Noteworthy: Are Genes Patentable?

From a scientist’s standpoint, the Supreme Court’s ruling this week in the case of the “Association for Molecular Pathology et al., v. Myriad Genetics, Inc., et al. is perversely logical. For the molecular biologists reading this, frustrated by the ambiguity in the mainstream media, the ruling can be summarized into 3 bullet points:

  • DNA sequences are not patentable
  • mRNA sequences are not patentable
  • cDNA sequences ARE patentable

 

DNA sequences are not patentable because they exist in nature. The act of identifying the gene using common techniques does not enable to discoverer to patent either the location of the gene or its sequence so that others cannot utilize this information because she did not create the sequence or cause it to be in that location of the genome. This protection from patent coverage applies to all variations of the gene, including sequences with specific mutations linked to disease states.

 

mRNA (both as transcribed and once spliced) is not patentable because it is found in nature. If a scientist isolates mRNA, she did not create it, and therefore, she is not creating a new product.

 

It should be noticed, that the courts have ruled that if a scientist discovers a new method to isolate either DNA, mRNA, cDNA, or any similar scientific tool, then this new method would be patentable, even though the sequences discovered using the method might not be.

 

However, cDNA is created in a lab, even if naturally occurring enzymes and chemicals are used to create it. The theory behind this part of the ruling is that in the absence of human intervention, these specific sequences of DNA would not exist (the gene with the absence of introns). Thus the scientist has created a new a new patentable product.  I’ll be discussing this ruling over a few posts, from different angles. In my next post I’ll discuss the impact that this ruling has for molecular biologists.

 

The official ruling can be found here.

Getting Back on The Bike of Research: the Basics of Science Research

This week I moved back to the United States to work at my lab at the NIH for two months before my thesis defense in July. Not only was I in Oxford for 6 months, I was also concentrating on writing my thesis, and didn’t go into the lab except to meet with supervisors. So now I’m back in the lab on a daily basis and doing science. That said, I ‘m currently in the stage of simply accumulating supplies before I can do actually do any experiments.

Originally, the plan had been to simply write up, have my viva, and graduate, without returning back to the NIH. However, my examiners were busy, and couldn’t schedule by defense until July. This gives me some time now to return and try to generate more data. If the data is good, this means that I will have more work to support both my thesis defense and also to help make more coherent papers for publication. Since I’m only going to be back for 2 months, this really limits the scale of experiments that I can do, and certainly rules out completing any new transplant experiments since they take a minimum of 8 weeks to run, with the preparation and analysis times extra. However, I’ve come up with an experiment plan that closely matches what I’ve already done, but to take it a step further in some cases, or to simply do a more sophisticated study to glean better results.

I’m not concerned about coming back into the lab having been away for 6 months. With the exception of one major technique, all others are methods that I’ve done before. And I find that for planning and executing experiments, the logic and the process never change. It’s in line with belief that once you learn to ride a bike, you never really forget. I’ve been seriously studying science for 10 years now, 6 of which have been spent doing genuine research (instead of planned coursework experiments), and over time on learns the basics of experimental logic:

 

1)   How to choose an assay that will allow you to observe the specific factor you wish to

2)   How to minimize distraction by other factors by designing more simplistic experiments

3)   How to use proper controls to make sure that your experiment design is correct, even if the results you get are negligible or puzzling

4)   How to manage resources and time

5)   How to minimize human error (this involves knowing your personal limitations)

 

Over the next two months I’ll keep you informed about what I’m doing, the techniques and reasoning, and also my adventures into paper writing, along with updates on developments in tissue engineering and bone research.