I Love Biology Part I: The Replicators and The Blind Watchmaker

A few months ago, I read James Sommer’s article on the flaws on biological pedagogy: aptly called “I should have loved biology”. As a mathematically inclined teenager who thought he was going to be an engineer, I had the very experience that Sommer’s wished he had had with biology. Freshman year at MIT, I took a alternative biology class with Michael Laub (who was later my biology advisor) and Hidde Plough. While other biology classes, especially cell biology, were identical to what Sommer’s complained about in his article, this class was different. We covered the Avery experiment, and all the other experiments that eventually led to the labeling of DNA as the master template molecule. We constructed a scaled-down model of how the DNA alignment algorithm works. We learned about CRISPR, gut microbiome manipulation and gene drives, well enough to understand the basics of how they worked. The class didn’t fully realize Sommer’s dream of bottom-up biological understanding. But it was close enough to make me realize that biology was that kind of discipline, like math had been for me in highschool. And that class, more than anything else, is why I became a biologist.

One of the modules we covered in 7.015

The rest of my biology (and honestly computer science too) education did not live up to the standards set by that class. But it gave me the tools to be able to start building that foundational knowledge of biology myself. While I think Sommer’s misses the mark on some of his prescriptions (drawing as a necessary biological skill for example), I think his general thrust: biology being an inaccessible science, especially to quant-minded people, is correct. And so, over the next few years/decades, I’m going to endeavor to make a small dent in that, one blog post at a time. This week, we start at the beginning.

Natural Selection: The Core Tenet of Biology

Many things in biology look like they were designed. Almost any part of the human body works so well that we’ve yet to design better artificial replacements. Glasses and dialysis are a poor substitute for the eye and the kidney, respectively. Photosynthesis is still about 2–4x more efficient at converting sunlight into energy than photovoltaic solar cells. Creationists take this “perfect” functioning as evidence for intelligent design by a deity. However, this is pretty overwhelming evidence that these biological machines were created by what Richard Dawkin’s calls the “Blind Watchmaker”: Natural Selection.

There is a process in computer science called the genetic algorithm that mimics this process on a macroscale.

Credit to https://pastmike.com/what-is-a-genetic-algorithm/

Basically, you start with a population of replicators with a collection of random traits. On a computer, this is represented by 0s and 1s, in organisms by stretches of DNA. These replicators produce copies of themselves until resources in the environment start to run low. On the the computer this can be simulated by a capped population size. In the real world, these limits are usually reflective of resource constraints, such as food, water, space, etc.

When the environment is constrained, only the replicators that have best fitness survive and make copies of themselves. Fitness is a concept that will be defined further in other blog posts, but for now its enough to think of it as the level optimization to the environment. On the computer this takes the form of maximizing certain variables. In real life this is usually maximizing the ratio of effort to resource extraction of the environment.

This quickly leads to a population composed of copies of the replicator from the initial population with the highest fitness. But this might not be the best possible fitness, as the pool of replicators was only a limited subset of all possible replicators. Maybe there is some amazing replicator that can consume resources twice as efficiently, but it didn’t happen to be in the starting population. We can do better by allowing mutations to occur: small changes in the instructions of each replicator. Now the whole possible space of replicators can be explored, and the one with the best possible fitness found.

Mutation also allows replicators to adapt to new environments. If you suddenly change the parameters on your genetic algorithm to favor a different variable, or change the food source in the real world, you are now stuck in a low fitness situation. Mutation allows the regeneration of random variation, as seen in the initialization of the environment, which provides the substrate for selection.

For a python implementation of the genetic algorithm, check out this article.

A schematic of fitness landscapes

It is often helpful to visualize fitness using a map. The combination of traits determine your north-south, east-west coordinates. The fitness of those traits determines your height. You can increase or decrease your height by moving on the north-south, east-west access, but only moves that increase your height will be allowed stick in the population.

Of course, its not quite as simple as this. In large populations of replicators, with fairly weak pressure to increase fitness, its difficult for beneficial replicators to do much better than average. We will discuss this in more detail next week.

Proof for Natural Selection

I have seen natural selection at work with my own eyes. When you work in an undergraduate biology lab, it’s not difficult. In many experiments, you grow bacteria on a plate with antibiotics, and a control plate with only the growth medium. Compared to the control plate, which is covered in a carpet of bacteria, the antibiotic plate only has a few dots. These dots are bacteria that are antibiotic resistant, either because of natural variation, or more usually because you inserted the gene on a plasmid. By adding the antibiotic to the environment, you change the selective pressure on the bacteria. Now the most fit bacteria are the ones with the gene that protects them from this change in environment. Natural selection at work.

Researchers at Harvard went a step further and evolved antibiotic resistance from scratch. They made a giant table with increasing an increasing gradient of both food and antibiotic towards the middle. In order to access the food, the bacteria had to develop increasing levels of antibiotic resistance. You can watch this in real time below

Many creationists will agree that bacteria evolve, but refuse to acknowledge that something as complicated as the human eye could have ever evolved. Dawkins refutes the argument on grounds of too steep evolutionary landscape here, but there’s also a ton of evidence of larger scale evolution occurring that we can gather from ecological research. We actually read a paper in class about melanisation of moth wings as a result of the industrial revolution, which established a direct linkage between a single (though large), genetic mutation and the trait itself. You can check out that paper here.

As always, if you have any feedback or questions, leave a comment below or email me at deusexvitablog@gmail.com, or leave a comment below.

“To the contrary, that the very genes do not lose a miRNA that has not been brushed away by the finger of God.” Musings about biology, learning and literature