The paradigm of Systems Biology — of attacking systems study as a gestalt rather than a piece-by-piece parts list, is absolutely necessary.

But.

Recall this past decade, when Bioinformatics was getting the same kinds of rush-rush-buzz-promise stuff that we’re hearing now about Systems Biology.

I’m not saying we were naive in the 1990’s and early 2000’s, because I think even then we were trying to be realistic. We didn’t think the genome was going to necessarily yield the grail of pharmacogenomics, any more than we expected microarrays would eventually yield perfectly predictable cause-and-effect at the transcriptional level. Biology once again was revealed as complex and recalcitrant to simple models. Many scientists in industry and academia continued to represent the information-driven paradigm as the next wave that would make medicines cheaper, make drug discovery faster—and give whole new therapies that nobody even dreamed of.

We know what happened when the promises didn’t deliver. Granted, drug discovery is a slow process, but computational biology and bioinformatics are generally given a wary eye outside of academia. They didn’t deliver fast enough, so therefore this kind of discovery-driven approach was viewed as a failure.

This mistaken perception should give us a lesson, now, in systems biology. We don’t want to lose the faith of the decision-makers, because systems-level biology research is absolutely necessary for advancement in medical science.

Bioinformatics and computational biology are necessary parts of the effort. The most successful projects in computational biology tend to couple tightly with biology. Similarly, systems biology requires full support from bioinformatics and computational biology, and this should be clear from the start.

If you haven’t read my earlier posts, I’ll let you know that part of my concern is that systems biology, as a paradigm for doing science, is going to inherit the same challenges still facing bioinformatics and computational biology, but further compounded by more challenges in complexity.

In the bioinformatics community, we’ve tried to generate standard protocols for interpreting problems in tech like microarray analysis (for example) and have generally not succeeded. The protocols are there, and there are some good methods for data analysis, but everyone has a different favorite experimental platform and post-processing method. Experimental conditions are not standardized even on the same cell types. All this (and more) confounds federation of experimental results. The difficulties in generating consistent results between experiments aren’t a failure of bioinformatics or mathematics per se. The inconsistencies, outside of a lack of standards, are a consequence of biology—more specifically, challenges in chemistry and molecular-level physics. In fact, most of the problems with bioinformatics analysis of data sets arise from the reality of the data sets. Biology at the molecular level is not usually repeatedly, predictably deterministic. Models are built to take noise, and these molecular processes, into account.

Additionally, high-throughput methods, as they get miniaturized, get more prone to statistical noise at the molecular level, and therefore more prone to variation.

Systems biology is challenged by these facts. Molecular science is heavily stochastic. Molecular biology is highly variable even between “identical” cells. Systems biology is by definition, inherently molecular. We all face the challenges of understanding biological systems, which are controlled by multiple parameters that need to be modeled within multi-component, complex time-dependent multi-hierarchical systems governed by stochastic phenomena at all levels.

Given all these challenges, I fidget a bit when I hear that systems biology’s goal is better and less expensive clinical trials. Our goals should be more immediate: let’s do a good job at figuring out the cellular consequence of insulin signaling, for instance. I don’t want to be the voice of doom, but I also don’t want industry or government giving up on systems biology approaches when they find out it’s even harder than basic bioinformatics problems were. In fact, we haven’t even gotten to a satisfactory place in a lot of bioinformatics and computational biology challenges, including protocols, communication and data federation. As a community, we’re still addressing those questions with projects like Bioconductor for common protocols in statistical data analysis, and BioPax and SBML for common data exchange formats.

Let’s also remind people outside the direct scientific community that we generally don’t know how most biology works at the molecular level. That’s the point of systems biology, after all—to try to figure that out. We might have to start simple. Part of being responsible is saying that we don’t know all that much about what we’re doing. Let’s not let the promises be made for us by keeping quiet about the scope of the problem.

What should we try to do, right now, in order to make sure that systems biology isn’t written off as “too hard”, too soon? How can we avoid systems biology being prematurely labeled as a failed approach?

Here’s my suggestions, please add your own in the comments. This is a community effort.

1) Encourage responsibility in public speaking and in industry partnerships. Academia should be honest with industry as to what’s possible using systems approaches. Small companies should try to refrain from promising to solve all of pharma’s problems with systems biology. Industry and investors should be wary of too many concrete promises in such a new field. As a community, we should encourage industry and academic leaders to speak to the challenges ahead, as much as to the promises. We should remind those who are interested that results won’t happen tomorrow. We have to start working on this problem now before we can expect to start getting results. IN fact, we should expect that most great initial results will probably be as much serendipity as good design and skill.

2) Start in a focused manner. If we can’t start small, we should start focused and not spread our whole community out over many different projects when we might succeed in focusing at least a few large groups on a single project.

3) We have to pay attention, close attention, to education. The International Society for Computational Biology, for instance, has been mulling over the challenges of good computational biology education. We should be more proactive in training biologists to be computer-literate. If a University is going to start a Systems Biology group, they should include a biology degree that will enable a student to function in that group.

4) Actively re-train physical scientists. Don’t make computational biology or systems biology inaccessible to adults who are already trained in one discipline. Encourage them to re-train and come on board. I recall a story of a highly qualified physics PhD interviewing for a job in a systems biology group in a large institution here in the Boston area, being embarrassed when he accidentally heard someone asking a hiring manager why they were bothering to interview an “adult” instead of a “kid”, for a key scientist position in their modeling core? For me, this is confusing. Systems biology projects need, most of all,the sum total of our experience.

5)As we had the Human Genome Project, we need a Human Systems Project. The NIGMS is forming national centers for Systems Biology. We need to move on something wider and multi-centered with set protocols and common language. Don’t close it down to the privileged institutions. Open the effort to all Universities around the world by making data available from a National Systems Project where protocols, cell types, and systems can be better defined.

It would be great to see some more serious dialog out there, instead of rushing to see who can get the most money the fastest with the most faculty on board. We’ve got to make sure that we don’t make people bitter with promises of too much, too soon. If Systems Biology is going to transform biology as we know it, it can’t lose the confidence of the decision-makers.