. Data is a human-machine hybrid that is built up from a combination of carbon and non-carbon atoms. His underlying molecular structure is thus very different from those of persons. But the higher order emergent functions he exhibits – especially free will allows Picard to make a convincing case for Data to be treated as a person. This crucial recognition of emergent functions in fact saves Data’s life. It's what compels the man who is trying to dismantle him to address him as "he" instead of "it".

Whether it’s Data or a dolphin, a computer or a catfish, while it’s probably not possible to give a wholly objective and airtight definition of personhood, framing the discussion in terms of comparing different levels of emergent functions and behaviors provides a useful guide.

More is indeed different.

This piece was published yesterday in an issue of '3-Hours', the online magazine of Neuwrite Boston.

Why healthcare might not have benefited from a Steve Jobs-style disruptor

Steve Jobs holding a non-emergent object

The website Stat has an article titled "Why healthcare needs a Steve Jobs-style disruptor". The article which is by a physician named Damon Ramsey focuses on Jobs' ability to rethink design and to reinvent many of the ways in which we interact with the digital world. Ramsey thinks that Jobs would have had much to offer our current healthcare system with its convoluted regulatory mechanisms and information systems; it draws inspiration from an account by Jobs's sister in which she recounts him sketching out new designs for hospital systems even on his deathbed.

It's a pleasing vision, and Jobs was certainly a visionary who will go down as one of the most important people in the history of modern civilization, but I actually don't think that someone like him will be a disruptor in healthcare. The main reason is that healthcare is very different from electronics and computer science in terms of the complexity and predictability of its essential elements. Jobs might certainly have been useful in designing some of the electronic interfaces in hospitals, but that's a very limited part of the system. A major part of healthcare lies in the process of drug discovery, and in this vast arena I think Jobs would have been far less effective. In fact his working philosophy might have even been a hinderance.

Jobs' main achievement was to make computers and other electronics easy to use: even more than Bill Gates he brought computer technology to the masses. He was probably the best interface designer of his time, and he also had a genuine capacity to see the interconnections between various aspects of software and hardware.

And yet Jobs was designing his iPhones and Macs based on extremely well understood principles of software and hardware engineering. He certainly needed to think creatively in order to understand how to make these principles play well with each other, but he did not have to worry about the truth of the principles themselves. In addition the systems he was looking at were very modular, so most creative ways to package them together would work since they would not suffer from unexpected interactions. Put simply, there was very little chance that Jobs's devices would blow up.

In contrast, biological systems are startlingly non-modular and non-linear. Getting them to work is not a matter of designing interfaces. Not only do we not yet understand how to discover new drugs well, but we don't know how to do that because we lack an understanding of the human body to begin with. The "software" in case of drug discovery would be the genome which dictates the actual workings of the cell. The "hardware" is the universe of proteins that serve as workhorses for regulating every single important process in our body, from reproduction to the immune response. Unlike a microprocessor in which the welding together of software and hardware is a matter of engineering, welding together the software and hardware of the human body is currently impossible, simply because we are ignorant both about the nature of these components and their interactions.

I think Steve Jobs would have been completely befuddled if he had been confronted with the task of reinventing drug discovery. In fact one wonders if he would have fundamentally misunderstood the problem; it's worth noting that some people think that he died an early death because he wasted critical time in refusing standard chemotherapy for his cancer, opting to pursue untested "alternative" cures instead (although he does seem to have regretted his decision later). Knowing what we do about his philosophy, I get the feeling that he preferred the simple to the complex, the intuitive to the un-intuitive and the predictable to the chaotic. iPads and Macs are all of the former, biological systems are all of the latter. Notwithstanding his drive and intelligence, a Steve Jobs in drug discovery might have likely have taken his team down some very dark and interminable alleys.

The challenges that Jobs met were very impressive, but they were primarily engineering challenges which could be solved by putting together a bunch of smart people in a room and giving them enough money. The systems he was looking at were largely homogeneous, did not involve too much unexpected feedback and were non-emergent. The challenges that healthcare faces - and this includes the regulatory, economic and informational challenges which the article mentions - deal with highly emergent systems composed of very unexpected feedback and non-linear phenomena arising from extremely heterogeneous and diverse players. Solving those systems is not a matter of designing a better mousetrap, it's one of understanding what a mouse is in the first place. Steve Jobs would not exactly have been the right candidate for unraveling that particular pickle.

