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?



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, neuroscience methods etc.), 
someone with great quantitative skills (math/physics).


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 some kind of position based on that. I believe I could do
well enough to produce some tools and maybe even get a (likely mediocre) 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 merely a supporting role in academia (since they alone cannot generate new knowledge, the main objective of research). 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 and thus can't evolve into any of the two profiles mentioned?

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 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.
Speaking as a computer scientist who works with life sciences: there is a lot more use for computer science than just modeling.

Consider:


Who makes and maintains the instruments?
Who handles LIMS development and deployment?
Who develops automation systems and precision protocols?
Who develops the control mechanisms that actually implement biomedical intervention concepts?
Who develops taxonomies, interchange standards, and data representations?


This just scratches the surface of computer science opportunities in the biomedical 
Unfortunately, the view that you hold of "you're either an experimentalist or a modeler" is far too prevalent in biomedical culture, and computer scientists often get little respect for their contributions.  That culture is starting to shift however, and especially in younger and more engineering-centric subfields like synthetic biology... and especially in some of the awesome things the younger practitioners are doing.