I’ve been learning a little bit of biology during 2019, specifically genetics and epigenetics. This post lists out the resources I’ve been using.
Why biology, why now?
Looking back at my notes (from December 2018, in fact) I can’t quite work out why now. I never enjoyed biology at school, as it seemed arbitrary and mostly about memorising names of things. Now, I think it’s one of the most exciting subjects.
It’s likely because:
I’ve always been interested in cognition, development, and machine learning. Understanding biology seems like a useful avenue to improve my understanding.
There’s the health angle. After all, biology is eating the world.
A friend and colleague, Suran, almost certainly floated some intriguing ideas that I wanted to follow up on.
To back up briefly, the first resource I used was the (now withdrawn) Coursera “Animal Behaviour” 8-week course from the University of Melbourne. That was 2013, and it taught ideas around topics such as mate selection, competition, using simple models of behaviour. All fascinating, although it was Tinbergen’s four questions (week 1) that made the biggest impression for me. The four questions are the various hows and whys of behaviour, one of which is about evolution. Back then, I realised I didn’t have much understanding of how genetics worked, and its role in evolution.
On this topic, I also liked Sara J. Shettleworth’s Cognition, Evolution, and Behavior text.
To learn something about genetics, I took Duke University’s “Introduction to Genetics and Evolution”. That’s an 11-week course and opened my eyes to quite how much maths and statistics goes into genetics.
What I found useful from the course was a basic understanding of the structure of DNA, alleles, where genetic markers come from, and how mutation and recombination work out. These are ideas I had from evolutionary computing, but not from real biology.
I was looking back at my notes today. I’ve forgotten details such as how to calculate this or that: but I do have a sense of the logic of the field.
The course text is the excellent Introduction to Genetic Analysis. It’s vast and (to me) comprehensive. If I feel the need to know the facts, I go look there. However, I found the (shorter and older) Evolutionary Genetics from Maynard Smith more enjoyable.
I want to note that A Brief History of Everyone Who Ever Lived by Adam Rutherford is just great for putting this all in context. And for giving a few details: I think BHOEWEL (as nobody calls it) was the first time I read about how you get a picture of DNA.
Next up was the 7-week Epigenetic Control of Gene Expression from The University of Melbourne. While every cell in our bodies has the same genetic information, there are differences in what that genome does. Your brain cells are one kind of thing, and liver cells another, even though they have the same genomes. That’s the expression of genes in the course title, and epigenetics is the mechanism. I learned a lot about these chemical marks applied to the genome that turn on and off genes to produce different effects.
It isn’t easy to highlight one thing from this course. The erasure and re-establishment of epigenetic marks during early development and the lectures on cancer and ageing are perhaps the highlights. If this is an area that sounds interesting, definitely try this one on Coursera.
There was no required text for the course. I was reading The Epigenetics Revolution by Nessa Carey at the same time. This is a popular science take on the same material, and again, I’d recommend it.
Other books and sites
There were a handful of other resources I found useful:
Deep Learning for the Life Sciences is a programmer-centric overview of a wide range of problems and data sets.
The Book of Why. Not directly related to genetics or epigenetics, but addressing causation (rather than correlation).
PubMed, especially the review articles, is a wonderful source of current thinking.
Gene Machine: The Race to Decipher the Secrets of the Ribosome is a book more about how science works, but also a little bit about the ribosome (the machinery that translates mRNA into proteins).
I found it useful to have a science dictionary. I used one on my phone for the convenience of having it ready to go. It was the Science Dictionary by Farlex for iOS.
Genetics and epigenetics are so interesting to me in part because they feel close to programming. It’s hard going, but fascinating. My initial sense was: “what a mess”. But I’m starting to get an idea that it’s really resilient.
The courses and texts I’ve listed above have been a joy to follow. If you have any suggestions, feel free to share (e.g., via Twitter).
What’s next? I’m not sure. Perhaps learning about the immune system. Or maybe going back and doing an A-Level in biology.