Many research strategies, developed inside large research institutions, don’t work well in small teaching-centered institutions.
One of these strategies, I suggest, is the use of a biological model system.
What do I mean by model systems? Any system, used in multiple laboratories, that has been optimized for a broad variety of lab investigations. I’m thinking of zebrafish, Drosophila, mice, C. elegans, E. coli, Arabidopsis, and honey bees. For the ant people, this includes Temnothorax.
It might seem counterintuitive that model systems can prevent you from getting research done in a teaching-centered institution. After all, these model systems were developed, in part, to make research easier and remove methodological barriers experienced by those working in a broad variety of organisms.
Even though model organisms are easy to work with, have lots of standardized methods, and a massive bank of potential collaborators, the costs and risks of working with a model system far outweigh the benefits and the opportunities.
My initial thought on this topic came from observation. I don’t see much research on model systems getting done within teaching institutions. I also have seen a variety of people, who work on model systems in teaching institutions, experience substantial challenges that have kept them from getting work done. Here’s a list of problems that I’ve seen crop up for those using model systems in teaching institutions.
- The low-hanging fruit has been picked. In model systems, the things which are a combination of easy and obvious have already been done. That only leaves things which are either difficult, requiring particularly deep insight, or trivial.
- Model systems have intense competition. In model systems, many labs compete rather than collaborate. A small lab in a teaching institution isn’t built to deal with this kind of competition. This doesn’t mean that you should avoid competition by working on minutia, but it also means that you shouldn’t choose an avenue that has high pressure competition. As any ecologist will tell you, competition is an interaction which has a negative effect on both parties. (An economist will tell you the same thing, though it’s better for the consumer).
- You might get scooped. The probability that someone is doing exactly what you are working on is higher in a model system.
- Collaborating is difficult. The labs that run model systems typically have lots of routine methods happening like clockwork, run by technicians and students who have become specialized in the model system. If you’re working on this model system, you are not likely to be able to contribute anything of substance to a big lab, which could do the same thing much more quickly than you could offer. Collaboration is easier if you have a specific resource or skills that others want or need, and that’s harder from a teaching institution if you work in a model system.
- You may become isolated. In the community of people working in model systems, if you have a small undergraduate lab you’re more likely to be overlooked by your peers. I’m not sure how or why this is the case, but it’s what I’ve observed on several occasions. Everyone I know who has worked on a model system wasn’t well integrated into the community of frenemies that works on the system. It’s hard to have a scientific impact if you are unrecognized by your peers.
- The bar for evidence is very high. Peer review is more likely to be rough and demanding. People with model systems tend to be territorial, and especially if you’re not perceived as a competitor that can bite back with teeth, then at least one reviewer is likely to go the extra mile to find something wrong with what you’ve done.
- The minimum publishable unit is greater. If you’re working in a model system, odds are that the big labs working in the field are able to heavily replicate their experiments, and sandwich multiple experiments together into a paper. This establishes a standard for a large volume of data in a paper. I’m not arguing that you should seek to publish as soon as you have enough data for any kind of publication. However, if you have a genuine, interesting, and cool finding and want to get it out for everybody to read, it’s a bummer to have others telling you that you that it’s ready for publication because you don’t have a mountain of data.
- Maintaining colonies takes continuous effort. Mustering any kind of consistent student work in the lab is a success. You don’t want your success in long-term student research to be squandered on the maintenance of lab colonies of a model system. You want to work on a system in which students can dive in and collect actual data, rather than spending all of their time keeping critters fed, clean, reproductive and healthy. Also, you want to be free to disappear for long periods of time, and not chained to the lab to maintain your organisms.
So why would one ever work with a model organism? One great reason would be to provide lots of valuable hands-on laboratory experiences to students. But in my observation, this kind of approach is a poor recipe for completing original research in a teaching institution.
In my opinion, if we are giving students research training, that means we’re giving them research training. Which means that they’re doing research, and research is something that ends up in new findings that are shared with the scientific community. If you want to give a genuine research experience to students, then you need to regularly publish the work that you are doing. From my vantage point, it’s very difficult to do this while working with a model system in a teaching institution.
