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After earning the ire of computational biologists and network theorists last week, it's time to get to the positive side of networks and systems biology. If you hadn't guessed it before, the name of this blog reflects my interest in complex biological systems. When I rant about networks and comp. bio., it's tough love, and I really have the best interests of the field at heart.More Adaptive Complexity articles
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Here's something that ought to put my take on the field in context. It's from an interview with Stanford Economist Brian Arthur and his early direction of the economics research program at the complex systems-oriented Santa Fe Instutite:
[Arthur] made two key decisions early on, he says. The first had to do with topics. He was distinctly unenthused by the idea of applying chaos theory and nonlinear dynamics to economics, which seemed to be a big part of what [Nobel-winning economist Ken] Arrow had in mind. There were plenty of other groups doing that kind of thing already - and with very few worthwhile results, so far as he could tell. Nor was Arthur interested in having the program build some huge economic simulation of the whole world. "This may have been in [Citibank big shot and Satana Fe Institute benefactor] John Reed's mind," he says, "and it seems to be the first thing engineers or physicists want to do. But it's as if I said to you, 'You're an astrophysicist, why don't you build a model of the universe?' " Such a model would be just about as hard to understand as the real universe, he says, which is why astrophysicists don't do it that way. Instead, they have one set of models for quasars, another set for spiral galaxies, another set for star formation, and so on. They go in with a computational scalpel to dissect specific phenomena.
And that's exactly what Arthur wanted to do in the Sante Fe program... He also wanted people to learn how to walk before they tried to run. In particular, he wanted to see the program take some of the classical problems in economics, the hoary old chestnuts in the field, and see how they changed when you loked at them in terms of adaptation, evolution, learning, multiple equilibria, emergence, and complexity - all the Santa Fe themes...
That emphasis on the old chestnuts got the program in hot water later, says Arthur, when a number of people on the institute's science board accused them of being insufficiently innovative. "But we thought it was just good science, good politics, and good procedure to approach the standard problems," he says. "These are problems that economists recognize. Above all, if we could prove that changing the theoretical assumptions to be more realistic made major differences to the insights you got, maybe getting a feeling of more realism in those insights, then we could show the field that we had really contributed something."
From Complexity, Mitchell Waldrop, p. 244-245
Complex systems researchers need to follow this advice. The availability of petabytes of data and cheap computational power doesn't mean it's a wise idea to build huge models of everything, like say, the entire transcription network of a cell. Traditional biologists may not be that computer-savvy, but they have been very good at defining important biological questions. Most molecular biologists I know would love to understand, on a quantitative level, why a MAP kinase pathway or transcriptional cascade functions the way it does. They're sympathetic to systems-level questions, as long as those are questions with the promise to make an impact on how all biologists, computational and wet-lab, think about about transcription or signal transdusction.
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Love the blog. I wish you'd take some time to speak a little more on your own particular view of complexity, complex systems, networks, and complexity theory. The field is still so new that the terminology itself is often fuzzy and overlapping. I've read books that placed network science as a sub-field of complexity theory (Mark Buchanan) and books that claimed complexity theory was a sub-discipline of network theory (Barabasi). I've heard scientists who casually proclaimed that complexity theory was nothing more than an extension of chaos theory and that ultimately they were the same thing (Steven L. Goldman ) and scientists claiming that chaos and complexity are complementary, yet polar, opposites (seemingly everyone else). Likewise I've often heard nonlinear dynamics described as a field within complexity science, and then I've read sources claiming that complex systems research and complexity theory should be classified beneath the rubric of "nonlinear science" along with chaos theory, fractal geometry, the study of solitons, cellular automata, and what have you.
So, what I'm most curious to see (well, read) in this blog is what do *you* think? Where do you fall on these most basic and fundamental questions? The most confusing thing for those of us trying to learn about and desiring to perhaps get into the field ourselves some day is that complexity theory (or science, if you prefer), chaos theory, nonlinear dynamics, network theory, and general systems theory are all discreet and definable in specific terms dealing with specific topics and ideas--but they all nonetheless share common subject matter, similar perspectives and methodological tools, and often overlap one another like the Venn diagram from Hell. They all relate to one another, but the experts seem divided on just how they do so.
Perhaps you could write your own little primer to the field--the larger subject of complexity and complex systems, that is, as well as how it relates to and unites disciplines such as sociology and economics in addition to your own field of biology. How is the aspiring student of complexity to navigate the the sometimes messy terminology of an intellectual arena that despite being several decades in the making still feels young and is evolving with every year that passes in surprising ways?