Distributions of overall correlation and p-value when overlapping domains are removed. S2 Fig. Phylogenetic trees for species used in this study. S1 Dataset. Complete model and protein domain annotation, including covariate data for each domain. Acknowledgments We thank Tricia Serio for helpful comments on the manuscript, and Edward Bedrick for consultation regarding meta-analysis and permutation testing.
References 1. Zuckerkandl E, Pauling L. Evolutionary Divergence and Convergence in Proteins. Evol Genes Proteins. An integrated view of protein evolution. Nat Rev Genet. Evolutionary systems biology: links between gene evolution and function. Curr Opin Biotech. Alvarez-Ponce D. In: Fares MA, editor.
Natural Selection: Methods and Applications. CRC Press; Zhang J, Yang JR. Determinants of the rate of protein sequence evolution. Kimura M, Ota T. On some principles governing molecular evolution. Biochemical evolution. Annu Rev Biochem. Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. An analysis of determinants of amino acids substitution rates in bacterial proteins. Mol Biol Evol. Do essential genes evolve slowly? Curr Biol. Wang Z, Zhang J. Why is the correlation between gene importance and gene evolutionary rate so weak?
PLoS Genet. Impacts of gene essentiality, expression pattern, and gene compactness on the evolutionary rate of mammalian proteins. Protein dispensability and rate of evolution. Rate of evolution and gene dispensability. Why highly expressed proteins evolve slowly.
Richard Dawkins, Edward O. Wilson, And The Consensus Of The Many
Hardison RC. Comparative genomics. PLoS Biol. Soskine M, Tawfik DS. Mutational effects and the evolution of new protein functions. Protein tolerance to random amino acid change. High-resolution mapping of protein sequence-function relationships. Nat Methods. From in vivo to in silico biology and back. Loewe L. A framework for evolutionary systems biology.
BMC Syst Biol. Gunawardena J. Models in sytems biology: the parameter problem and the meaning of robustness. Elements of Computational Systems Biology. Structure, function and evolution of multidomain proteins. Curr Opin Struct Biol. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res. BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.
BioModels: ten-year anniversary. The statistical mechanics of complex signaling networks: nerve growth factor signaling. Phys Biol. Dynamic simulations on the arachidonic acid metabolic network. PLoS Comput Biol. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol. Nat Biotechnol.
Evolution and the Levels of Selection by Samir Okasha
Genes Cells. Quantitative analysis of pathways controlling extrinsic apoptosis in single cells. Mol Cell. Systems-level interactions between insulin-EGF networks amplify mitogenic signaling. Mol Syst Biol. A systems biology dynamical model of mammalian G1 cell cycle progression. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Network-level analysis of light adaptation in rod cells under normal and altered conditions.
Mol Biosyst. Modeling regulatory mechanisms in IL-6 signal transduction in hepatocytes. Biotechnol Progr. View Article Google Scholar Building a kinetic model of trehalose biosynthesis in Saccharomyces cerevisiae. Methods Enzym. Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress. J Biol. Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell. Downregulation of PP2A Cdc55 phosphatase by separase initiates mitotic exit in budding yeast. Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles.
J Royal Soc Interface. Kofahl B, Klipp E. Modelling the dynamics of the yeast pheromone pathway. Haldane JBS. The Effect of Variation of Fitness. Am Nat. Evidence for polygenic adaptation to pathogens in the human genome. Molecular properties of rhodopsin and rod function. J Biol Chem. Elucidation of phenotypic adaptations: Molecular analyses of dim-light vision proteins in vertebrates.
Viral ILinduced cell proliferation and immune evasion of interferon activity. Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data. BMC Bioinformatics. Hassler U, Thadewald T. Nonsensical and biased correlation due to pooling heterogeneous samples. Field AP. Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed- and random-effects methods. Psychol Methods. Is the meta-analysis of correlation coefficients accurate when population correlations vary? Physicochemical modelling of cell signalling pathways. Systems biology: parameter estimation for biochemical models.
