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.

So here are the papers that I was reading in April:

  1. Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria.
    Kassen, R., & Bataillon, T. (2006) Nature Genetics, 28(4): 484.

    Focus on Orr-Gillespie theory that samples potential mutants from a distribution that always allows the possibility of fitness increase. Find wild-type is not at fitness peak and a negative cost of resistance with single step mutants. This seems relevant both to my work on hard fitness landscapes (where I focus on unreachable fitness peaks) and the games played by cancer (where we observe a negative cost of resistance).

    As fellow blogger Vincent Cannataro pointed out on twitter, his thesis started from looking at this and asking: what would the distribution of mutations look like for somatic cells? This resulted in a study of stem-cell niches and the distributions of fitness effect in aging and tumorgenesis. And, more recently, in an argument that cancer biologists have much to gain from using classical population genetic and molecular evolutionary theory.

    So clearly, a classic paper that is worth reading.

  2. What fitness can’t be.
    Ariew, A. & Ernst, Z. (2009) Erkenntnis 71(3): 289.

    Ariew and Enst take Gillespie’s model of impact on evolution of difference in variance (see my old post on evolution as a risk-averse investor) to argue for a holistic & non-scalar conception of fitness. In particular, they argue that fitness is not reducible to properties of an individual organism & its (local) environments. I largely agree with this perspective and believe that the ease of agent-based modelling is the reason that the stance Ariew & Ernst argue against is so ingrained in some subfields.

  3. Give one species the task to come up with a theory that spans them all: what good can come out of that?
    Kokko, H. (2017) Proc. R. Soc. B. 284(1867): 20171652.
    Human & taxonomic bias shapes what questions we ask & what we consider as answers in evolution. We might construe evolutionary theory rather differently if we came at it from a different perspective. This seems to mesh nicely with some confusions over fitness and the bias towards thinking about large mammamls like ourselves instead of microscopic systems when looking at defining key concepts.

    This is close to my interest in naturalization of selfishness. And discussions of that interest is what motivated Rob Noble to recommend this paper to me when I visited him in Basel. Although unlike Kokko, I would not attribute such bias to us being human beings, but more specifically to our current/recent culture. I have not sketched out these arguments in detail yet, but I hint at them in the opening of a recent commentary with Tom Shultz on the evolution of externalized morality.

  4. Measured, modeled, and causal conceptions of fitness.
    Abrams, M. (2012). Frontiers in Genetics: 3: 196.

    I found this to be a very useful paper for thinking about difference conceptions. Abrams creates a taxonomy of fitness concepts into four categories: (measured or tendential) token fitness vs. (statistical or parametric) type fitness. Tendential token fitness is similar as what I called reductive fitness in old posts and parametric (or statistical) type fitness is similar to what I called effective fitness. This paper helped me avoid some of my old confusions, and I’ve expanded on it greatly in last months post on token vs. type fitness.

    My only criticism of Abrams is one that extends to all the philosophers I read in April: too much focus on large organisms. In this particular case, it leads him to see measured token fitness as epistemically primary. I don’t think that this is the case for most microscopic systems. In fact, Andriy Marusyk has even expressed skepticism about it being primary for macroscopic systems. However, Andriy was responding to my post where I did not clearly sketch Abrams’ difference between measured vs tendential token fitness and so Andriy might have been doubting the epistemic primacy of tendential token fitness — a sentiment that Abrams would endorse.

  5. Cause and effect in biology.
    Mayr, E. (1961) Science 134(3489): 1501-1506.

    This is a classic paper that divides biology into two methodological camps: functional biology seeks proximal explanations and evolutionary biolagy seeks ultimate explanations.

    Mayr discusses teleology, prediction and four reasons for indeterminacy in biology: randomness, uniqueness, complexity and emergence. I find the last two particularly interesting, since proper abstraction can allow us to have macroscopic theories that hide some of the indeterminacy of their microscopic implementations.

  6. Ernst Mayr’s ‘ultimate/proximate’ distinction reconsidered and reconstructed.
    Ariew, A. (2003). Biology and Philosophy 18(4): 553-565.

    Mayr’s proximate/ultimate distinction was very influential in biology and philosophy, but not as clear as it could be. In this paper, Ariew unpacks this distinction. He thinks that to make ultimate explanations useful, they need to be generalized to ‘evolutionary’ explanations. This generalization is needed because for Mayr, ultimate explanations only appealed to natural selection, while in general, various other evolutionary forces (drift, gene flow, etc) can also matter. The distinction between proximate and evolutionary explanations mirror some of the differences between individual (token) and trait (type) fitness.