Physicist Leo Kadanoff on reductionism and models: "Don't model bulldozers with quarks."

I have been wanting to write about Leo Kadanoff who passed away a few weeks ago. Among other things Kadanoff made seminal contributions to statistical physics, specifically the theory of phase transitions, that were undoubtedly Nobel caliber. But he should also be remembered for something else - a cogent and very interesting attack on 'strong reductionism' and a volley in support of emergence, topics about which I have written several times before.

Kadanoff introduced and clarified what can be called the "multiple platform" argument. The multiple platform argument is a response to physicists like Steven Weinberg who believe that higher-order phenomena like chemistry and biology have a strict on-on-one relationship with lower-order physics, most notably quantum mechanics. Strict reductionists like Weinberg tell us that "the explanatory arrows always point downward". But leading emergentist physicists like P W Anderson and Robert Laughlin have taken objection to this interpretation. Some of their counterarguments deal with very simple definitions of emergence; for instance a collection of gold atoms have a property (the color yellow) that does not directly flow from the quantum properties of individual gold atoms.

Kadanoff further revealed the essence of this argument by demonstrating that the Navier-Stokes equations which are the fundamental classical equations of fluid flow cannot be accounted for purely by quantum mechanics. Even today one cannot directly derive these equations from the Schrodinger equation, but what Kadanoff demonstrated is that even a simple 'toy model' in which classical particles move around on a hexagonal grid can give rise to fluid behavior described by the Navier-Stokes equations. There clearly isn't just one 'platform' (quantum mechanics) that can account for fluid flow. The complexity theorist Stuart Kauffman captures this well in his book "Reinventing the Sacred".

Others have demonstrated that a simple 'bucket brigade' toy model in which empty and filled buckets corresponding to binary 1s and 0s (which in turn can be linked to well-defined quantum properties) that are being passed around can account for computation. Thus, as real as the electrons obeying quantum mechanics which flow through the semiconducting chips of a computer are, we do not need to invoke their specific properties in order to account for a computer's behavior. A simple toy model can do equally well.

Kadanoff's explanatory device is in a way an appeal to the great utility of models which capture the essential features of a complicated phenomenon. But at a deeper level it's also a strike against strong reductionism. Note that nobody is saying that a toy model of classical particles is a more accurate and fundamental description of reality than quantum mechanics, but what Kadanoff and others are saying is that the explanatory arrows going from complex phenomena to simpler ones don't strictly flow downward; in fact the details of such a flow cannot even be truly demonstrated.

In some of his other writings Kadanoff makes a very clear appeal based on such toy models for understanding complex systems. Two of his statements provide the very model of pithiness when it comes to using and building models:

"1. Use the right level of description to catch the phenomena of interest. Don't model bulldozers with quarks.

2. Every good model starts from a question. The modeler should aways pick the right level of detail to answer the question."

"This lesson applies with equal strength to theoretical work aimed at understanding complex systems. Modeling complex systems by tractable closure schemes or complicated free-field theories in disguise does not work. These may yield a successful description of the small-scale structure, but this description is likely to be irrelevant for the large-scale features. To get these gross features, one should most often use a more phenomenological and aggregated description, aimed specifically at the higher level. 

Thus, financial markets should not be modeled by simple geometric Brownian motion based models, all of which form the basis for modern treatments of derivative markets. These models were created to be analytically tractable and derive from very crude phenomenological modeling. They cannot reproduce the observed strongly non-Gaussian probability distributions in many markets, which exhibit a feature so generic that it even has a whimsical name, fat tails. Instead, the modeling should be driven by asking what are the simplest non-linearities or non-localities that should be present, trying to separate universal scaling features from market specific features. The inclusion of too many processes and parameters will obscure the desired qualitative understanding."

