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Additionally, for neuroscience you have books like Dayan and Abbott where you’ll see PDE’s, basic fourier analysis, linear algebra. If you want more exotic or abstract math you need to look carefully many compilation projects exist discussing diff geo, group theory, stochastic pde’s, lots of papers as well, applied to morphogenesis and eco-evolutionary modeling. Recently a few researchers wrote a book on reformulating population genetics in terms of information geometry which uses diff geo, lin alg, statistics and probability theory. Most of mathematical biology is going to be PDE’s, stats and probability, dynamical systems theory, linear algebra and multivariable calculus.
The more physics oriented mathematical modeling makes use of the tools physicists are familiar with so you may need more of a background in functional analysis or calc of variations there but then again if you like that kind of thing you will likely end up learning all of that and the physics its usually employed to work with.
Having a basic understanding of analysis, lin alg, stats and probability and diff eq’s can get you started. The nice thing about math bio is that you don’t need an enormous amount of prerequisite exposure to the bio since most of the theory is a patchwork of real experimental rigor, vague intuitions, placeholder descriptions, chemistry and physics pastiches and nonsense that needs to be replaced with actual physics and math.
