Supply and demand as driving forces behind biological evolution

Recently I was revisiting Xue et al. (2016) and Julian Xue’s thought on supply-driven evolution more generally. I’ve been fascinated by this work since Julian first told me about it. But only now did I realize the economic analogy that Julian is making. So I want to go through this Mutants as Economic Goods metaphor in a bit of detail. A sort of long-delayed follow up to my post on evolution as a risk-averse investor (and another among many links between evolution and economics).

Let us start by viewing the evolving population as a market — focusing on the genetic variation in the population, in particular. From this view, each variant or mutant trait is a good. Natural selection is the demand. It prefers certain goods over others and ‘pays more’ for them in the currency of fitness. Mutation and the genotype-phenotype map that translates individual genetic changes into selected traits is the supply. Both demand and supply matter to the evolutionary economy. But as a field, we’ve put too much emphasis on the demand — survival of the fittest — and not enough emphasis on the supply — arrival of the fittest. This accusation of too much emphasis on demand has usually been raised against the adaptationist program.

The easiest justification for the demand focus of the adapatationist program has been one of model simplicity — similar to the complete market models in economics. If we assume isotropic mutations — i.e. there is the same unbiased chance of a trait to mutate in any direction on the fitness landscape — then surely mutation isn’t an important force in evolution. As long as the right genetic variance is available then nature will be able to select it and we can ignore further properties of the mutation operator. We can make a demand based theory of evolution.

But if only life was so simple.

It might be possible to argue that isotropic mutations at the level of single nucleotide is an acceptable model. Although, of course even there we know that mutations rates are not constant across all nucleotides or in all places in the genome. But just to give the demand-side biologist something, let’s pretend these are isotropic: any nucleotide change is as likely as any other. From here we could build up the demand-side theory; at least if evolution selected genotypes.

But evolution doesn’t select for genotypes, it selects for phenotypes.

And many different genotypes can map to a single phenotype. More importantly, different phenotypes can have drastically different domain sizes: i.e. different number of genotypes that map to them. Most importantly, the difference in phenotypic domain sizes can be systematic across the trait that we’re studying the evolution of. For example: on the one hand, any given symmetric RNA secondary structure will have many more genotypes that produce it compared to any given asymmetric structure (for example, see Dingle et al., 2015). More recently, Dingle et al. (2018) even suggest that this might be a universal feature of certain general classes on input-otuput maps. On the other hand, there might be more genotypes that produce arbitrary (instead of specific) asymmetric structures than symmetric ones since there are many more different asymmetric structures. Or, to borrow one of Julian’s favorite examples in the second direction: there are many more ways to be complex than there are to be simple.

So if we’re interested in a trait like the complexity of organisms then we have to worry about the discrepancy in the larger number of mutants that result in higher complexity than the smaller number that results in lower complexity. If this discrepancy is too large then it can result in an entropic drive that overpowers selection. In other words, even if simpler organisms tend to be fitter, evolution might push towards more complex organisms just because there are more options. This can get even more complicated if the trait under selection is something that alters the supply of mutants — say by changing mutation rate (for example, see Xue et al., 2015).

This would be supply-driven evolution.

But it isn’t the only aspect of supply-driven evolution. For another aspect, we should focus on the words ‘tend to be fitter’ and see how this can be made more precise. Julian has already done this, but — in the spirit of shorter posts — I’ll save that discussion for another post.

We can also move from static fitness landscapes to games by considering how supply can create certain demands. But again, another post.

Hopefully, Julian will point out any of my misrepresentations of his ideas in the comments.

References

Dingle, K., Schaper, S., & Louis, A. A. (2015). The structure of the genotype–phenotype map strongly constrains the evolution of non-coding RNA. Royal Society Interface Focus, 5(6): 20150053.

Dingle, K., Camargo, C. Q., & Louis, A. A. (2018). Input–output maps are strongly biased towards simple outputs. Nature Communications, 9(1): 761.

Xue, J. Z., Kaznatcheev, A., Costopoulos, A., & Guichard, F. (2015). Fidelity drive: A mechanism for chaperone proteins to maintain stable mutation rates in prokaryotes over evolutionary time. Journal of Theoretical Biology, 364: 162-167.

Xue, J. Z., Costopoulos, A., & Guichard, F. (2016). A Trait‐based framework for mutation bias as a driver of long‐term evolutionary trends. Complexity, 21(5), 331-345.

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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.

2 Responses to Supply and demand as driving forces behind biological evolution

  1. Julian Xue says:

    Thanks Artem. You understand me perfectly. I’m not really the first to arrive at this idea, Stoltzfus and others have articulated how the isotropicity of mutations really affect evolution — “arrival of the first” vs. “survival of the fitness”. The goal is to actually for this thought to penetrate standard evolutionary thought and textbooks and curriculum, which as still Natural Selection based.

    I think what we need is a particular evolutionary event that can only be explained supply-side, and some sort of experimental verification that this is the case.

    I think this supply-side theory has explanatory power in the evolution of all the “vaguer” traits for which the genotype-phenotype map is poorly specified, traits such as “complexity” or “intelligence”, because two things can be equally complex or intelligent (by any measure) and actually be incredibly different, with very different fitnesses. In this case, supply-driven forces become important, because if — that’s a very big if — there are a lot more ways to be complex vs. being simple, then we see evolution trend towards complexity, simply because it’s “supplied” up more often.

    I’ve been able to tighten the argument for the steady increase in irreversible hierarchies (prok -> euk -> multicellular), but I’ve got a hunch that this theory can shed some light on evolutionary lock-in as well — although I don’t yet have a clear idea.

    Looking through the site, there’s comments on some of the other posts for years back — including from Stoltzfus in 2016 — that I’ve totally neglected. My terrible apologies (to you and myself, sigh).

    • It’s definitely hard to keep up with blogs and all the comments. Thankfully, it’s never too late to revisit a post!

      The place I want to push supply-side theory is in looking at cancer. Various traits associated cancer progression certainly seem ‘vague’ in the right way. And unlike grand theories (like complexity, etc), it seems potentially more tractable experimentally. Thus, we could use cancer to learn fundamental evolutionary biology. I’d certainly be interested in looking at how supply and demand of mutations interact in cancer initiation and the evolution of therapy resistance.

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