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HOME > PAST ISSUE > July-August 2011 > Article Detail

MARGINALIA

The Humpty-Dumpty Problem

Even when we understand their parts, living things are hard to put back together

Robert L. Dorit

Too Many Pieces

2011-07MargDoritFB.jpgClick to Enlarge ImageImplicit in both the strategy of reductionism and the metaphor of the machine, however, is a hidden assumption. That assumption is that an accurate understanding of the parts will reveal the workings of the machine in their entirety. To state it differently, we do not need to be overly concerned with how we take the machine apart, because the parts themselves will dictate the reassembly. But is this assumption true?

Anyone who has tried to repair a piece of lab equipment, a washing machine or even an old-school carburetor knows the dangers of taking something apart without paying careful attention to the process. Making a couple of sketches or taking a few photographs during disassembly certainly makes life easier later on, when the goal is reconstruction. A little thought and a few disastrous reassemblies make it easy to dismiss the idea that the parts harbor the information needed to reassemble the whole. Yet the truth is complicated. I suspect that the reductionist strategy, in its purest form, does work when the number of component parts is comparatively small and their relations are limited and predictable. If a given gear can only mesh with a small number of other components, and a given spring fits only on a single stem, the parts do encode the whole. This is why a good clock maker could probably put a clock back together—even one she has never seen before—if all of the parts were laid out in front of her.

But living systems are not really clocklike in their assembly, and organisms are not really machines. Despite Descartes’ contention that we could not distinguish a well-made automaton of an ape from an actual ape (“were there such machines exactly resembling organs and outward form an ape or any other irrational animal, we could have no means of knowing that they were in any respect of a different nature from these animals”), the relations of parts to wholes in living systems is entirely different from that in machines—and most unclocklike.

If anything, living systems consistently violate all of the criteria for reducibility. The number of elements that compose any living system—an ecosystem, an organism, an organ or a cell—is enormous. In living systems, the specific identities of these component parts matter. Unlike chemistry, for instance, in which an electron in a lithium atom is identical to an electron in a gold atom, all proteins in a cell are not equivalent or interchangeable. Each protein is the result of its own evolutionary trajectory. We understand and exploit their similarities, but their differences matter to us just as much. Perhaps most importantly, the relations between the components of living systems are complex, context-dependent and weak. In mechanical machines, the conversation taking place between the parts involves clear and unambiguous interactions. These interactions result in simple causes and effects: They are instructions barked down a simple chain of command.

In living systems, by contrast, virtually every interesting bit of biological machinery is embedded in a very large web of weak interactions. And this network of interactions gives rise to a discussion among the parts that is less like a chain of command and more like a complex court intrigue: ambiguous whispers against a noisy and distracting background. As a result, the same interaction between a regulatory protein and a segment of DNA can lead to different (and sometimes opposite) outcomes depending on which other proteins are present in the vicinity. The firing of a neuron can act to amplify the signal coming from other neurons or act instead to suppress it, based solely on the network in which the neuron is embedded. The disappearance of a single species can stabilize an ecosystem or send it spinning into chaos, depending on (you guessed it) the network of interactions that surrounds that species. This extensive and subtle connectivity, which gives meaning to the behavior of the underlying components, turns out to be a consistent feature of living systems.

The recurrent evolution of these networks of weak interactions suggests that they may allow biological systems to incorporate information from the environment while also maintaining stability in the face of constant perturbation. This general feature of living systems also has clear methodological consequences for modern biology. Once this gossamer web is taken apart in search of the smallest components we can study, the process of putting it back together bears no resemblance at all to reconstructing a clock. Thus we find ourselves, early in the 21st century, with extraordinarily detailed descriptions of the components of many biological systems. But reconstructing those systems is proving to be a monumental and consistently surprising enterprise.

The promise of reductionism rested on the belief that an intelligent dissection of complex phenomena would not only yield progress, but would eventually reduce any problem to its component parts. Complexity, we naively hoped, was simply a by-product of incomplete understanding, an illusion that would fall away once the parts were fully understood. But this is the dirty little secret of contemporary biology: Despite our reductionist successes, the central conceptual problems of biology have not yielded to study. We have revealed the elegant workings of neurons in exquisite detail, but the material understanding of consciousness remains elusive. We have sequenced human genomes in their entirety, but the process that leads from a genome to an organism is still poorly understood. We have captured the intricacies of photosynthesis, and yet the consequences of rising carbon-dioxide levels for the future of the rain forests remain frustratingly hazy. We are, in short, the king’s horses and the king’s men: We stare at the pieces, knowing what Humpty should look like, but unable to put him together again.




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