We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities -- (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data -- inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomena can be modelled in an infinite number of different ways, through the adoption of an open/pluralistic approach. We explain the open approach using fish locomotion as a case study and illustrate some of the pitfalls -- universalism, creating models of models, etc. -- that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling. This article is dedicated to the memory of Edmund Crampin.

Maths is the language of science. Perhaps a little more surprisingly, maths is also increasingly integral to biology. Computer scientist and World War II code-breaker Alan Turing was one of the first to suggest that biological phenomena could be studied and understood mathematically. In 1952 he proposed a pair of mathematical equations which provide an explanation for how pigmentation patterns might form on animals' coats For hundreds of years mathematics has been used, to great effect, to model relatively simple physical systems. Newton's universal law of gravitation is a fine example.

I started off life with many heroes. Thanks to my grandparents, I had enough and more from mythology. And then of course, there was Hercules. Then I grew a little older, became monotheistic and concentrated all my worship on Alan Turing. The pattern of the monomyth or the'hero's journey' is always the same - mysterious adventures into supernatural worlds, supernatural encounters, fantastic victories and of course, they all come back with the greater powers to help common folk like you and me.