This paragraph captures as well as anything else why chemistry requires its own language, rules and analytical devices for understanding its details and why biology and psychology require their own similar implements. Not everything can be understood through quantum mechanics, because as you try to get more and more fundamental, true understanding might simply slip away from between your fingers.

RIP, Leo Kadanoff.

Agonists and antagonists, and why drug discovery is hard (again)

Here's a valuable and comprehensive review on one of the most glaring pieces of evidence for why drug discovery is so hard - the fact that very small structural changes in molecules can lead to drastic changes in their biological activity.

I particularly like this review because it's absolutely chock-full of examples of small structural changes which not only impact the magnitude of binding of a small molecule to a receptor protein but invert it - that is, change an agonist into an antagonist. And the receptor family in this case is GPCRs, so it's not like we're talking about a minor rash of examples in a scientifically insignificant and financially paltry domain.

Here's one of my favorite examples from the dozens showcased in the piece; in this case a set of small molecules targeting the nociceptin receptor which is being studied as a potential target in treating heart failure and depression.

At first sight it's compelling how such similar groups as a cyclooctyl, a cyclooctyl-methyl and a phenyl can lead to complete inversion of activity, from 200 nM agonism to 1.5 nM antagonism. Thinking in 3D however makes the observation a bit more comprehensible. The N-cyclooctyl on the right is going to have a very well-defined conformational preference - pointing pretty much straight in one direction. The cyclooctyl-methyl on the other hand is going to have much more conformational freedom. It's also going to occupy much more space than the phenyl group on the right.

Now this kind of retrospective analysis may well be the explanation, but very few medicinal chemists would have been able to predictive this complete inversion in activity at the outset (as a medicinal chemist recently quipped at a Gordon Conference, "We medicinal chemists are very good at predicting the past.")

Here's a more diabolical example that would have been even harder to predict. In this case the target concerns two suptypes of the mGlu (metabotropic glutamate) receptor which is involved among other things in Parkinson's and anxiety.

In this case, not only does that 'magic' methyl group and its precise stereochemistry change an antagonist into an agonist but it even changes the agonism/antagonism mix at two separate receptors. Try explaining that, even in retrospect.

These kinds of well-known activity cliffs reinforce the essentially non-linear nature of medicinal chemistry, a quality that is essentially emergent since it arises from the interaction of small molecules with a highly non-linear biological system. Neither experimental chemistry nor computational modeling would allow us to predict activity cliffs like these because of the lack of sensitivity in such techniques.

It's things like these which I always think really need to be communicated to laymen to impress the staggering difficulty of drug design to them - most of the times we are simply ignorant when it comes to designing molecules like the ones above with any kind of predictive power and we can only find out about their fickle properties in retrospect. Perhaps then we will get less heat from the public for why we sometimes have to spend (and charge) so much money for our products.

Edward Witten, chemistry and the problems with falsification

Science writer and journalist John Horgan who wrote the notorious and thought-provoking book "The End of Science" in the 90s has an interesting interview with theoretical physics giant Edward Witten. Witten, who won the Fields Medal back in the 80s, is widely regarded because of his huge status and influence as one of the main reasons a number of physicists switched to doing string theory in the 90s.

The whole interview is worth reading but there was one chemistry-related response that Witten gave which especially caught my eye:

Horgan: Do you agree with Sean Carroll that falsifiability is overrated as a criterion for distinguishing science from pseudo-science? 

Witten: Scientists aim to get as reliable and precise an understanding of nature as we can.  The gold standard is a precise prediction that can be tested in a precise way in a laboratory experiment.  Experiments that disprove theories are an important part of the scientific process. 

With that said, it is a little too narrow to claim that science consists of trying to falsify theories because a lot of science consists of trying to discover things. (Chemists who attempt a new synthesis could say they are trying to falsify the hypothesis that this new synthesis won’t work.  But that isn’t what they usually say.  People who search for life on Mars could say they are trying to falsify the hypothesis that there is no life on Mars. Again, people don’t usually talk that way.)

