I am considering applying to Ph.D. programs, and am not sure what direction to take. I'm hoping you all can help me out.

I have a background in both biology (molecular focus) and physics (condensed matter and math, heavy on theory). Several years industry experience working in labs doing molecular biology and genetics research.

My primary interest is in mathematical modeling of biological systems, and taking this knowledge to then engineer function. Essentially, what I view as a combination of biophysics, systems biology, and synthetic biology.

Here is the dilemma. It is important for me, should I pursue a Ph.D., to have a solid foundation and training in fundamental principles in physics and math, because I want to be able to derive new theories, models, and methods when needed. This leads me to think biophysics or applied math is the "right" area to do my Ph.D in. But biophysics is very broad and systems biology is only a small part. It is also removed from applications in bioengineering.

I know computational and systems biology intermingles biophysics, applied math, genetics, computer science, and various other fields. It also seems like the type of research I am interested in is typically done in computational and systems biology groups. But I'd like to know if computational biologists, aside from doing simulations, also commonly do work in deriving new models/theories in biophysics. Or do they generally apply this knowledge to build simulations and understand data sets (i.e. removed from fundamental biophysics research)? I don't want to be a programmer -- I want to be a theorist who uses computational tools (and only programs when it's needed to simulate something) to solve problems that can't be solved analytically.

Is computational and systems biology an appropriate "umbrella term" for what I'd like to do? Or would I be better suited in a physics Ph.D. program, and attempt to bridge that training towards systems biology research?

Also, how difficult is it to change to a related field if the opportunity arises? Can computational biologists, who are also interested in engineering work, build a model of a system and then work with others to help engineer it?

My overall concern is pigeonholing myself into a field that I don't want to spend decades working in. I want to maintain flexibility along the range of biophysics to bioengineering, and work on problems when the need arises. Also, what field do my research interests actually fall under? I find it often depends on the university and specific department, so it's a hard question for me to answer myself....

Thanks in advance for any input.


closed as primarily opinion-based by David, kmm, user22020, Bryan Krause, AliceD Oct 26 '17 at 21:13

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.


Computational Biology is just the application of computers to solve problems in biology. It may mean using machine learning to identify regulatory regions in DNA or it may be solving a complex system of differential equations to model chemical reactions in some metabolic pathway. Whether a background in physics, chemistry, biology, math, or computer science is more central depends on the exact problem you want to work on, and in fact which aspect of the problem you want to work on.

Also, what field do my research interests actually fall under? I find it often depends on the university and specific department,

Bingo! Your interests are clearly cross-disciplinary, so for any given research question, different researchers in different fields will all be working on different aspects of the problem. Academic disciplines don't get to declare monopolies on research problems!

You Ph.D. is going to require you to spend 4-6 years working on a VERY specific problem. It would be wonderful if you could figure out exactly which research problem you wanted to dedicate the next 6 years to, and then choose the Ph.D. program based on their research program on that problem. This is rare though. More typically Ph.D. students have to spend a year or two doing rotations and talking to potential mentors before picking a research problem. In that case you are going to have to decide which academic discipline attracts you more, and then find a department in that discipline that seems to have a lot of collaborations with the other disciplines that attract you. See Buridan's Ass;

  • $\begingroup$ Thank you! Just the response I was looking for. I actually do know the problem I want to work on. I want to take a biological system (currently working in the agriculture industry; so take plants in this case, but I am open to others) and apply systems biology methods to better predict phenotype, and figure out what parts need to be changed/engineered/modified to drive the plant (or other system) to a desired state (e.g. disease resistance). But, a "big picture" answer to this may require new applied math (graph theory) or biophysical (statistical mechanics) methods that aren't known yet. $\endgroup$ – Diracula Oct 20 '17 at 19:17
  • $\begingroup$ But I "don't yet know what I don't yet know", if that makes sense. So is it better to go more fundamental? (biophysics --> statistical mechanics, applied math --> graph theory, group theory) Or more applied (computational biology, systems biology)? Intellectually I think I'd rather go more fundamental and transition to applied/computational (I think it's harder to do the converse). But I really think this may be limiting future job opportunities (who would hire a biophysics phd for a computational job when you have a computational biologist phd to choose from)? $\endgroup$ – Diracula Oct 20 '17 at 19:24
  • $\begingroup$ (i.e. it's not just a "what am I interested in" question, but also "how do I get a stable job at the end of my Ph.D. question) Which makes it way more complicated. :( $\endgroup$ – Diracula Oct 20 '17 at 19:27
  • $\begingroup$ 'So is it better to go more fundamental? (biophysics --> statistical mechanics, applied math --> graph theory, group theory)' There is no general answer to this. It depends entirely on the problem and your personal tastes. $\endgroup$ – Charles E. Grant Oct 20 '17 at 20:41
  • $\begingroup$ "it's not just a "what am I interested in question, but also but also 'how do I get a stable job at the end of my Ph.D. question) Which makes it way more complicated. ' That's a separate question from the one you asked. Keep in mind that Stackexchange is a Q&A forum, not an open-ended discussion forum. Use the chat facility for open-ended discussions. Right now demand is high for machine learning and 'data science' experts across a variety of industries. Whether that will be true in 6 years, no one knows. No one can give you guarantees on this, other than that jobs in academia will be tight. $\endgroup$ – Charles E. Grant Oct 20 '17 at 20:48

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