Cataloging a year of blogging: complexity in evolution, general models, and philosophy
February 28, 2017 Leave a comment
Last month, with just hours to spare in January, I shared a linkdex of the 14 cancer-related posts from TheEGG in 2016. Now, as February runs out, it’s time to reflect on the 15 non cancer-specific posts from last year. Although, as we’ll see, some of them are still related to mathematical oncology. With a nice number like 15, I feel that I am obliged to divide them into three categories of five articles each. Which does make for a stretch in narrowing down themes.
The three themes were: (1) complexity, supply driven evolution, and abiogenesis, (2) general models and their features, (3) algorithmic philosophy and the social good.
And yes, two months have passed and all I’ve posted to the blog are two 2016-in-review posts. Even those were rushed and misshapen. But I promise there is more and better coming; hopefully with a regular schedule.
Complexity, supply driven evolution, and abiogenesis
- Variation for supply driven evolution (March 17th) by Julian Xue
- Mutation-bias driving the evolution of mutation rates (March 31st) by Julian Xue
- Eukaryotes without Mitochondria and Aristotle’s Ladder of Life (May 15th)
- Chemical games and the origin of life from prebiotic RNA (November 30th) by Eric Bolo
- Fusion and sex in protocells & the start of evolution (December 18th)
With the above two posts, Julian launched a series on his ideas behind supply-driven evolution (SDE; Xue et al. 2015a,b). SDE stresses that it isn’t just what is being selected for that shapes the direction and ‘destination’ of evolution, but that methods for generating variation are just as important. Sometimes the two work together, sometime supply counter-acts selection, and at other times SDE can overpower selection completely. In the second post, he showed how the existing thought of Yampolsky & Stoltzfus (2001) on mutation bias (that we extended together in Xue et al. 2015b) fit into his overall framework. And what makes mutation rate more special than other traits.
For Julian this work is part of the explanation for why we observe increases in biological complexity. He has more posts in this series, and you can expect to see them early this year.
But while searching for a why, it is always important to keep an eye on the what that is being explained. In this case, are there actually irreversible increases in complexity that need explanation? One of these irreversible rungs is the absorption of membrane-bound organelles by eukaryotes. In the above post, I overview the Karnkowska et al. (2016) work that challenges the view that this is an irreversible step. The authors showed that Monocercomonoides sp. PA203 has completely lost the mitochondrial organelle, instead using another gift from bacteria — the cytosolic sulfur mobilization system. This is a nice reminder to be wary of taking Aristotle’s ladder of life — or its complexity variants — as given.
If we want to climb the ladder, however, it is also important to think about where it starts. Eric Bolo did this on the blog in 2014, by looking at the Bianconi et al. (2013) work on ( how cooperation among enzymes can lead us toward the genesis of biology. Last year, he continued this thread by going from theoretical RNA-like molecules to looking at RNA in real experiments:
Above, Eric reviews Yeates et al. (2016) work on measuring games played by RNA.
In the above article, I provide a detailed summary and review of the Sinai et al. (2016) preprint on primordial sex along with suggestions for extensions. This article pairs well with Eric’s 2014 post on evolution of cooperation among enzymes through the fusion and fission of primordial cells. In the comments, my conversation with Andriy on his thoughts on phoenix cells and fusion in cancer brought back references to Julian’s work on supply driven evolution.
These 5 posts had a corpus of around 8.8 thousand words and garnered 996 views last year.
General models and their features
- Hadza hunter-gatherers, social networks, and models of cooperation (February 4th)
- Modeling influenza at ECMTB/SMB 2016 (July 13th)
- Don’t take Pokemon Go for dead: a model of product growth (October 2nd) by Abel Molina
- Multiple realizability of replicator dynamics (June 9th)
- Multiplicative versus additive fitness and the limit of weak selection (August 17th)
In this post, I review Apicella et al. (2012) and extract their network of relationships between Hadza hunter-gatherers. The hope is to use this network as a backdrop for our work on evolving useful delusions (Kaznatcheev et al., 2014). This January I was in Montreal to discuss this work with Marcel and Tom, so you can expect more posts and an updated paper on evolving useful delusions sometime this year.
My first visit to the UK (minus a day layover in London as a kid) was this July for the ECMTB/SMB conference. As detailed in the previous linkdex, I focused on presenting my recent work on cancer. But the conference itself spanned all of mathematical biology. In my intro to the conference, I focused on one of the plenary talks based on Gog et al. (2014). We got to see SIR-models parametrized from insurance billing data and asked what that can tell us about resistance.
In the above post, Abel develops a simple model for Pokemon Go uptake and retention; similar to a SIR-model. He makes some predictions, in particular: that Pokemon Go isn’t fading into obscurity. With above 4 months since then, it’d be interesting to see how his predictions hold up.
But during the year, I focused not only on other general models but also general features of models. In particular, features of replicator dynamic models.