I have to give credit to Witten for pointing out the problems with falsifiability in the context of chemistry; in my experience there's not even too many chemists who talk about this rather central aspect of the science. The most prominent one who does is Roald Hoffmann who in his recent collection of essays (which I reviewed for Nature Chemistry) does analyze the issues with using falsifiability as a significant criterion in the philosophy of chemistry. Hoffmann also points to other well-known constructs from the philosophy of science, such as hypothesis testing, as being insufficient guides in understanding the methodology of chemistry.

As both Hoffmann and Witten point out, most chemists when they are trying to make molecules are not really trying to falsify anything, except in the trivial and general sense of trying to "falsify" the basic principles of chemistry. But this fact also goes to the heart of chemistry as a science and art that is more akin to architecture and which really tries to build things rather than simply breaking them down. That is also the reason why, in my opinion, the whole reductionist paradigm has some clear limitations when applied to chemistry; most chemists are really trying to see how emergent molecular properties arise from putting atoms together in different arrangements. Breaking things down in the form of elemental or spectroscopic analysis is of course a reductionist activity that's an essential part of chemistry, but even that process really is in service to understanding how atoms can combine together to give rise to novel structure and function.

It's good to have even physicists like Witten point this out. But it's also something that more chemists and chemistry popularizers should be aware of, especially when they describe their philosophy of doing science to the wider world. Explicating the unique philosophy of chemistry not only sheds light on why chemistry is different in its own way but also demonstrates why we must not hold exalted notions from the philosophy of science like falsification sacrosanct.

The Higgs boson and the future of science

My latest post on the Scientific American blog network ties together several threads about reductionism, emergence and the nature of scientific problems which I have explored on this blog.

Philip Anderson: Anderson first described the so-called Higgs mechanism and also fired the first modern salvo against strong reductionism (Image: Celeblist)

The discovery of the Higgs boson (or the "Higgs-like particle" if you prefer) is without a doubt one of the signal scientific achievements of our time. It illustrates what sheer thought - aided by data of course - can reveal about the workings of the universe and it continues a trend that lists Descartes, Hume, Galileo and Newton among its illustrious forebears. From sliding objects down an incline to smashing atoms at almost the speed of light in a 27 kilometer tunnel, we have come a long way. Dissecting our origins and the universe around us scarcely gets any better than this.

Yet even as the exciting discovery was being announced, I could not help but think about what the Higgs does not do for us. It does not speed up the time needed to discover a new cancer drug. It does not help us understand consciousness. It does not tell us how life began or whether it exists elsewhere in the universe. It does not explain romantic love, how to design the best solar cell, why people have certain political preferences and how exactly to predict the effects of climate change. In fact we can safely predict that the discovery of the Higgs boson, as consciousness-elevating as it is, does not impact the daily work of 99% of all pure and applied scientists in the world.

I do not say all this to downplay the discovery of the particle which is an unparalleled triumph of human thought, hard work and experimental ingenuity. I also do not say this to make the obvious point that a discovery in one field of science does not automatically solve problems in other fields. Rather, I say this to probe the deeper reality beyond that point, to highlight the multifaceted nature of science and the sheer diversity of problems and phenomena that it presents to us at every level of inquiry. And I say this with a suspicion that the Higgs boson may be the most fitting tribute to the limitations of what has been the most potent philosophical instrument of scientific discovery - reductionism.

In one sense the discovery of this fundamental component of matter can be seen as the culmination of reductionist thinking, accounting as it does for the very existence of mass. Reductionism is the great legacy of the twentieth century, a philosophy whose seeds were sown when Greek philosophers started mulling the nature of matter. The method is in fact quite intuitive; ever since they stepped down from the trees, human beings have tried to solve problems by breaking them down into simpler parts. In the twentieth century the fruits of reductionism have been nothing short of awe-inspiring. Reductionism is what told us that molecules are made of atoms, that the universe is expanding, that DNA is a double helix and that you can build lasers and computers. The reductionist ethic has given us quantum mechanics, relativity, quantum chemistry and molecular biology. Over the centuries it has been used by its countless practitioners as a fine scalpel which has laid bare the secrets of nature. In fact many of the questions answered using the reductionist method were construed as being amenable to this method even before their answers were provided; for instance, how do atoms combine to form molecules? What is the basic nature of the gene? What are atoms themselves made up of?

