Is mathematical aptitude the limit of how well I can do in an interdisciplinary life science field as a computer scientist?

_{I apologize in advance if this question is too broad, or too subjective - I couldn't think of a better place to ask it.}

Summary: does mathematical aptitude impose an upper boundary on how well one can do in research areas where life sciences intersect with engineering and computer science? I.e., is an ideal researcher in such fields either an experimentalist or an applied mathematician/physicist?

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I'm a computer science graduate thinking about pursuing a PhD in an interdisciplinary life science field; specifically systems biology, synthetic biology, bioinformatics, genomics or cognitive (neuro)science. Characteristic of all these disciplines is that they seem to - ideally - require one of the following two profiles:

• someone with great experimental/domain skills (e.g. molecular biology),


• someone with great quantitative skills.

By virtue of my undergrad background, I'm likely closer to the second profile, even though I have some domain knowledge. However, here's where my self-doubts begin. People of this profile seem to be - in the long term at least - expected to produce new theoretical knowledge primarily by employing advanced math. Top research papers seem to be full of it. I'm concerned about this for the following reasons:

• my mathematical skills and aptitude in the context of this profile are average, or a bit above average at best. Sure, I can handle ordinary differential equations and numerical integration, but once it gets to the postdoc level and above, I'm competing with people who are elite talents and have backgrounds in math or physics from top schools. I don't believe I posses anywhere near the math talent that they have, and I simply wouldn't be able to do the job as good as them - and I don't want to be producing subpar research. Furthermore, I spent a significant amount of time studying things that don't seem applicable to this type of research, like CPU architecture or OS internals. Because I'm targeting programs in Europe - where a PhD is normally 3 years - I can't in this short time develop my math skills enough to close the gap, especially considering my less-than-elite talent (funny as it may sound, I believe I have a much greater talent for the humanities/philosophy, but the amount of positions there is close to zero).


• by far my strongest actual skill is coding/programming, and I could probably get a position based on this alone. I believe I could do well enough to produce some tools and even get a PhD eventually, but after this step - when an independent research path is expected - it seems to me I would be at a disadvantage and heading for the industry, because implementation skills are simply not enough, i.e. they seem to have a supporting role in academia. Permanent faculty positions seem to go out at much higher rates to math-inclined individuals, who are at an advantage when it comes to formulating a unique theoretical research statement. At least, this is my observation.

Should I choose something else if I'm not a top math talent?

Or is such categorization of researcher profiles in fact a false dilemma and there are more options, i.e. not everything required to perform well in these fields can be reduced to mathematical aptitude?

For what it's worth, I did an undergraduate research in computational biology that lead to published papers, and sure I could handle math at that level, but it just seems to me that to make it as a top full-time researcher, I would either have to become an applied mathematician or an experimentalist.