Complexity_ A Guided Tour - Melanie Mitchell [138]
Next came a big leap in Kauffman’s interpretation of this model. Every cell in the body has more or less identical DNA. However, the body has different types of cells: skin cells, liver cells, and so forth. Kauffman asserted that what determines a particular cell type is the pattern of gene expression in the cell over time—I have described above how gene expression patterns can be quite different in different cells. In the RBN model, an attractor, as defined above, is a pattern over time of “gene expression.” Thus Kauffman proposed that an attractor in his network represents a cell type in an organism’s body. Kauffman’s model thus predicted that for an organism with 100,000 genes, the number of different cell types would be approximately the square root of 100,000, or 316. This is not too far from the actual number of cell types identified in humans—somewhere around 256.
At the time Kauffman was doing these calculations, it was generally believed that the human genome contained about 100,000 genes (since the human body uses about 100,000 types of proteins). Kauffman was thrilled that his model had come close to correctly predicting the number of cell types in humans. Now we know that the human genome contains only about 25,000 genes, so Kauffman’s model would predict about 158 cell types.
THE ORIGIN OF ORDER
The model wasn’t perfect, but Kauffman believed it illustrated his most important general point about living systems: that natural selection is in principle not necessary to create a complex creature. Many RBNs with K = 2 exhibited what he termed “complex” behavior, and no natural selection or evolutionary algorithm was involved. His view was that once a network structure becomes sufficiently complex—that is, has a large number of nodes controlling other nodes—complex and “self-organized” behavior will emerge. He says,
Most biologists, heritors of the Darwinian tradition, suppose that the order of ontogeny is due to the grinding away of a molecular Rube Goldberg machine, slapped together piece by piece by evolution. I present a countering thesis: most of the beautiful order seen in ontogeny is spontaneous, a natural expression of the stunning self-organization that abounds in very complex regulatory networks. We appear to have been profoundly wrong. Order, vast and generative, arises naturally.
Kauffman was deeply influenced by the framework of statistical mechanics, which I described in chapter 3. Recall that statistical mechanics explains how properties such as temperature arise from the statistics of huge numbers of molecules. That is, one can predict the behavior of a system’s temperature without having to follow the Newtonian trajectory of every molecule. Kauffman similarly proposed that he had found a statistical mechanics law governing the emergence of complexity from huge numbers of interconnected, mutually regulating components. He termed this law a “candidate fourth law of thermodynamics.” Just as the second law states that the universe has an innate tendency toward increasing entropy, Kauffman’s “fourth law” proposes that life has an innate tendency to become more complex, which is independent of any tendency of natural selection. This idea is discussed at length in Kauffman’s book, The Origins of Order. In Kauffman’s view, the evolution of complex organisms is due in part to this self-organization and in part to natural selection, and perhaps self-organization is really what predominates, severely limiting the possibilities for selection to act on.
Reactions to Kauffman’s Work
Given that Kauffman’s work implies “a fundamental reinterpretation of the place of selection in evolutionary theory,” you can imagine that people react rather strongly to it. There are a lot of huge fans of this work (“His approach opens up new vistas”; it is “the first serious attempt to model a complete biology”). On the other