British agricultural revolution gave us evolution by natural selection

This Wednesday, I gave a talk on algorithmic biology to the Oxford Computing Society. One of my goals was to show how seemingly technology oriented disciplines (such as computer science) can produce foundational theoretical, philosophical and scientific insights. So I started the talk with the relationship between domestication and natural selection. Something that I’ve briefly discussed on TheEGG in the past.

Today we might discuss artificial selection or domestication (or even evolutionary oncology) as applying the principles of natural selection to achieve human goals. This is only because we now take Darwin’s work as given. At the time that he was writing, however, Darwin actually had to make his argument in the other direction. Darwin’s argument proceeds from looking at the selection algorithms used by humans and then abstracting it to focus only on the algorithm and not the agent carrying out the algorithm. Having made this abstraction, he can implement the breeder by the distributed struggle for existence and thus get natural selection.

The inspiration is clearly from the technological to the theoretical. But there is a problem with my story.

Domestication of plants and animals in ancient. Old enough that we have cancers that arose in our domesticated helpers 11,000 years ago and persist to this day. Domestication in general — the fruit of the first agricultural revolution — can hardly qualify as a new technology in Darwin’s day. It would have been just as known to Aristotle, and yet he thought species were eternal.

Why wasn’t Aristotle or any other ancient philosopher inspired by the agriculture and animal husbandry of their day to arrive at the same theory as Darwin?

The ancients didn’t arrive at the same view because it wasn’t the domestication of the first agricultural revolution that inspired Darwin. It was something much more contemporary to him. Darwin was inspired by the British agricultural revolution of the 18th and early 19th century.

In this post, I want to sketch this connection between the technological development of the Georgian era and the theoretical breakthroughs in natural science in the subsequent Victorian era. As before, I’ll focus on evolution and algorithm.

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Space-time maps & tracking colony size with OpenCV in Python

One of the things that the Department of Integrated Mathematical Oncology at the Moffitt Cancer Center is doing very well, is creating an atmosphere that combines mathematics and experiment in cancer. Fellow TheEGG blogger, Robert Vander Velde is one of the new generation of cancer researchers who are combining mathematics and experiment. Since I left Tampa, I’ve had less opportunity to keep up with the work at the IMO, but occasionally I catch up on Slack.

A couple of years ago, Robert had a computer science question. One at the data analysis and visualization stage of the relationship between computer science and cancer. Given that I haven’t posted code on TheEGG in a long time, I thought I’d share some visualizations I wrote to address Robert’s question.

There are many ways to measure the size of populations in biology. Given that we use it in our game assay, I’ve written a lot about using time-lapse microscopy of evolving populations. But this isn’t the only — or most popular — approach. It is much more common to dillute populations heavily and then count colony forming units (CFUs). I’ve discussed this briefly in the context of measuring stag-hunting bacteria.

But you can also combine both approaches. And do time-lapse microscopy of the colonies as they form.

A couple of years ago, Robert Vander Velde Andriy Marusyk were working on experiments that use colony forming units (CFUs) as a measure of populations. However, they wanted to dig deeper into the heterogeneous dynamics of CFUs by tracking the formation process through time-lapsed microscopy. Robert asked me if I could help out with a bit of the computer vision, so I wrote a Python script for them to identify and track individual colonies through time. I thought that the code might be useful to others — or me in the future — so I wanted to write a quick post explaining my approach.

This post ended up trapped in the drafts box of TheEGG for a while, but I thought now is as good a time as any to share it. I don’t know where Robert’s work on this has gone since, or if the space-time visualizations I developed were of any use. Maybe he can fill us in in the comments or with a new guest post.
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Four stages in the relationship of computer science to other fields

This weekend, Oliver Schneider — an old high-school friend — is visiting me in the UK. He is a computer scientist working on human-computer interaction and was recently appointed as an assistant professor at the Department of Management Sciences, University of Waterloo. Back in high-school, Oliver and I would occasionally sneak out of class and head to the University of Saskatchewan to play counter strike in the campus internet cafe. Now, Oliver builds haptic interfaces that can represent virtually worlds physically so vividly that a blind person can now play a first-person shooter like counter strike. Take a look:

Now, dear reader, can you draw a connecting link between this and the algorithmic biology that I typically blog about on TheEGG?

I would not be able to find such a link. And that is what makes computer science so wonderful. It is an extremely broad discipline that encompasses many areas. I might be reading a paper on evolutionary biology or fixed-point theorems, while Oliver reads a paper on i/o-psychology or how to cut 150 micron-thick glass. Yet we still bring a computational flavour to the fields that we interface with.

A few years ago, Karp’s (2011; Xu & Tu, 2011) wrote a nice piece about the myriad ways in which computer science can interact with other disciplines. He was coming at it from a theorist’s perspective — that is compatible with TheEGG but maybe not as much with Oliver’s work — and the bias shows. But I think that the stages he identified in the relationship between computer science and others fields is still enlightening.

In this post, I want to share how Xu & Tu (2011) summarize Karp’s (2011) four phases of the relationship between computer science and other fields: (1) numerical analysis, (2) computational science, (3) e-Science, and the (4) algorithmic lens. I’ll try to motivate and prototype these stages with some of my own examples.
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On Frankfurt’s Truth and Bullshit

In 2015 and 2016, as part of my new year reflections on the year prior, I wrote a post about the ‘year in books’. The first was about philosophy, psychology and political economy and it was unreasonably long and sprawling as post. The second time, I decided to divide into several posts, but only wrote the first one on cancer: Neanderthals to the National Cancer Act to now. In this post, I want to return two of the books that were supposed to be in the second post for that year: Harry G. Frankfurt’s On Bullshit and On Truth.

Reading these two books in 2015 might have been an unfortunate preminission for the post-2016 world. And I wonder if a lot of people have picked up Frankfurt’s essays since. But with a shortage of thoughts for this week, I thought it’s better late than never to share my impressions.

In this post I want to briefly summarize my reading of Frankfurt’s position. And then I’ll focus on a particular shortcoming: I don’t think Frankfurt focuses enough on how and what for Truth is used in practice. From the perspective of their relationship to investigation and inquiry, Truth and Bullshit start to seem much less distinct than Frankfurt makes them. And both start to look like the negative force — although in the case of Truth: sometimes a necessary negative.
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