Despite the challenges in historic work on systems biology, you’ll still find a lot of excitement these days in systems biology around Boston if you’ve been following the conference buzz and publications. Depending on who you ask, the promise of systems biology ranges across the board. Systems biology’s purpose may be to lead us to better, cheaper, more effective clinical trials. It might lead us to tailored biological therapies. There’s work on developing better antibiotics. There’s the worthy ambition for developing better artificial biology models and engineering existing ones for eating industrial waste(think little factories)...and you can probably name some that I’m not mentioning…feel free to use the comments function.

I am mildly concerned that there’s an inherent problem in a pell-mell rush towards systems biology. I’m concerned that there are no efforts to educate biologists capable of working in complex systems at the undergraduate level, or even at the graduate level, any more than we really, as a wide community, tried to widely disseminate computational training in biology. Systems biology extends that need. How many undergraduate biology degrees require extensive computer experience, physics, engineering or complex systems courses? Are we going to re-assemble the pieces of “old science” and call it “systems biology”, or are we going to attempt to educate the next generation of systems biologists?

We’ve got to re-train many of our existing biologists to be literate in the computational side of systems biology—not necessarily as expert programmers, but as biologists who can understand and work within the demands of the field. Without involved, literate biologists with a combination of lab and computational skills, “system biology” is going to be a paradigm that doesn’t differ significantly, at the scientist level, from what we already have.

We also need biology-literate classically-trained technologists, engineers, physicists, and chemists. What are we, as a systems biology enterprise, doing to widely encourage and develop this kind of education?

The community should make it a point to say right off, to anyone who cares to listen: some people are going to make promises about systems biology that will be hard promises to deliver on. I’ve heard it said more than once, in symposia and in discussion, that systems biology might make drug discovery more successful at either the discovery end or by helping to make clinical trials more successful.

Those kinds of statements will perk up the ears of biotech and pharma, who spend millions of dollars in failed clinical tests. Failures from from toxic side-effects, and also from a failure to show efficacy. Sometimes, a failure to reach a trial’s designed output might not say anything about the drug’s success in patients, but rather in conditions of the trial, such as the company’s choice of patient to test the drug in. If there is a way to increase success of clinical trials, even by a few percent, that means cost savings of millions of dollars. It’s suggested that systems biology approaches can work towards improving clinical trial success rates. Pharmas, biotechs, and large Universities are well aware of the potential of systems biology in this regard.

Promises of more successful clinical trials might be fulfilled someday but possibly with a caveat: I suspect that increased clinical trial success will come at a price I haven’t heard discussed. Along with the systems approach to translational medicine and use of this data in clinical trials, there is the inevitable need to segregate subgroups of patients in smaller groups, because systems biology will discover the diversity of phenotypes on genotypes and each type will require testing. We may have more successful trials, but I suspect they will be successful trials of smaller populations.

Expense may also go up. We’re likely going to see two additional sources of cost at least in the shorter term. First, the need for many more data points per subject to support a systems approach during a trial or preparing for a trial, and second, a greater multiplicity of stratified groups needing to be tested to ultimately make a good-sized target population for a particular drug’s market. Smaller populations successfully targted with a fully developed drug might mean more expensive drugs unless we can target multiple populations—which might mean multiple trials. I think any group will be lucky if it can find clear cases where systems biology approaches will yield information on a large biallelic population that easily segregates out for a clinical trial.

So, as planned, we can apply systems biology to clinical trials, or to drug discovery, or to translational medicine—and we might get lucky in some cases. But, as people in the field for the last few decades already know, biological systems are difficult to model for the fact that the data models aren’t out there, yet. The more detailed the model, the more data needed. There are sophisticated computational models that can model limited or simple systems right now. I’m impressed when I can see some accurate predictions on carefully controlled behavior in prokaryotes or a nice prediction on a biomarker set in a translational oncology setting. There’s good work out there, and I’ll be the first to shove a paper at you about some cool study or another. I’m no pessimist about systems biology as a paradigm for biology. I’m fascinated by it.