About two months ago I was in Steamboat Springs, Colorado, attending the Keystone Symposium on Systems Biology and Regulatory Networks. I went hoping to hear about forward-looking research that deals with some of the most fundamental outstanding questions in biology - fundamental in the sense of being relevant not just to a particular cell type or organism, but to most cells, developmental systems, or biological systems in general. I not sure whether I really got a glimpse of the future at this meeting, but I did get a good view of the present. Many of the speakers at this meeting are running labs that have made steady contributions to what is currently being called systems biology. I think these labs are producing good research, but I'm not so sure that this is what systems biology should look like. Much of the work presented at the conference was high-throughput data collection and analysis; in other words, genomics. This type of research can help obtain a global picture of what's going on in the cell - such as where all of the transcription factors (DNA-binding regulatory proteins) are under a given set of conditions, or which genes or proteins interact with each other (physically or genetically), forming an 'interaction network.' Unlike a lot of stuff out there that's billed as systems biology, much this stuff was quite good - the organizers did a good job of selecting worthwhile speakers. However, there are two primary reasons I don't think this type of research is really what systems biology should ultimately be: - What we learn isn't fundamental enough - it only applies in certain limited cases, where there are directly homologous systems in other cell types or organisms; it's not about biological systems in general. - The arguments aren't quantitative enough - we may learn quantitative things about many individual genes and proteins, but what we say about the big picture is still only qualitative. In other words, we can't yet reason about biological systems in the same way we reason about engineered, nonliving systems like circuits. These are hard challenges to meet, but unless we meet them, systems biologists will only be saying the same types of things about cells that molecular biologists have been saying for decades, except that they'll be saying them about larger and larger datasets. My favorite talk of the conference was by John Carlson, from Yale, who spoke about how fruit flies smell what's in their environment. Or, in Carlson's words (co-written with Elissa Hallem): "Sensory systems produce internal representations of external stimuli. A fundamental problem in neurobiology is how the defining aspects of a stimulus, such as its quality, quantity, and temporal structure, are encoded by the activity of sensory receptors." Basically, Hallem and Carlson wanted to figure out how a fly's odor sensing machinery is able to discriminate among the many different odors a fly encounters in its environment. An odorant is an often complex molecule, one that binds to odorant receptors (proteins in the cell membrane), which in turn activate a neural signal. Flies (and humans) possess many different odor receptors, but not nearly enough to have one receptor for every odor they can perceive. Instead, flies have to make do with receptors that can detect a variety of odorants, and their neural systems have to integrate this data to produce a useful internal representation of the odors in the environment. Hallem and Carlson took some of these fly odorant receptors and expressed them in special neural cells which harbored no other odor receptors. They would then expose the neural cell to an odorant; if that odorant activated the receptor, there would be an electrical pulse in the neural cell that they could measure. These researchers managed to actually measure this in live flies instead of just using neurons in tissue culture. They would suck up a fly into a pipette, leaving only its head sticking out, and then expose the fly to various odors. In this way they tested over 100 different odors on several dozen different odorant receptors. Not surprisingly, some receptors were activated by a broad spectrum of odors, while others were sensitive to only a few odors. Furthermore, each receptor was sensitive to a unique combination of odors; that is, no two types of receptor responded to exactly the same odors, although there was a lot of overlap in the sensitivity of receptor types. Carlson reported much more data, which I am skipping over here, but to make sense of it all, Carlson and Hallem represented their data in a 24-dimensional 'odor space' (one dimension for each receptor in the analysis). By analyzing the data this way, they were able to categorize the various odorants: certain groups of odors activated very similar sets of receptors; certain odors produced a complex response (that is, they activated many different receptors) while others produced a simple response (by activating only a few receptors). In essence, they were able to characterize classes of odors by the combinatorial neural response the odors produced. In the paper I linked to above, Hallem and Carlson end with this tantalizing sentence: "This analysis provides a foundation for investigating how the primary odorant representation is transformed to subsequent representations and ultimately to the behavioral output of an olfactory system." At the conference, Carlson made good on this promise. Since this is unpublished work, I can't write publicly about what he shared yet. But essentially, he has been able, using his odorant data, to build a fairly predictive model of fly behavior that actually works. When I see his paper come out, I'll post an update here. This is good systems biology. Carlson is doing more than just laying the foundation for a better bug-repellent; he is tackling a fundamental problem of how complex environmental signals are processed by limited numbers of neurons in an animal to produce a coherent behavioral response.