Yet as we enter the second decade of the twenty-first century, it is clear that reductionism as a principal weapon in our arsenal of discovery tools is no longer sufficient. Consider some of the most important questions facing modern science, almost all of which deal with complex, multifactorial systems. How did life on earth begin? How does biological matter evolve consciousness? What are dark matter and dark energy? How do societies cooperate to solve their most pressing problems? What are the properties of the global climate system? It is interesting to note at least one common feature among many of these problems; they result from the buildup rather than the breakdown of their operational entities. Their signature is collective emergence, the creation of attributes which are greater than the sum of their constituent parts. Whatever consciousness is for instance, it is definitely a result of neurons acting together in ways that are not obvious from their individual structures. Similarly, the origin of life can be traced back to molecular entities undergoing self-assembly and then replication and metabolism, a process that supersedes the chemical behavior of the isolated components. The puzzle of dark matter and dark energy also have as their salient feature the behavior of matter at large length and time scales. Studying cooperation in societies essentially involves studying group dynamics and evolutionary conflict. The key processes that operate in the existence of all these problems seem to almost intuitively involve the opposite of reduction; they all result from the agglomeration of molecules, matter, cells, bodies and human beings across a hierarchy of unique levels. In addition, and this is key, they involve the manifestation of unique principles emerging at every level that cannot be merely reduced to those at the underlying level.

The traditional picture of science asserts that X can be reduced to Y. Reality is more complicated (Image: P. W. Anderson, Science, 1972)

A classic example of emergence: The exact shape of a termite mound is not reducible to the actions of individual termites (Image: Wikipedia Commons)

This kind of emergence has long since been seen as key to the continued unraveling of scientific mysteries. While emergence had been implicitly appreciated by scientists for a long time, its modern salvo was undoubtedly a 1972 paper in Science by the Nobel Prize winning physicist Philip Anderson titled "More is Different", a title that has turned into a kind of clarion call for emergence enthusiasts. In his paper Anderson (who incidentally first came up with the so-called Higgs mechanism) argued that emergence was nothing exotic; for instance, a lump of salt has properties very different from those of its highly reactive components sodium and chlorine. A lump of gold evidences properties like color that don't exist at the level of individual atoms. Anderson also appealed to the process of broken symmetry, invoked in all kinds of fundamental events - including the existence of the Higgs boson - as being instrumental for emergence. Since then, emergent phenomena have been invoked in hundreds of diverse cases, ranging from the construction of termite hills to the flight of birds. The development of chaos theory beginning in the 60s further illustrated how very simple systems could give rise to very complicated and counterintuitive patterns and behavior that are not obvious from the identities of the individual components.

Many scientists and philosophers have contributed to considered critiques of reductionism and an appreciation of emergence since Anderson wrote his paper. These thinkers make the point that not only does reductionism fail in practice (because of the sheer complexity of the systems it purports to explain), but it also fails in principle on a deeper level. In his book "The Fabric of Reality" for instance, the Oxford physicist David Deutsch has made the compelling point that reductionism can never explain purpose; to drive home this point he asks us if it can account for the existence of a particular atom of copper on the tip of the nose of a statue of Winston Churchill in London. Deutsch's answer is a clear no, since the fate of that atom was based on contingent, emergent phenomena, including war, leadership and adulation. Nothing about the structure of copper atoms allows us to directly predict that a particular atom will someday end up on the tip of that nose. Chance plays an outsized role in these developments and reductionism offers us little solace to understand such historical accidents.