FEBS J. Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E. Sloppiness and the Geometry of Parameter Space. Uncertainty in Biology. Switzerland: Springer International; Universally sloppy parameter sensitivities in systems biology models. Perspective: Sloppiness and emergent theories in physics, biology, and beyond. J Chem Phys. Accounting for experimental noise reveals That mRNA Levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast.
Assessing the determinants of evolutionary rates in the presence of noise. Duret L, Mouchiroud D. Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Highly expressed genes in yeast evolve slowly. Subramanian S, Kumar S.
Gene expression intensity shapes evolutionary rates of the proteins encoded by the vertebrate genome. The evolutionary consequences of erroneous protein synthesis. Impact of translational error-induced and error-free misfolding on the rate of protein evolution. Misfolded proteins impose a dosage-dependent fitness cost and trigger a cytosolic unfolded protein response in yeast.
Protein misinteraction avoidance causes highly expressed proteins to evolve slowly. Evolutionary rate in the protein interaction network. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mangan S, Alon U. Structure and function of the feed-forward loop network motif.
Soyer OS, editor. Evolutionary Systems Biology. Springer; Limits of adaptation: the evolution of selective neutrality. Genome Biol Evol. Influence of metabolic network structure and function on enzyme evolution. Genome Biol. Loewe L, Hillston J. The distribution of mutational effects on fitness in a simple circadian clock. Berlin: Springer-Verlag; Dynamic sensitivity and nonlinear interactions influence the system-level evolutionary patterns of phototransduction proteins. Proc R Soc B. Characterizing selective pressures on the pathway for de novo biosynthesis of pyrimidines in yeast.
BMC Evol Biol. A whole-cell computational model predicts phenotype from genotype. Kacser H, Burns JA. The control of flux. Symp Soc Exp Biol. Evolution, 41, Jablonski, D. Species Selection: Theory and Data. Annual Review of Ecology and Systematics, 39, Goodnight, C.
Experimental studies of group selection: What do they tell us about group selection in nature? American Naturalist, , SS Muir, W. Group selection and social evolution in domesticated chickens.
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Evolutionary Applications, 3, Pagel, M. New York: Allen Lane. He applies evolutionary theory to all aspects of humanity in addition to the rest of life, both in his own research and as director of EvoS, a unique campus-wide evolutionary studies program that recently received NSF funding to expand into a nationwide consortium. Culture, Genes, and the Welfare of Others. Noses are relevant to fitness as are kin and non-kin groups, and lots of other things , but, ultimately what is selected are not noses, but the genes that produce noses.
There may be slightly genetically different groups of beavers that built slightly different beaver dams. I think you basically nailed it here. Nowak et al. And when Dawkins, Jerry Coyne and friends go after group selection, they are still stuck in stone age arguments, which is pretty embarrassing as a scientist. And sadly, many assume these voices to be the most informed due to their earned capital as good scientists. The majority of the scientific community has no idea what we are talking about most of the time. This, amazingly, is also true for some of the authors as well.
One aspect of the Nowak et al. People were upset Nowak et al. Thanks for the constructive comments so far. The nest influences the fitness of an individual bird and the dam influences the fitness of a group of beavers, introducing all the problems associated with altruism and selfishness.
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On evolution without replicators, it is possible for a phenotypic trait such as a pointy nose to reappear generation after generation. The next question is whether the phenotypic trait can reappear without elements such as genes that replicate with high fidelity. The answer, at least in principle is yes. The concept of hypercycles provides an example. Please consult the references that I provided for more, especially the one showing that cultural evolution can take place without anything corresponding to memes. Also, what is being selected for are the genes. The phenotypes themselves are effects of gene selection interacting with the environment , they are not themselves replicators.
But the genes that produce that extended phenotype do, and, so beaver dams may look like they evolve over time. Asking is it beaver dam selection, kin selection, or group selection? Dawkins is correct: it confuses replicator and vehicle or, extended phenotype. It is truly tiresome. The elephant in the room is that a number of academics have their careers and maybe other things wrapped up in a particular viewpoint on both sides. The paper by Nowak et al seems provocative albeit with intellectual value , the response from the plus others who actually wrote their own responses was lame and did not engage with Nowak et al, and now we see it replicated dare I use the term at all here between Wilson and Dawkins.
