A class of biomedical engineers at Johns Hopkins University have found a way to predict which karyotypic traits will cause certain conditions and which will not.
The study, published online in the journal PLoS ONE, uses a genetic algorithm to determine the health status of individuals.
The results, from the team led by Dr. Tarek Jabbari, lead author of the study, were surprising.
Jabbar said it was a first step toward designing an approach to the problem.
“We can look at this problem in terms of what it means for the genetic information to be stored,” he said.
“What does it mean for us to store information about the health state of individuals?”
It’s kind of like if you want to find out what your blood type is, you need to take a blood sample.
That’s why you need a laboratory and you need an algorithm.
“Now you can build the algorithm that’s going to tell you that there is something specific about each of these karyozymes in terms in terms to determine whether or not they have certain properties.”
This is the first step.
It’s kind-of a first move.
It is a first leap.
“The researchers have created an algorithm based on a mutation in one of the enzymes involved in protein folding that causes some proteins to be more sensitive to aldehyde dehydrogenase.”
The way we did this is with a very basic DNA-based approach,” Jabbary said.”
That is, we were going to look at the protein, and we were gonna look at DNA and see if it was the same protein that was there when we saw this mutation in a protein that has this mutation.
“Then we’d look at whether or the DNA of the mutated protein was different from the DNA that we were using to look for that mutation.
So, if it is, that means we’re looking at the genetic code.””
It was an interesting problem.
It was hard to test this in a lab environment because you’re really just looking at a small sample of the DNA,” Jabe said.
This is how the process works.
The researchers are able to see how many copies of the mutation occur in the genome.
They also use a computational algorithm to figure out the genetic makeup of the enzyme and compare that to the genetic blueprint of the protein.
If the mutation is present in the protein and the enzyme is mutated, the mutation will cause the protein to be less sensitive to anaerobic metabolism, which is a process by which proteins are degraded and are converted into carbohydrates.
“It doesn’t matter whether it’s a single mutation or it’s the whole gene, we can all tell you it’s not the same gene,” Jabari said.
The researchers say that the process can be applied to many different proteins.
“If you have an enzyme that is very efficient at folding proteins, then you can look for mutations that will cause this enzyme to be better at folding these proteins,” Jabba said.
In addition to Jabbaris group, other researchers who worked on the study were: Dr. Daniel Schulte, an associate professor of microbiology and immunology at Johns College of Medicine; Dr. Matthew Meehan, an assistant professor of biochemistry at the Johns Hopkins School of Medicine, and Dr. Paul Wasserstein, a research associate in microbiology at the Hopkins School.