Complexity theorist Stuart Kauffman who has written about the role of contingency as a powerful argument against strong reductionism (Image: Wikipedia Commons)

An even more forceful proponent of this contingency-based critique of reductionism is the complexity theorist Stuart Kauffman (supposedly an inspiration for the Jeff Goldblum character in "Jurassic Park") who has laid out his thoughts in two books. Just like Anderson, Kauffman does not deny the great value of reductionism in illuminating our world, but he also points out the factors that greatly limit its application. One of his favorite examples is the role of contingency in evolution and the object of his attention is the mammalian heart. Kauffman makes the case that no amount of reductionist analysis could explain tell you that the main function of the heart is to pump blood. Even in the unlikely case that you could predict the structure of hearts and the bodies that house them starting from the Higgs boson, such a deductive process could never tell you that of all the possible functions of the heart, the most important one is to pump blood. This is because the blood-pumping action of the heart is as much a result of historical contingency and the countless chance events that led to the evolution of the biosphere as it is of its bottom-up construction from atoms, molecules, cells and tissues. As another example, consider the alpha amino acids which make up all proteins on earth. These amino acids come in two potential varieties, left-handed and right-handed. With very few exceptions, all the functional amino acids that we know of are left handed, but there's no reason to think that right handed amino acids wouldn't have served life equally well. The question then is, why left-handed amino acids? Again, reductionism is silent on this question mainly because the original use of left-handed amino acids during the origin of life was to the best of our knowledge a matter of contingency. Now some form of reductionism may still explain the subsequent propagation of left-handed amino acids and their dominance in biological processes by resorting to molecular level arguments regarding chemical bonding and energetics, but this description will still leave the origins issue unresolved. Even something as fundamental as the structure and function of DNA - which by all accounts was a triumph of reductionism - is much better explained by principles of chemistry like electrostatic attraction and hydrogen bonding.

Life as we know it is based on left-handed amino acids. But there is no reason why right-handed amino acids could not sustain life (Image: Islamickorner)

Reductionism then falls woefully short when trying to explain two things; origins and purpose. And one can see that if it has problems even when dealing with left-handed amino acids and human hearts, it would be in much more dire straits when attempting to account for say kin selection or geopolitical conflict. The fact is that each of these phenomena are better explained by fundamental principles operating at their own levels. Chemistry has its covalent bonds and steric effects, geology has its weathering and tectonic shifts, neurology has its memory potentiation and plasticity and sociology has its conflict theory. And as far as we can tell, these sciences will continue to progress without needing the help of Higgs bosons and neutrinos. This also seems to make it unlikely that the discovery of a single elegant equation linking the four fundamental forces (the purported "theory of everything"), while undoubtedly representing one of the greatest intellectual achievements of humanity, will give sociologists and economists little pause for thought, even as they continue to study the stock market and democracies using their own special toolkit of bedrock principles.

This rather gloomy view of reductionism may sound like science is at a dead end or at the very least has started collapsing under the weight of its own success. But such a view would be as misplaced as announcements about the "end of science" which have surfaced every couple of years for the last two hundred years. Every time the end of science has been announced, science itself proved that claims of its demise were vastly exaggerated. Firstly, reductionism will always be alive and kicking since the general approach of studying anything by breaking it down into its constituents will continue to be enormously fruitful. But more importantly, it's not so much the end of reductionism as the beginning of a more general paradigm that combines reductionism with new ways of thinking. The limitations of reductionism should be seen as a cause not for despair but for celebration since it means that we are now entering new, uncharted territory. There are still an untold number of deep mysteries that science has to solve, ranging from dark energy, consciousness and the origin of life to more supposedly pedestrian concerns like superconductivity, cancer drug discovery and the behavior of glasses. Many of these questions require interdisciplinary approaches which result in the crafting of fundamental principles that are unique to the problem statement. Such a meld will inherently involve reductionism only as one component.

Now there are some who may not consider these problems as "fundamental" enough but that is because they would be peering through the lens of traditional twentieth century science. One of the sad casualties of the reductionist undertaking is a small group of people who think that cosmology and particle physics constitute the only things truly worth doing and the epitome of fundamental science; the rest is all detail that can be filled in by second-rate minds. This is in spite of the inconvenient fact that perhaps 80% of physicists are not concerned at all with fundamental questions. But you would be deluding yourself if you are thinking that turbulence in fluids is a second-rate problem (still unsolved) for second-rate minds, especially if you remember that Heisenberg thought that God would will be able to provide an explanation for quantum mechanics but not for turbulence. The fact is that "pedestrian" concerns like superconductivity have engaged some of the best minds of the last fifty years without fully succumbing to them, and at their own levels they are as hard as the discovery of the Higgs boson or the accelerating universe. Exploring these worthy conundrums is every bit as exciting, deep and satisfying as any other endeavor in science. Those who are wondering what's next should not worry; a sparkling journey lies ahead.

