A month in papers: mostly philosophy of biology

I’ve seen a number of people that have aimed for reading one paper a day for every day of the year. Unfortunately, I am not great at new years resolutions, and would never be able to keep pace for all 365 days. Instead, in April I tried a one paper a day challenge for the month. I still came up short, only finishing 24 of 30 papers. But I guess that is enough for one paper per weekday.

As I went along, I posted tweet-length summaries in a long thread. In this post, I want to expand on and share what I read in April. And in the future, I think I’ll transform the month-goals into week goals of five papers per week. Just to avoid colossal twitter threads. I tried that last week, for example. But I don’t think I’ll end up making those into posts. Although, as happened in April, they might inspire thematic posts.

Read more of this post

Advertisements

Symmetry breaking and non-cell-autonomous growth rates in cancer

“You can’t step in the same river twice” might seem like an old aphorism of little value, but I think it is central to making sense of the sciences. This is especially clear if we rephrase it as: “you can’t do the same experiment twice”. After all, a replication experiment takes place at a different time, sometimes a different place, maybe done by a different experimenter. Why should any of the countless rules that governed the initial experiment still hold for the replicate? But our methodology demands that we must be able to repeat experiments. We achieve by making a series of symmetry assumptions. For example: the universality or homogeneity of physical laws. We can see this with early variants of the principle of sufficient reason in Anaximander and Aristotle. It developed closer to the modern statements with Galileo, Copernicus and Newton by pushing the laws of physics outside the sublunary sphere and suggesting that the planets follows the same laws as the apple. In fact, Alfred North Whitehead considered a belief in trustworthy uniformity of physical laws to be the defining feature of western philosophy (and science) since Thales.

In this post, I want to go through some of the symmetries we assume and how to break them. And I want to discuss this at levels from grand cosmology to the petri dish. In the process, I’ll touch on the fundamental constants of physics, how men stress out mice, and how standard experimental practices in cancer biology assume a cell-autonomous process symmetry.

Read more of this post

Eukaryotes without Mitochondria and Aristotle’s Ladder of Life

In 348/7 BC, fearing anti-Macedonian sentiment or disappointed with the control of Plato’s Academy passing to Speusippus, Aristotle left Athens for Asian Minor across the Aegean sea. Based on his five years[1] studying of the natural history of Lesbos, he wrote the pioneering work of zoology: The History of Animals. In it, he set out to catalog the what of biology before searching for the answers of why. He initiated a tradition of naturalists that continues to this day.

Aristotle classified his observations of the natural world into a hierarchical ladder of life: humans on top, above the other blooded animals, bloodless animals, and plants. Although we’ve excised Aristotle’s insistence on static species, this ladder remains for many. They consider species as more complex than their ancestors, and between the species a presence of a hierarchy of complexity with humans — as always — on top. A common example of this is the rationality fetish that views Bayesian learning as a fixed point of evolution, or ranks species based on intelligence or levels-of-consciousness. This is then coupled with an insistence on progress, and gives them the what to be explained: the arc of evolution is long, but it bends towards complexity.

In the early months of TheEGG, Julian Xue turned to explaining the why behind the evolution of complexity with ideas like irreversible evolution as the steps up the ladder of life.[2] One of Julian’s strongest examples of such an irreversible step up has been the transition from prokaryotes to eukaryotes through the acquisition of membrane-bound organelles like mitochondria. But as an honest and dedicated scholar, Julian is always on the lookout for falsifications of his theories. This morning — with an optimistic “there goes my theory” — he shared the new Kamkowska et al. (2016) paper showing a surprising what to add to our natural history: a eukaryote without mitochondria. An apparent example of a eukaryote stepping down a rung in complexity by losing its membrane-bound ATP powerhouse.
Read more of this post

Counting cancer cells with computer vision for time-lapse microscopy

Competing cellsSome people characterize TheEGG as a computer science blog. And although (theoretical) computer science almost always informs my thought, I feel like it has been a while since I have directly dealt with the programming aspects of computer science here. Today, I want to remedy that. In the process, I will share some Python code and discuss some new empirical data collected by Jeff Peacock and Andriy Marusyk.[1]