I have a couple other alternative explanations for the (perceived) phenomenon that researchers working on model systems in teaching institution have a harder time publishing their work. The first alternative is that I’m just wrong, and that my experiences or perceptions aren’t representative or accurate.
The second alternative is that less research is completed because of the use of strategies from R1 labs that don’t translate to a teaching institution. Most people at teaching institutions, who work on model systems, are continuing with their dissertation system, I think. The research approaches from a dissertation lab probably don’t translate well to a teaching institution. I haven’t spent much time inside the labs of teaching institution professors using model systems, so I don’t know how they run things on a day-to-day basis, and am not too sure about this.
Do you think that it’s hard to get research done on a model system at a teaching institution? Did you switch into one, or out of one, to get research done? Thinking about it?
Note: This is a repost of a post from August 2013. It did have good comments back then, by the way. I’m posting this because a) most of you haven’t seen this; b) I still think it’s a valid argument and a point to ponder; c) it’s interesting to see how my writing style for this site has evolved – I think I’m a much better writer now than I was then; d) Sabbatical has been busier than non-sabbatical and I’ve got lots of posts I’m excited to write, but not at this moment.
But by opting out of model systems you are excluding yourself from almost all of life science research. It seems like the alternative to using model systems is either working on obscure species that are difficult to source commercially and probably difficult to rear in lab, or field work which is expensive and logistically complicated to involve students in. I’m sure there are a lot of exceptions, especially in ecology type settings, but model organisms is for many fields the norm. If you are a neuroscientist it would be very difficult to study anything unless you use model organisms, or humans which is a lot more complicated and usually only possible in research universities. That said I think that it is a matter of research question rather than research organism that makes work more or less competitive. There is plenty of research to be done on mice, rats, fish, worms, flies, etc. that does not require expensive techniques or 20 person labs, and that is very publishable. It is just a matter of picking the right research question. In my opinion, the difficulties you describe are more a reflection of the difficulties of doing science in a teaching focused university that of model systems.
As a researcher from a small teaching school, working on a model organism, I tend to disagree with your statement (although it is well articulated, and thought provoking!).
For one, when writing about “model organisms” you really mean “top 5 or so model organisms”, which changes the message a little bit. If you work in an “emerging” model organisms (like marmorkrebs or, say, Xenopus tropicalis), or an “oldschool” model organism with somewhat waned fame (like ranid frogs or blowflies), you are golden.
Second, and perhaps more importantly, even within more developed organisms there are always areas that nobody studies right now. Even for something like zebrafish, if you multiply number of known cell types, the number of genes that can be disregulated in each of them, and the number of research questions you can ask, you get a almost “combinatorial” number that is much greater than the number of people working on zebrafish at any given moment. Of course you can be “scooped”, but replications are still a thing, and both from a pedagogical perspective and from a tenure point of view, hot replication study is still some excellent material. And in most cases you probably won’t get scooped, just because the numbers are so high that nobody will compete with you directly.
And then if you feel really blue, again, you can always move laterally to some medaka fish or whatever (see point one).
A larger “minimal publishable unit” is a real problem, at least in my field (neuroscience), and I hope we as a community will gradually move away from the “Cell” model of publishing (huge finished story, 10 figures, 8 different methods, two postdocs’ lives sacrificed on the altar of science) back to more localized studies that used to be the norm in the 70s-80s, and still seem to be a norm in ecology and chemistry. But this is really a broader problem, and some publishers seem to be fighting it, at least to some degree, so I am fairly optimistic about this part as well.
While I would strongly disagree with the statement that “opting out of model systems you are excluding yourself from almost all of life science research,” both of the previous comments are really compelling – there is still a lot of biology to be done with these organisms. In my fairly small department (~10 FTE), we have labs that focus on E.coli, Arabadopsis/Physcomitrella, Xenopus, Mosquitos, Manduca, and most recently Zebrafish. Plus, we are adding someone soon who works with a mouse model, an approach shared by colleagues in neuroscience.
