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“Complexity Brake” Defies Evolution

The complexity of life is daunting to describe, let alone evolve. The number of parts and their interactions makes biological complexity very different from technology. The only hope of understanding it is to look for modular, hierarchical structures that have, so far, eluded evolutionary science.
In a recent Perspective piece called “Modular Biological Complexity” in Science, Christof Koch (Allen Institute for Brain Science, Seattle; Division of Biology, Caltech) explained why we won’t be simulating brains on computers any time soon:

Although such predictions excite the imagination, they are not based on a sound assessment of the complexity of living systems. Such systems are characterized by large numbers of highly heterogeneous components, be they genes, proteins, or cells. These components interact causally in myriad ways across a very large spectrum of space-time, from nanometers to meters and from microseconds to years. A complete understanding of these systems demands that a large fraction of these interactions be experimentally or computationally probed. This is very difficult.

Physicists can use statistics to describe a homogeneous system like an ideal gas, because one can assume all the member particles interact the same. Not so with life. When describing heterogeneous systems each with a myriad of possible interactions, the number of discrete interactions grows faster than exponentially. Koch showed how Bell’s number (the number of ways a system can be partitioned) requires a comparable number of measurements to exhaustively describe a system. Even if human computational ability were to rise exponentially into the future (somewhat like Moore’s law for computers), there is no hope for describing the human “interactome” — the set of all interactions in life.

This is bad news. Consider a neuronal synapse — the presynaptic terminal has an estimated 1000 distinct proteins. Fully analyzing their possible interactions would take about 2000 years. Or consider the task of fully characterizing the visual cortex of the mouse — about 2 million neurons. Under the extreme assumption that the neurons in these systems can all interact with each other, analyzing the various combinations will take about 10 million years…, even though it is assumed that the underlying technology speeds up by an order of magnitude each year.

Even with shortcuts like averaging, “any possible technological advance is overwhelmed by the relentless growth of interactions among all components of the system,” Koch said. “It is not feasible to understand evolved organisms by exhaustively cataloging all interactions in a comprehensive, bottom-up manner.” He described the concept of the Complexity Brake:

Allen and Greaves recently introduced the metaphor of a “complexity brake” for the observation that fields as diverse as neuroscience and cancer biology have proven resistant to facile predictions about imminent practical applications. Improved technologies for observing and probing biological systems has only led to discoveries of further levels of complexity that need to be dealt with. This process has not yet run its course. We are far away from understanding cell biology, genomes, or brains, and turning this understanding into practical knowledge.

Why can’t we use the same principles that describe technological systems? Koch explained that in an airplane or computer, the parts are “purposefully built in such a manner to limit the interactions among the parts to a small number.” The limited interactome of human-designed systems avoids the complexity brake. “None of this is true for nervous systems.”
Having set up the bad news, he offered possible good news: find modular, hierarchical structures in the biological interactome. “If groups of components behave as a single module, the effective number of interactions that need to be analyzed would drop precipitously,” he said, giving biologists a possible foot off the Complexity Brake in the future. Have such structures been found? Are we close to finding them? Is there any hope for putting the foot on the gas instead of the brake?

The discovery of modular, hierarchical structures that capture the behavior of complex systems in a causal manner is essential to speed up solving these challenging problems. This emphasizes the need to develop heuristic methods to discover modules. For if appropriate modules cannot be found, understanding of life will escape us.

Let’s take stock of what he said from an ID perspective. Koch has essentially written off the positivists who think we are near to recreating life in a test tube from scratch, duplicating a human brain with computers, or telling the public “now we know” how life works. Forget it; the Complexity Brake guarantees that human minds will never be able to exhaustively describe biological complexity. The only hope for speeding around the Complexity Brake is to discover modules that cut down the interactome significantly. Even then, understanding could take decades, centuries, millennia.
Well, then, has Koch told us how evolution produced such complexity? On evolution, he had only this to say (quoted above): “It is not feasible to understand evolved organisms by exhaustively cataloging all interactions in a comprehensive, bottom-up manner.” In other words, Koch merely assumes that evolution produced the complexity, but then tells us it is impossible to work out that complexity from the bottom up, unless hierarchical modules are discovered.
This leaves evolutionists in a hopeless bind. On the one hand, they cannot possibly describe life from the bottom up, even though they believe that’s how it evolved. This begs the question of whether it evolved, in Darwinian fashion, to begin with. On the other hand, evolutionists would need to discover modular, hierarchical structures to have any hope of understanding life. A moment’s reflection brings us to the recognition that modular, hierarchical structures defy neo-Darwinian explanation. Neo-Darwinism (random mutation and natural selection) can only concern itself with the immediate fitness benefits of single mutations, not hierarchies or modules.
Even a Hox gene mutation or a whole-genome duplication won’t get evolutionists past the Complexity Brake. For all practical purposes, gene duplications and developmental mutations can be treated as point mutations. Only if a set of mutations evolves into a module, where all the different members of the module share in the fitness benefit, may the system become amenable to a neo-Darwinian analysis. A Hox mutation might result in another body segment or substitution of antennae for eyes, but that’s not what Koch has in mind. He told us what he meant: “The discovery of modular, hierarchical structures that capture the behavior of complex systems in a causal manner is essential to speed up solving these challenging problems.” The extra pair of legs is a result of an upstream switch; it does not capture the behavior of systems “in a causal manner” to allow scientists to explain it (or evolve it).
The only modular, hierarchical systems we are aware of from uniform experience arise via intelligent design. With computers, for instance, we can describe the modular hierarchy from the bottom up: a microchip at the bottom level, circuit boards at the next level, a minicomputer at the next level, a local area network (LAN) at the next level, a wide area network (WAN) at the next level, the Internet at the top level. Similar hierarchies may be described in terms of software. At each level, components interact on purpose according to the hierarchical rules, but can be described in terms of their individual components at the bottom level. (One also notices that mutations at any level in computer systems do not cause improvement.)
Living systems also follow modular, hierarchical arrangements at the macro level: a cell can belong to a tissue, that belongs to an organ, that belongs to a system (like the nervous system), that is a body part of an individual, who is a member of a population, that is a member of an ecosystem. A species can be described as a member of a genus, a family, an order, a class, a phylum, and a kingdom.
At the most basic level that concerned Koch, though — the interactome in the cell — all hope of “bottom-up” explanation eludes the neo-Darwinist. By uniform experience, therefore, the inference to the best explanation for the modular hierarchical complexity in life is intelligent design.