Together with David Basanta and Jacob Scott, the five of us are looking at the in vitro dynamics of resistance to Alectinib in non-small cell lung cancer. Alectinib is a new ALK-inhibitor developed by the Chugai Pharmaceutical Co. that was approved for clinical use in Japan in 2014, and in the USA at the end of 2015. Currently, it is intended for tough lung cancer cases that have failed to respond to crizotinib. Although we are primarily interested in how alectinib resistance develops and unfolds, we realize the importance of the tumour’s microenvironment, so one of our first goals — and the focus here — is to see how the Alectinib sensitive cancer cells interact with healthy fibroblasts. Since I’ve been wanting to learn basic computer vision skills and refresh my long lapsed Python knowledge, I decided to hack together some cell counting algorithms to analyze our microscopy data.[2]

In this post, I want to discuss some of our preliminary work although due to length constraints there won’t be any results of interest to clinical oncologist in this entry. Instead, I will introduce automated microscopy to computer science readers, so that they know another domain where their programming skills can come in useful; and discuss some basic computer vision so that non-computational biologists know how (some of) their cell counters (might) work on the inside. Thus, the post will be methods heavy and part tutorial, part background, with a tiny sprinkle of experimental images.[3] I am also eager for some feedback and tips from readers that are more familiar than I am with these methods. So, dear reader, leave your insights in the comments.

Read more of this post

Cataloging a year of blogging: cancer and biology

Welcome to 111101111.

Another year has come to an end, and it is time to embrace tradition and reflect on the past twelve months. In fact, I will try to do one better and start a new tradition: cataloging a year of blogging.

Last year, I split up the 83 content heavy posts of 2013 into nine categories in three themes: established applications of evolutionary game theory (ethnocentrism and the public good; and mathematical oncology), expanding from behavior to society and mind (representations and rationality for replicators; feedback between finance & economics and ecology & evolution; and, learning, intelligence, and the social brain), and envisioning the algorithmic world (proof, automata, and physics; natural algorithms and biology; fitness landscapes and evolutionary equilibria; and, metamodeling and the (algorithmic) philosophy of science). In 2014 there was a sharp decrease in number of posts with only 44 articles of new content (and the 3 posts cataloging 2013, so 47 total) — this was due to a nearly 4 month blogging silence in the middle of the year — but a quarter increase in readership with 151,493 views compared to 2013’s 119,935 views. This time, I will need only two posts to survey the past year; this post for the practical and the next for the philosophical.

MathOncoFor me, the year was distributed between three cities, the usual suspects of Montreal and New York, and in October I moved down to Tampa, Florida to work with David Basanta and Jacob Scott in the Intergrated Mathematical Oncology department of the H. Lee Moffitt Cancer Center and Research Institute. A winter without snow is strange but wearing shorts in December makes up for it; plus the sunsets over the Gulf of Mexico are absolutely beautiful. Unsurprisingly, this move has meant that the practical aspects of my focus have shifted almost completely to biology; cancer, in particular.

This post is about the biology and oncology articles that made up about half of last year’s content. Given the autobiographical turn of this post, it will be (loosely) structured around three workshops that I attended in 2014, and the online conversations and collaborations that TheEGG was a host to.
Read more of this post

Experimental and comparative oncology: zebrafish, dogs, elephants

One of the exciting things about mathematical oncology is that thinking about cancer often forces me to leave my comfortable arm-chair and look at some actually data. No matter how much I advocate for the merits of heuristic modeling, when it comes to cancer, data-agnostic models take second stage to data-rich modeling. This close relationship between theory and experiment is of great importance to the health of a discipline, and the MBI Workshop on the Ecology and Evolution of Cancer highlights the health of mathematical oncology: mathematicians are sitting side-by-side with clinicians, biologists with computer scientists, and physicists next to ecologists. This means that the most novel talks for me have been the ones highlighting the great variety of experiments that are being done and how they inform theory.In this post I want to highlight some of these talks, with a particular emphasis on using the study of cancer in non-humans to inform human medicine.
Read more of this post

Phenotypic plasticity, learning, and evolution

MendelBaldwinLearning and evolution are eerily similar, yet different.