Dawkins review is nothing but a polemic, and it just regurgitates what he has always said. There is no intellectual movement or engagement from him at all. What is really needed is someone to provide a nice in-depth resolution of the two positions—actual meaningful points of disagreement, along with points of agreement, as can be found here.
For example, it seems to be that whereas Nowak et al argue that other traits such as communal nest-building need to be in place BEFORE eusociality can arise which is likely to depend on kin effects , the traditional IF view is that kin effects will facilitate the cooperative dispositions, which rollup together traits such as nest-building along with eusocial reproductive strategies. Yes, Greenbeards. If you spend a little time around me, you can begin to evaluate how altruistic I am.
Michael Mills. You have selectively quoted DSW. Nose shapes do replicate in families; but with relatively low fidelity. Nose shapes will indeed be relevant to fitness — and therefore selected — in certain environments. This is immediately apparent from a comparison of nose-shapes between geographically distinct human sub-populations.
I suggest that the assumed need for high-fidelity is the crucial mistake made by some evolutionary theorists when discussing aspects of multi-level selection. There is no clear distinction that separates the two. Kin a kind of group and genes are levels of biological organization above and below the individual organism, after all. It might be difficult to end a debate without declaring a loser, but I hope we can move on soon.
In experimental high-energy physics, yes, collaborations can include hundreds of researchers, making author lists bloom to multiple pages, but this is not a CERN team hunting for the Higgs boson. Such a manifesto, weighted down by signatures, might be helpful when the issue itself is political, like convincing people that climate change is real and anthropogenic. I went through all the responses to the Nowak, Tarnita and Wilson paper no doubt this speaks to some masochistic streak in me. When a depressingly politicised area of science meets poor publishing practices, up surges a perfect storm of miscommunication.
But there are more things in heaven and earth than are dreamt of in your r, b and c. Bijma and M. DOI: Van Dyken et al. Damore and J. Journal of Theoretical Biology 31— Simon, J. Fletcher and M. Kin selection is widely-accepted orthodoxy. Group selection is a controversial theory with a long history of associated confusion. The fact that it adds nothing to inclusive fitness theory is hardly much of a point in its favour.
Kin selection work typically emphasizes close relationships — which is highly appropriate. Group selection work typically emphasizes more distant and numerous relationships — which is not appropriate at all. Kin selection seems to be better. No wonder it wiped out group selection as an explanation for altruism decades ago.
You can only find out what will probably happen by doing proper Evolutionary Dynamics calculations. I am not convinced in group selection nor in kin selection. I am convinced only in self selection. Since I suppose that brain is the part on which evolution works. Brain is very selfish in its function. Genes are responsible only for the structure of the brain.
Kin selection as commonly used and popularised relates to close relationships, but Inclusive Fitness Theory, as currently refined and which is the real theory here, not the subset kin selection framework , is not limited to close relationships, because r is no longer limited to genealogical relations. IF makes the same predictions about altruists helping other altruists as MLS does. And MLS applies to close relationships as much as more distant relationships.
Yes, r, is now shared phenotype, because that is what matters for selection. The Price Equation clearly demonstrates how it is the partitioning of variance at the within and between group levels that matters to selection. Not necessarily shared genes. This is why Hamilton updates his theory, and why IFT has been further revised to reconcile this oversight.
When r took on serious overhauls, I would say that is where IFT ran in to some issues. Certainly, the updating of r made IFT a general framework but not everyone followed this change. Very few know of it. In fact, few knew Hamilton even wrote other papers since then.
When that concept of r changed, many did not follow, and some [eg. Nowark et al. I for one accept these updates, but it is not hard to argue that once the fundamentals of a theory becomes overhauled, it is not just an update, but a new theory [consider kin selection theory as coined by Maynard-Smith and what that term means now]. At that point came equivalence, and complete niche overlap leading to heavy competition. This is simply an objective account of the history.