Some of these folks have found a specialized niche, while others are using the model to answer questions that are somewhat atypical for the particular model – e.g., using Manduca to look at allometric scaling, or examining flowering time genes in moss.
I think that all of the points Terry brings up are valid concerns, but each of them has a flipside as well. For example, the low hanging fruit being picked also means that there is a LOT of existing background information (and techniques) that might inform creative new applications of the model.
I would not count them out as a valid approach at a teaching institution.
I’d also say use what you have access to to do research. Honeybees might not be great in some cases, but if your campus maintains a bee colony for some reason….there you go! On my undergrad (PUI) institution, the ecology professor did a lot of experiments with tagging and tracking the campus squirrel population. You do bring up valid points for teaching students. A very effective combination I’ve seen in the plant world is to use 2 organisms, the model, Arabidopsis, combined with another plant and using them compliment each other (like taking the 2nd plant’s version of a gene and expressing it in Arabidopsis to see if it can rescue a loss-of-function of the homolog in Arabidopsis since it’s easily transformable.
There’s clearly not a ‘one size fits all’ answer here and technology is making non-models a lot more accessible for research (as well as making them ’emerging models’ for specific things).
“You might get scooped. The probability that someone is doing exactly what you are working on is higher in a model system.”
It is actually strange that there is still so much concern of being scooped.
Given the current crisis of reproducibility, one would expect to see at least 3-5 papers from different labs to really trust something while just one paper – even in CNS – would not be sufficient.
That’s actually a wonderful suggestion! And in a way similar to what I meant by “moving laterally”, to a similar, but understudied animal model. Provided that the researcher doesn’t move too far, it would give them both an opportunity to publish original research, including breakthrough research, potentially, and an opportunity to replicate existing well-known data. Because replication is important.
“The low-hanging fruit has been picked. In model systems, the things which are a combination of easy and obvious have already been done. That only leaves things which are either difficult, requiring particularly deep insight, or trivial.”
That depends on what should be considered trivial or important. Probably, the current values are a bit distorted.
In fact when you start to work on any question you realize that we still know very little about anything and that a lot of staff that could be useful for you is missing:
a) we still don’t know what most genes do in any model system from E. coli to flies and humans;
b) we still don’t know how intracellular signaling, protein sorting and trafficking pathways work – in detail;
c) how various important proteins are regulated on various levels;
d) various engineered protein tools in biology/biotechnology can be further improved – but usually no one bothers – as well as plenty of new useful staff could be developed;
e) repetition studies
g) etc, etc, etc
The problem is that this is an enormous pile of small questions without an immediate high impact. Cool labs in “cool” places don’t work on that because the impact on average won’t be sufficient to publish it in “cool” journals and there are always plenty of more cool staff to work on.
So in fact there are plenty of questions to work on in any model organism and beyond.
This is where labs with less resources could contribute a lot for the scientific community and fill the gaps left by research-intense institutions.
As I probably said on that old post, I think my own model system (protist microcosms) would be great at a teaching-focused institution. But that’s in part because it’s an exception to many of your points (which all sound reasonable to me, though I don’t have much experience of my own by which to judge them).
Not many people work in the protist microcosm system, and very few have made a career out of it, and none of them are cliquish. Which obviates all those concerns about the intense competition, getting scooped, unreasonable pickiness of reviewers, etc.
It’s easy to train undergrads to work in the system. My summer undergrads only need a few days of training.
The required equipment and ongoing costs are both cheap. You don’t need the sort of lab one could only buy with a massive startup package to be able to work in the system.
You don’t need collaborators.
The experiments are short–30-90 d is typical. A undergrad can complete an independent study or honors project within a single semester.
Maintaining colonies is easy. You just let the cultures sit on a shelf in your lab, and subculture every 3-4 weeks, which is not an onerous task. And any old cultures you don’t need any more can be poured down the drain. No animal care protocols with protists.
I switched from a non-model, social wasps, to a model, social amoebae, Dictyostelium discoideum. There are still low hanging fruits and benefits if you take the model from a very different angle. But interesting to think about.