This tension fuels my interest in understanding how they interact. In the context of social learning, we can think of learning and evolution as different dynamics. For individual learning, however, it is harder to find a difference. On the one hand, this has led learning experts like Valiant (2009) to suggest that evolution is a subset of machine learning. On the other hand, due to its behaviorist roots, a lot of evolutionary thought simply ignored learning or did not treat it explicitly. To find interesting interactions between the two concepts we have to turn to ideas from before the modern synthesis — the Simpson-Baldwin effect (Baldwin 1886, 1902; Simpson, 1953):
Read more of this post

Dogs are hosts to the oldest and most widely disseminated cancer

SugarA little while ago, I got a new friend and roommate: Sugar. She is very docile, loves walks and belly-rubs, but isn’t a huge fan of other dogs. Her previous owner was an elderly woman that couldn’t take Sugar outside during most of the year — if you haven’t heard, Montreal is pretty difficult to walk around during winter. This resulted in less exposure to other dogs leading to an anti-social attitude, and less exercise which (combined with Sugar’s adorable demands for food) made Sugar overweight. She now gets plenty of exercise and is slowly returning to a healthy weight and attitude.

But, you can never be too careful, so Sugar will go in for a check-up on Monday. Just like humans, dogs have many treatable conditions, and for some — like cancer — it is better to catch them early. But when it comes to cancer, there is one things that sets dogs apart from nearly all other species: they are susceptible to one of only two known naturally occurring clonally transmissible cancers — canine transmissible venereal tumor (CTVT).

That’s right, a contagious cancer. More precisely a single clonal line that has been living as as a parasitic life form for over 11,000 years (Murchison, Wedge et al., 2014)!
Read more of this post

Evolution as a risk-averse investor

DanielBernoulliI don’t know about you, but most of my money is in my savings account and not in more volatile assets like property, bonds, or stocks. This is a consequence of either laziness to explore my options, or — the more comforting alternative — extreme risk-aversion. Although it would be nice to have a few thousand dollars more to my name, it would be devastating to have a few thousand dollars less. As such if I was given a lottery where I had a 50% chance of loosing $990 or a 50% chance of winning $1000 then I would probably choose not to play, even though there is an expected gain of $10; I am risk averse, the extra variance of the bet versus the certainty of maintaining my current holdings is not worth $10 for me. I most cases, so are most investors, although the degree of expected profit to variance trade-off differs between agents.

Daniel Bernoulli (8 February 1700 – 17 March 1782) was one of the mathematicians in the famous Bernoulli family of Basal, Switzerland, and contemporary and friend of Euler and Goldbach. He is probably most famous for Bernoulli’s principle in hydrodynamics that his hyper-competitive father Johann publishing in a book he pre-dated by ten years to try and claim credit. One of Daniel’s most productive times was working alongside Euler and Goldbach in the golden days (1724-1732) of the St. Petersburg Academy. It was in Russia that he developed his solution to the St. Petersburg paradox by introducing risk-aversion, and made his contribution to probability, finance, and — as we will see — evolution.
Read more of this post

Predicting the risk of relapse after stopping imatinib in chronic myeloid leukemia

IMODay1To escape the Montreal cold, I am visiting the Sunshine State this week. I’m in Tampa for Moffitt’s 3rd annual integrated mathematical oncology workshop. The goal of the workshop is to lock clinicians, biologists, and mathematicians in the same room for a week to develop and implement mathematical models focussed on personalizing treatment for a range of different cancers. The event is structured as a competition between four teams of ten to twelve people focused on specific cancer types. I am on Javier Pinilla-Ibarz, Kendra Sweet, and David Basanta‘s team working on chronic myeloid leukemia. We have a nice mix of three clinicians, one theoretical biologist, one machine learning scientist, and five mathematical modelers from different backgrounds. The first day was focused on getting modelers up to speed on the relevant biology and defining a question to tackle over the next three days.
Read more of this post