- What scientists refer to when they talk about an experimental system is a set of experiments done on a biological system that allows them to get access to certain questions for which there are no answers. - So when you're thinking about what model system to use, so this is something that is not something that is always very much in our control, right, so it might be that there is, you're in a specific type of lab that has a very specific type of expertise. And I think in science, in general, when you're thinking about the long term of your career, I do think it's really important to consider which model organisms or model systems are really, really the most powerful for answering the questions that you're after, but when you're within a specific type of lab and you're sort of limited by resources or whatever it may be, there may not be much choice that you have. So one thing that's really important to do is to try and think of what types of questions or what aspects of the question that you're asking are you poised to answer using the system that you're using. So that would be sort of the other side of that coin is not just how do you pick a model system, but what is the best use of the model system that you're after. So within the big scientific question that you're asking. - What is important for the studying graduate students to understand is why that lab chose that system. What are the strengths and the weaknesses of that system in addressing the different questions, because that's gonna create a framework of what type of questions you want to go after. Depending on the strength of the system. The system is gonna limit how far you can go with a given question. So the way to understand the limits of the system, you can start by first understanding what the history of the system is. Why did people set up that system in the first place? What were the questions that they felt could be best addressed with that system? - There may one system that has really really good for forward genetics, or another model system that's really really good for reverse genetics, and some that might be particularly suited to specific types of imaging. And so you may say that in this organism, so in our case we're studying a green algae, and in this case we can do some really really wonderful forward genetics and biochemistry and imaging in the flagella that we're studying. And so what we want to do is really go after the types of experiments that we are uniquely poised to answer in this model systems. - So how you define the question is very much tied with the answer you're gonna get, but also the system that you're using. - The examples are we studied secretions in dog pancreas because pancreatic cells, they're dedicated tissue to secrete all your hormones and everything else that you need for your body. So they do secretion a hundred times more than your stomach or your nerve or anybody else. So if you take a pancreatic cell, they're chock-full of the machinery to do secretion and therefore, if you want to study a secretion, that's a good place to study it. Another example is how are we going to find out what are the protein structures at the ends of chromosomes? Well, humans have 46 chromosomes, that's 92 ends, but this little organism called tetrahymena shatters its chromosomes into 2,000 pieces, so it's got 4,000 ends. So if you want to study the proteins that are at the ends of chromosomes, you study in tetrahymena because that's a great place to study it. - What makes biology so exciting today is that the choice of organisms now is getting larger and larger, I mean all the way to humans, I mean the information we can get out of even human beings today is incredible and much more advanced than earlier decades ago. So we're in a really exciting time in biology where, again, the menu of choices of what we can study and how we can study it has opened up a whole variety of questions obviously that were impossible to address when I was a graduate student, but it still requires that wise choice of really carefully thinking about choices right at the onset of the project.