So using your logic, IFT should essentially be dropped as that was a specific framework which expanded under the influence of Price and his covariance perspective. Partitioning of phenotypic variance, however, is very intuitive and was there from the start, this is why Hamilton had his AHA moment [which many have no idea of]. So if you want group selection people to distinguish MLS from Kin selection, well thats easy.
Keep r as genetic relatedness and MLS will keep with variance partitioning, and problem solved. But I do not see how MLS needs to distinguish itself from kin theory. All I care about is that people have an understanding of the frameworks. The field of social evolution is plagued by those that rebrand old ideas as new, strong opinions backed up by little to no knowledge of the topic, and poor scholarly efforts to read the background literature.
Tim, I am not accusing you of this, it is a more general comment to why there is so much beefing in this field. There is as usual in such situations a variety of concepts and terms that are used in various ways by different authors, and changeably with individual authors, and so on. The models tend to be categorical when llfe may be much more fluid. The basic idea seems patently true, because whatever succeeds preferentially succeeds, and there are many paths to success, and we know they all are relevant.
He points out something else that may be quite important. If we fixate on individual narrow traits like pointy noses and Mendelize them as being due to single alleles, then things may seem quite simple and fit to a formal theory. Many or most traits are not monogenic, and the individual contributing alleles mainly have very low penetrance, but nothing stops one treating the trait essentially as if it were monogenic. As I understand his post, DSW discusses recurring traits in the population distribution of trait values to help get past the hypercategorical arguments at play in this dispute.
If traits of this sort are due to the effects of many contributing genes, each varying among contemporary individuals and over time , and most alleles having little individual effect, then we can develop a better understanding in terms of phenotypes than genotypes. The individual alleles come and go, but if there is some advantage, whatever its source s , in being toward one tail of the distribution, the distribution can move in that direction in a way that favors the group or individual without the net fitness effect at any one contributing gene being very tight or precise or even detectable in any serious way at any given time.
If being willing to help the contemporaries we might meet, even at some personal expense, is polygenic, then no two rescuers have the same rescuer genotype, even if the trait itself becomes more common. There need not even be a cost to the rescuer for his actions. Maybe the same phenotypes make better warriors as well as helpers multilevel effects! Limited space was available to deal with the myriad technical problems.
The same holds for several of the other Nature commentaries published at the same time, many of them by experimental biologists. The focus was on brief comment in the original venue. On the other hand more detailed technical commentary has been published, e. Lion et al. TREE , Gardner et al. One point that almost all the commentators myself included missed out on was what was highlighted by NTW reply, and by a careful re-reading of their original paper — they think that the neighbour-modulated version of inclusive fitness a mathematical convenience to aid analysis is actually the same as classical Darwinian fitness, i.
Tim Tyler. I do not want to single you out as an example of the lack of understanding the two theories but your response is certainly loaded with misconceptions of both. Both, are about how variation is partitioned in the population. IFT [as originally coined by Hamilton] uses the term r to demonstrate this.
It was originally envisioned that shared genes would have an indirect fitness consequence to the actor by going disproportionately more to its shared genes in others. This had since been updated, not by the MLS folks, but by IFT proponents for various reasons [genetic relatedness does not necessarily mean phenotypic relatedness and vis versa.
Relatedness is just one way to partition variance, while assortative interactions, culture, punishment and other mechanisms can do the same, if not even more strongly]. Thus the modern variant of, r, is actually a measure of how [phenotypic] variance is partitioned at the group vs within group level. MLS shows the direction and strength of selection at the multiple levels on a particular trait. IFT more or less conceptualizes the population structure in r, showing the effect a trait proportionately has on itself due to the distribution of like vs unlike phenotypes.
In other words, you can say an altruistic trait is disadvantageous within groups, but advantageous at the group level, and the relative strength of selection [partitioning of variance] at these various levels determines the balance of these forces, and the net effect. OR you can say, an altruistic trait does well if the positive effect it has on itself is increased because these effects are going proportionately more to other altruists [itself so to speak] because there is increased group vs within group level variance. Thus, the frameworks are entirely equivalent and uber general.
