I’ve never been good at memorising things for their own sake. That’s probably why I never experienced biology as memorisation.
Actually, let me qualify that: Biology is not only memorisation, but maybe involves some degree of it. To study life’s components, they need to named, and then arranged in some logical order. This logical order changes depending on the question at hand. For example a protein can be a sequence of amino acids, a transcription factor or a signaling molecule, depending on the context and question. So you do at the outset need some memorisation but that’s basically not what biology is about. It’s about understanding relationships. I only very superficially understand category theory but I think it’s a subfield of math that has the most in common with how biology works, as it seems to be about relationships between carefully defined objects then the objects themselves.
In bio, much like in math, definitions are everything. In single cell RNA-seq experiments clusters of cells are annotated as specific cell types using cell type specific marker genes. The presence of these genes and the clustering by RNA types and count ensures that cells are mapped to the correct types. This illustrates something important about biological measurement: the need to be stable enough to be useful but flexible enough to accommodate new measurements.
By that I mean the following: for decades individual cell types were annotated by specific marker genes in the absence of ways to quantify the whole transcriptome. Now with the advent of technologies such as scRNA-seq we can simultaneously measure the transcriptomes of millions of cells. We need to be able to map these to the same objects that were studied for decades, so we use the marker genes to annotate. What this says about definitions is that some definitions remain consistent over decades of study. Others are redefined as technology improves. All of this to say: a cell type is not a platonic object. It is a node in a web of relationships that gets refined as the web becomes more detailed.This is perhaps one of the reasons I find biology increasingly mathematical. Not because biology can be reduced to equations, but because both disciplines force you to think carefully about what an object is, how it is defined, and what follows from that definition.
A mathematical object is not necessarily interesting because of what it looks like in isolation. A group is interesting because of the relationships between its elements. A vector space is defined not just by a collection of vectors, but by the operations and relationships that structure them. The object becomes meaningful through the structure around it.
Biology works in much the same way. A cell is not particularly interesting as an isolated bag of molecules. Its identity emerges from its relationships to other cells, its developmental history, its gene regulatory network, its signalling environment and the functions it performs. Change one part of that context and the boundaries of the object can become fuzzy.
This also explains why biological classification can feel simultaneously precise and provisional. We give things names because we need stable objects to reason about, but the names are really handles for much richer structures. A "T cell" is not a single immutable entity sitting somewhere in nature waiting to be discovered. It is a useful classification that captures a particular set of molecular, developmental and functional relationships. As our measurements become more precise, the classification can split, merge or acquire additional dimensions without necessarily making the older classification wrong.
There is something quite satisfying about this. Scientific progress does not always consist of discovering new objects. Sometimes it consists of discovering that the objects we already had names for were embedded in a much richer structure than we could previously see.
Perhaps that is also why I have always found memorisation in science somewhat beside the point. Memorising the names of things gives you the vertices. Understanding science means learning the edges.