  7. Trait fitness is not a propensity, but fitness variation is.
    Sober, E. (2013). Studied in History and Philosophy of Science Part C: Studies in History and Philosophy of Biology and Biomedical Sciences. 44(3): 336-341.

    Sober argues that token fitness is useless in practice. However, some of his points would not convince microbiologists or computational biologists. Sober advises that we focus on type-fitness and its causal properties. In particular, he finds that variation in type fitness is a propensity. I wonder if his argument can be broken by a 2nd-order variant of Gillespie’s toy model: i.e. a model that has variation in variation in fitness.

  8. Is organismic fitness at the basis of evolutionary theory?
    Pence, C.H. & Ramsey, G. (2015). Philosophy of Science 85(5): 1081-1091.

    Pence & Ramsey respond to Sober as an ecologist might. They mirror Abrams’ four categories by looking at two divisions along two dimensions. They see two kinds of fitness organismic vs trait fitness. And they note two roles for fitness: metrological vs conceptual. In looking at how people translate between organismic and trait fitness, they find three distinct but often conflated translations: averaging, being predictively useful, and causing the changes within an organism. I think the last translation can be avoided by using types instead of traits. But I think that the tensions between the first two translations are central to a lot of confusion in biology (like the confusion over spatial structure). Philip Gerlee and I had some inconclusive discussions about this in the comments.

  9. A note on the complexity of evolutionary dynamics in a classic consumer-resource model.
    Ispolatov, I. & Doebeli, M. (2018). arXiv: 1803.07669.

    Here Ispolatov & Doebeli look at the classic consumer-resource model (that I was not familiar with before) and decouple the rate of consumption from the resultant fitness gain. They find that if fitness not power-law of consumption then chaotic adaptive dynamics are possible, else it is a hill-climb. They suggest that they are the first to consider decoupling consumption rate from fitness gain, but I had to use a similar trick for my thoughts on EGT without interactions. Although my treatment was not in the consumer-resource model and had multiple types, and their results are much more thorough and consider the adaptive dynamics of a single type. I have not had a chance to email the authors, yet. Mostly because I am not well versed in adaptive dynamics.

  10. Modifying and reacting to the environmental pH can drive bacterial interactions.
    Ratzke, C. & Gore, J. (2018) PLoS Biology 16(3): e2004248.

    In experimental work that is inspired by a similar concern as the previous theoretical paper, Ratzke & Gore took cells with different monoculture dynamic motifs and then used those motifs to ‘predicted’ new coculture dynamic motifs that they then observed.

    This was very interesting work, and I would be interested to repeat something like this but with direct measurements of fitness functions instead of qualitative fits between motifs.

  11. Selection in a complex world: deriving causality from stable equilibrium.
    Desmond, H. (2018). Erkenntnis 83(2): 265-286.

    Against staticalism, Desmond defends natural selection as causal if it is directed toward a stable equilibrium. But he seems to imagine evolution as proceeding almost exclusively on smooth landscapes and I don’t get how he’d handle epistasis. Would he use compound selection? In particular, I need to think about how Desmond’s thought would apply to hard fitness landscapes where the population can ‘never’ find a local fitness peak.

  12. Populations, metapopulations, and species populations: what are they and who should care?
    Wells, J.V. & Richmond, M.E. (1995). Wildlife Society Bulletin 23(3): 458-462.

    It is of fundamental importance — especially to type fitness — how we define populations. Well & Richmond suggest that populations can be defined by ‘natural’ cuts at one of three structural joints: spatial, genetic, or demographic. They contrast: metapopulations, demes, clusters. This was a very useful overview for me.

  13. Populations as individuals.
    Millstein, R.L. (2009). Biological Theory 4(3): 267-273.

    Gerrymandered populations make selection & drift arbitrary, so a good definition of populations is needed. Starting from six reasonable assumptions, Millstein defines population as a Ghiselin-Hull individual whose parts are causally interconnected by mating and/or struggle for existence. I am drawn to this perspective, because it lets us think of a population as a ‘rational’ decision maker that uses replicator dynamics as its belief update process.

  14. Natural selection and the struggle for existence.
    Lennox, J.G. & Wilson, B.E. (1994). Studies in History and Philosophy of Science Part A 25(1): 65-80.

    Lennox & Willson argue that modern biologists don’t realize the centrality of the ‘struggle for existence’ for Darwin’s natural selection. In particular, they think that ideal r-selection would not be considered as natural selection for Darwin, but as a separate evolutionary force.

  15. The mind, the lab, and the field: Three kinds of populations in scientific practice.
    Winther, R.G., Giordano, R., Edge, M.D., & Nielsen, R. (2015). Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 52: 12-21.