To guide us on this journey all we have to remember are the words of one of the twentieth century's great reductionists and one of Peter Higgs's heroes. Paul Dirac closed his famous text on quantum theory with stirrings that will hopefully be as great a portent for the emergent twenty-first century as they were for the reductionist twentieth: "Some new principles are here needed".

References:
1. P. W. Anderson, More is Different, Science, 1972, 177, 393
2. David Deutsch, "The Fabric of Reality", 2004
3. Stuart Kauffman, "Reinventing the Sacred", 2009; "At Home in the Universe", 1996
Other reading:
1. Terrence Deacon, "Incomplete Nature", 2011
2. John Horgan, "The End of Science", 1997
3. Robert Laughlin, "A Different Universe", 2006

The man who made it possible

There's a nice set of articles in this week's Nature celebrating the birth centenary and work of a man whose work underlies almost all of modern life - Alan Turing. Considering the complete transformation of human life that computing has enabled, Turing's work along with that of his fellow computer pioneer John von Neumann will likely be recognized as one of those watersheds in human history, comparable to the invention of the calculus and the discovery of electricity. It is remarkable to consider how many millions of software engineers and billions of dollars in revenue are being generated every day off these scientists' ideas.

The historian and writer George Dyson starts off by documenting Turing and von Neumann's basic achievements. Dyson has a book out next week on this very topic, and given his past writings on technology and computing I am looking forward to it. As Dyson tells us, the basic groundbreaking idea of Turing and von Neumann was not just a machine which performs calculations at superhuman speed. It was the concept of a shared program and the then startling idea that you could code both the data and the instructions for it in the same language (binary) in the same machine. This idea really is one of those "big ideas", simple to state but absolutely revolutionary in its impact. Turing and von Neumann's greatness thus lies not in conceiving the physical manifestation (or 'instantiation' as programmers would say) of a computing recipe but in their abstract generalization of the very idea of a programmed computer.

What is not always recognized is that Von Neumann went a step ahead and floated an even more remarkable notion, that of a machine which contains instructions for assembling copies of itself. Von Neumann immediately tied this to biology; but this was a few years before Watson and Crick discovered the specific mechanism in the form of DNA base pairing. Unlike the "dynamic duo", nobody remembers von Neumann as making a signal conceptual contribution to biology. I remember the writer John Casti lamenting the general public's lack of recognition of von Neumann as the man who first really thought of the mechanism of heredity on the general basis that mathematicians are used to. As Casti pithily put it in his wonderful book 'Paradigms Lost': "Such are the fruits of the theoretician, especially one who solves 'only' the general case". To be fair, biology is an experimental science and no amount of theorizing can nail an experimental fact, but I suspect mathematicians would widely commiserate with Casti's lament.

The biologist Sydney Brenner then follows up by recognizing Turing's contributions to biology. In 1952 he wrote what is considered the first paper on nonlinear dynamics in which he described pattern formation in chemical reactions and possibly in developmental biology. We are still trying to understand the implications of that discovery even as nonlinear dynamics itself has become an enormously fruitful field with deep applications in modeling almost any complex natural phenomenon.

Finally, mathematician Barry Cooper from the University of Leeds points out a tantalizing question stemming from Turing's work; are complex, emergent phenomena strictly computable? This is partly a question about the limits of 'strong' reductionism and one that I have explored on this blog often. We don't yet know the answer to this question, to whether we can compute higher-order phenomena starting from a few simple particles and fields bequeathed to us by the particle physicists. As we tackle problems as complex as the future of the cosmos and the source of consciousness in the twenty-first century, this question will continue to hound scientists and philosophers. It waits for an answer as profound as Turing's answer to Hilbert's famous Entscheidungsproblem.