Proving the sky is blue

Sure, it may not look like much, but this little graph here is what I spend just about every day producing. It's the optical fluorescence readout from a real-time polymerase chain reaction. This little graph contains information on 96 samples that I've painstakingly prepared. 30 of sample A, 30 of sample B, 24 standards, and 12 blanks. With a little mathematical witchery, I can convert this graph into information about the relative concentrations of my samples, the amplification efficiency of each sample, and the linear range of amplification for the particular gene I've extracted from my bugs.

This one isn't finished yet, but it's looking good so far. You can see 5, almost 6, distinct groups of curves. These correspond to my 6 dilutions. And you can see that the second group of curves has a whole lot of lines, 60 to be exact. Those are my two samples. It's good to see them pretty closely packed like this. It tells me their standard deviation is low, and that sample A isn't a whole lot different from sample B.

That's a pretty big deal. The whole point of this research is essentially to quantitatively demonstrate that samples A and B are the same. In reality, it's the same sample, but half was treated one way, and the other half was treated another way. Scientists have assumed A and B would be the same for years, but it had never been shown quantitatively because there had been no real-world application in which it would be neccessary. Engineers have now come up with a real-world application that requires quantitative data, so now us quazi-scientist/engineers have to proove A = B. So this is it. This is what I do, day in and day out. With different bacteria. Different genes. Different primers. It's my job to proove A = B...the mathematical equivalent of proving the sky is blue or that acceleration due to gravity is 9.8 meters per second squared.

OK, maybe it isn't that obvious, but alot of the pure-science types around here can't fathom why it's necessary to proove something that's been assumed for so long. They kind of see my research as the equivalent of... well... proving the sky is blue. "It just is," they say. Well, that's all fine and good when your research never extends outside the doors of a lab. But to engineer real-world solutions to address real-world problems, I need real world numbers...otherwise any design is based on a whole lot of arm waving and no substance. When addressing public and environmental health concerns, that's just not good enough. That's why this little graph here is so great.