    Discuss different uses and conceptual basis of theoretical populations versus lab and natural populations. They provide a useful discussion of the word `effective’ as a theory-nature bridge.

  16. Size of population and breeding structure in relation to evolution.
    Wright, S. (1938). Science 87: 430-431.

    Wright (1938) discusses three microdynamic implementations (different number of males & females, variance in offspring, cyclic populations) for effective population size and relates this to spatial structure. Similar to what I want to do with the bridge between reductive and effective games.

  17. Maladaptation and natural selection.
    Nesse, R.M. (2005). The Quarterly Review of Biology 80(1): 62-70.

    Praises G. Williams and evolutionary medicine. Suggests six explanations for maladaptation: (1) environmental change; (2) co-evolution; (3) epistasic trade-off; (4) local peaks and stochastic paths; (5) fitness is not health; (6) symptoms as aversions.

  18. Evolutionary constraints.
    Hansen, T.F. (2015). Oxford Bibliographies DOI: 10.1093/OBO/9780199941728-0061.

    Distinguishes functional vs orthogenic views: evolution driven by external selection vs. internal lineage-specific forces. Lists Arnold’s three types of constraints on evolution: genetic, selective/functional, and phylogenetic/historical. To port Mayr’s view of causes to the world of constraints, I would consider the external constraints as ‘ultimate’ and orthogenic as ‘proximal’.

  19. The evolution of maladaptation.
    Crespi, B.J. (2000). Heredity 84(6): 623.

    This review looks at four kinds of adaptation: teleonomic, phylogenetic, population genetic, & quantitative genetic to define maladaptation as deviations from adaptive peaks. Lists seven causes and proposes identification methods.

  20. Declining cellular fitness with age promotes cancer initiation by selecting for adaptive oncogenic mutations.
    Marusyk, A. & DeGregori, J. (2008). Biochimica et Biophysica Acta Reviews of Cancer 1785(1): 1-11.

    Marusyk & DeGregori on Adaptive Oncogenesis: fitness of stem cells declines with age thus increases the selection strength for cancer. It is easier for a beneficial mutation to occur in an old or injured tissue.

  21. Constraints on phenotypic evolution.
    Arnold, S.J. (1992). The American Naturalist 140: S85-S107.

    Discussion of genetic constraints & how they’re shaped in short term by selection and developmental and functional constraints. Mentions learning as breaker of constraints, but has little to say about long-term effects.

  22. Evolutionary constraint and ecological consequences.
    Futuyma, D.J. (2010). Evolution 64(7): 1865-1884.

    Does knowing the G-matrix of genetic correlations and other evolutionary constraints help us predict short-term and long-term responses of organisms to climate change? This approach seems like a lot of fun with linear theories and balance of forces. I think it can be useful for short-term, but not long-term constraints.

  23. Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem.
    Frost, J.J. & Pienta, K.J., & Coffey, D.S. (2018). Oncotarget 9(14): 11429.

    The authors suggest that cancer breaks combinatorial, geometric and functional symmetries. They try to inspire a new/fun view but I think they are very sloppy with metaphors to terms in math that have a rigorous meaning (that sometimes isn’t compatible with their claims). It is important to inspire new ideas with analogies to other fields, but I feel that some of the analogies to math try to appeal to the rigour and certainty of math without having any rigour or certainty in the analogy nor definition of the terms they are appealing to.

  24. Evolution of microbial markets.
    Werner, G.D., Strassmann, J.E., Ivens, A.B., Engelmoer, D.J., Verbruggen, E., Queller, D.C., … & Kiers, E.T. (2014). Proceedings of the National Academy of Sciences, 111(4): 1237-1244.

    In preperation for a meeting with Gijsbert Werner, I read his view of microbial interactions through market lens (as a generalization of reciprocity) with analogies to six strategies: (1) avoid bad partners, (2) build local ties, (3) diversify/specialize, (4) become indispensable, (5) save, (6) eliminate competition. Useful for new ideas.


About Artem Kaznatcheev
From the Department of Computer Science at Oxford University and Department of Translational Hematology & Oncology Research at Cleveland Clinic, I marvel at the world through algorithmic lenses. My mind is drawn to evolutionary dynamics, theoretical computer science, mathematical oncology, computational learning theory, and philosophy of science. Previously I was at the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, and the School of Computer Science and Department of Psychology at McGill University. In a past life, I worried about quantum queries at the Institute for Quantum Computing and Department of Combinatorics & Optimization at University of Waterloo and as a visitor to the Centre for Quantum Technologies at National University of Singapore. Meander with me on Google+ and Twitter.

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