I graduated last year with a degree in Applied Mathematics and just this month I started a PhD in molecular genetics. I was accepted into a program specifically for individuals with a quantitative background, though the differences between my program and the general graduate program seems fairly minimal.
The department is fantastic and I'm enjoying learning the experimentation, but I haven't had the opportunity yet to use my actual skills.
The more talks I attend, the more I realize that much of the "computational" side of molecular genetics seems to be split between machine learning, bioinformatics and statistics or some combination of the three. In my undergrad I studied topics such as differential equations (partial and ordinary, extensively), dynamical systems, vector calculus (though I never took any fluids courses, I was more of a systems guy), computational mathematics, control theory and some computer science (about 5 courses).
I've talked to a few of the faculty so far and when I mention the possibility of differential equation modeling, they don't seem comfortable giving me guidance. In addition, I read a review paper today that seems to suggest this kind of modeling is fairly uncommon.
So, my questions are these: Are differential equation models useful to geneticists and biologists? Why are they not done more often? How difficult would it be to create my own project that involves differential equation modeling coupled with experimental parameter finding and model verification?
Nobody seems to be able to give me an answer, so I hope the Stack Exchange community has some insight.
Thanks in advance!