Network Thinking in Biology

A recent post on Pharyngula about current debates on the evolution of gene regulatory networks in embryo development motivated me write down some thoughts about the current status of network thinking in biology. Networks are one of the latest hot items in molecular biology, but at this point, in my opinion, their significance to our current understanding is hugely overrated. More and more papers are showing up with 'network' in their titles, but most of the time, the word is uninformative about the content in the article, or just a fancy way of renaming what biologists have been doing for years - mapping the interactions between proteins, DNA, and small molecules.

What has network thinking actually done for biology? Very little except generate confusion and bad paper titles. At most, the idea of a network is used as a helpful metaphor. Metaphors can be useful in science, but if networks are to really make a big impact on our understanding of molecular biology, we need to move beyond vague metaphors and into a rigorous set of concepts that actually make a difference in how we think about biological problems.

Here's one way of looking at the situation: A recent review article by Alberto Barabasi begins by succinctly summarizing what nearly all molecular biologists would agree is a major goal:

"A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells."

For at least one model organism, brewer's yeast, we're getting pretty damn close to coming up with a complete inventory of the identity, interactions, and functions of all the proteins in a cell. We know in great detail what goes on inside a yeast cell, and within 10 years, there won't be much left to discover in terms of the basic molecular biology that people have been working out for 50 years. Genomics has really accelerated this process. So the question arises, how do we make sense of all this?

This is where we hope networks will come in. As an analogy, think about a computer - you can know all about how transistors function, or how processors and buses and memory work in detail - in the case of yeast, it is like we have one particular logic board really figured out. But really, all that information only gets you so far if you don't understand things at a higher level of abstraction - if you don't understand the processor instruction set, or memory addressing, or network communication protocols.

Going back to Barabasi's review, we read further that:

"Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century."

If only that were true. We certainly have seen advances made by people who study general properties of networks in abstract terms (Barabasi is one of those people), but I have frankly found very little to suggest that the 'universal laws' emerging from this work have deeply enhanced our understanding or predictive ability. For example, we know that many networks are 'scale free' - meaning that most network components have few connections, while a few 'hubs' have many connections. (The distribution of connections is exponential, which is what is meant we we say scale free networks follow a power law.) Why do we care whether a network is scale-free? The answer is that such networks more resistant to disruption; so scale-free biological networks are reasonably robust to mutation. After decades of genetic knock out experiments, we knew that fact already though, and while it's nice to know the source of this robustness, the utility of the scale-free concept seems to end there.

Compare this with the discoveries of the 50's and 60's - the Central Dogma: the idea that DNA makes RNA makes protein, or Jacob and Monod's model of gene regulation, which changed the way everyone in molecular biology thought about their work. These concepts set down the foundation for almost everything that happened in the next 50 years, culminating in the genome sequencing projects of the last decade. These genome sequencing projects are in some ways the ultimate validation of the importance of the Central Dogma.

I haven't come across any use of network concepts in biology that promise to be as significant. Basically, I think we don't even know how to start thinking about the next level of abstraction. Some people have made the excellent point that we shouldn't expect that level of abstraction to be closely analogous to what we see in human-designed circuits or computers, because evolution is likely to have hit upon very, very different solutions. That sounds like a tantalizing challenge.

What people have been doing instead is to just keep doing what we've been doing for 50 years, albeit with fancy genomic tools. We're still filling in details and mapping the interactions in the cell. But most of us deeply believe that there is another level of explanation - more than just having the most detailed map we can get. Sure, we can plug all those details into a computer and call it a model, but I'm not sure we'll understand much more as we do that.

So really, I think we're still waiting for networks to have a serious impact on the way we think about biology.