The first post was originally written as part of the series on double goods game, and an adapted version of the text appears in the appendix of Kaznatcheev et al. (2016). It has also become part of the measuring games project. Although I initially wanted to avoid using exponential growth as a background model, it ended up a more natural approach that measuring the gain function directly. As such, it is important to remember that exponential growth models are one of the many ways to implement replicator dynamics. The second was aimed to clarify a confusion that came up as we refined a model with Pranav Warman and David Basanta. Although it was specific to definitions of fitness in replicator equations, given the ubiquitous nature of the multiplicative weight updating algorithm (Arora, 2012) that underlies replicator dynamics (hint: think of Bayesian inference as another realization), I would not be surprised if there is a broader lesson hidden in these two posts. Maybe I can find it this year.
These 5 posts had a corpus of around 8.9 thousand words and garnered around 2.7 thousand views last year.
Algorithmic philosophy and the social good
Of course, a year like 2016 couldn’t go by without some philosophy:
- Systemic change, effective altruism and philanthropy (June 2nd)
- Argument is the midwife of ideas (and other metaphors) (August 10th)
- Computational kindness and the revelation principle (June 30th)
- Social algorithms and the Weapons of Math Destruction (September 14th)
- Antoni Gaudi and learning algorithms from Nature (December 25th)
The above two posts focused on our conception of helping people, and changing our own thoughts and society through argument. I tried to reconcile my impressions of effective altruism and also explored alternatives to the ARGUMENT is WAR metaphor.
One of the perks of blogging is that old posts sometimes cross the desks of publishing houses. And when they have a new book coming out, they want you to read it an early version. Just in case you might have something nice to say. In the case of Algorithms We Live By and Weapons of Math Destruction, I did have a lot to say. Hence the above two articles.
The capstone and most popular post of the year was a reflection on if algorithms can be learned from Nature or not; and how thinking about architecture might help us. In the article, I lean towards algorithms not being learned from, but instead read into nature. In this way, I continued to develop my thoughts on Post’s Kantian variant of the Church-Turing thesis.
These 5 posts had a corpus of around 11.4 thousand words and garnered around 15.1 thousand views last year.
Apicella, C.L., Marlowe, F.W., Fowler, J.H., & Christakis, N.A. (2012). Social networks and cooperation in hunter-gatherers. Nature, 481(7382): 497-501.
Arora, S., Hazan, E., & Kale, S. (2012). The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory of Computing, 8(1), 121-164.
Bianconi, G., Zhao, K., Chen, I.A., & Nowak, M.A. (2013). Selection for replicases in protocells. PLoS Computational Biology, 9(5) PMID: 23671413
Gog, J.R., Ballesteros, S., Viboud, C., Simonsen, L., Bjornstad, O.N., Shaman, J., Chao, D.L., Khan, F., & Grenfell, B.T. (2014). Spatial Transmission of 2009 Pandemic Influenza in the US. PLoS Computational Biology, 10(6) PMID: 24921923
Karnkowska, A., Vacek, V., Zubáčová, Z., Treitli, S., Petrželková, R., Eme, L., Novák, L., Žárský, V., Barlow, L., Herman, E., Soukal, P., Hroudová, M., Doležal, P., Stairs, C., Roger, A., Eliáš, M., Dacks, J., Vlček, C., & Hampl, V. (2016). A Eukaryote without a Mitochondrial Organelle. Current Biology, 26: 1-11.
Kaznatcheev, A., Montrey, M., & Shultz, T.R. (2014). Evolving useful delusions: Subjectively rational selfishness leads to objectively irrational cooperation. Proceedings of the 36th annual conference of the cognitive science society. arXiv: 1405.0041v1.
Kaznatcheev, A., Vander Velde, R., Scott, J.G., & Basanta, D. (2017). Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature. British Journal of Cancer PMID: 28183139
Sinai, S, Olejarz, J, Neagu, IA, & Nowak, MA (2016). Primordial Sex Facilitates the Emergence of Evolution. arXiv: 1612.00825v1.
Xue, J.Z., Costopoulos, A., & Guichard, F. (2015a). A Trait-based framework for mutation bias as a driver of long-term evolutionary trends. Complexity. doi: 10.1002/cplx.21660
Xue, J. Z., Kaznatcheev, A., Costopoulos, A., & Guichard, F. (2015b). Fidelity drive: A mechanism for chaperone proteins to maintain stable mutation rates in prokaryotes over evolutionary time. Journal of Theoretical Biology, 364: 162-167.
Yampolsky, L.Y., & Stoltzfus, A. (2001). Bias in the introduction of variation as an orienting factor in evolution. Evolution & Development, 3(2): 73-83
Yeates JA, Hilbe C, Zwick M, Nowak MA, & Lehman N (2016). Dynamics of prebiotic RNA reproduction illuminated by chemical game theory. Proceedings of the National Academy of Sciences of the USA, 113(18): 5030-5 PMID: 27091972.