What happens between groups matters. Whether you want it in pill form or the syrup doesnt matter. A perfect example of how one can translate back and forth between frameworks is my paper [the role of multilevel selection in the evolution of sexual conflict Evolution] which is explicitly in MLS terms, and its companion paper [Sexual conflict in viscous populations: The effect of the timing of dispersal Theoretical Population Biology] by IFT folks who do the same thing but use IFT terms. I recommend you read up a bit more because your perception of both frameworks and the history of the field is off.
That being said it is 2am here so I hope this makes sense and I will leave it to others to add to my comments. The major thesis advanced by E. Wilson and Mark Pagel in their respective books involves human groups in which genealogical relatedness is low even in small hunter-gatherer groups and the balance between levels of selection is determined by other factors norms, policing, etc. Thus, the claim that the only important cases of group selection involve groups of close genealogical relatives cannot be sustained in my opinion.
But instead they take that well-understood observation amongst theorists and use it to argue that inclusive fitness theory is wrong. In particular, maternal care is not given based on shared phenotypes. Cuckoos live by exploiting this difference. However, it is based on heritable variation — just like all other forms of kin selection are.
Group selection proponents should work to distinguish their theory from kin selection, IMO. Equivalence between the two theories is not good for one of them. At the momemnt, group selection is looking a lot like a oogolrome. We know it is equivalent to 30 years of orthodoxy — what is less clear is what makes it worthwhile. Applying the idea to family groups — where relatedness is significant is what the kin selection folk do.
The long history of this is what has given group selection a bad name. Group selection had better have some impressive redeeming features if it wants to be taken seriously, after all these decades of causing this sort of problem. Incidentally, the idea that I. When genes are identical, in practice, they are identical by descent. Genes being identical by chance — without descent being involved — is so unlikely as to not be worth bothering about.
Modern inclusive fitness is perfectly fine. What Nowak et al. Very similar to how group selection gets misrepresented by many, including some of those authors. Many group selectionist rallied against that Nowak et al. In any case, we all study evolution so are well versed in what comes from competition. Such as 1 authoring papers as petitions is not the way to go.
I certainly have issues with how Nowak et al. I am not accusing all the author papers of this by any means at all, but coming from the group selection side of things, I was like oh someone misrepresented your framework in Nature? That happens to me so much I am confused what Group Selection is. Either way, we all must be knowledgeable of our field and take better care of our scholarship. Misrepresenting theories seems rampant in the field of social evolution and it is embarrassing for all of us, no matter what framework one prefers. Hopefully it improves.
Thanks not only for the well-done ETVOL-exclusive post, but also for the comments, which I read with interest and delight. Wilson should have teamed up with him in this way. It contains papers that deal with the evolution of replicators Szmathmary , individuals Michod , and animal Kitchen and Packer and human societies Maynard Smith.
Other papers treat conflicts and communities of interest between genes within the same organism Pomiankowski , between the sexes Lessells , parents and offspring Godfray , members of social groups Keller and Reeve , and species in ecological communities Herre. The properties and consequences of selection at different levels are discussed in general papers by Leigh, Nunney, and Reeve and Keller.
The book contains a large quantity of fascinating biological information and descriptions of clever theoretical models, and will undoubtedly be a very useful resource for researchers in evolutionary biology. One question that it raises, however, is whether there is a useful general theory of conflict that can be applied painlessly to solve particular problems.
My impression is that there is not; I can see very little in common between, for example, the application of ESS theory to parent—offspring conflict, and the use of population genetic dynamic modelling to study transposable elements or segregation distorters. The generalizations that are provided in the overview papers are, inevitably, bland and not very informative. I also have the impression that different topics are at very different levels of scientific development, in the sense of confronting theories with tests.
Topics such as sex ratio distortion and worker conflict in insect societies seem to be in a healthy state, where details of biology can be successfully interpreted by plausible models. In contrast, I am not convinced that the discussions of the evolution of replicators and individuals, at one extreme, or human language at the other, are more than ingenious speculation, with little hope at present for hypothesis testing. It is interesting that different authors contradict each other on some of these topics; Michod, for example, accepts the role of hypercycles in prebiotic evolution, whereas Szmathmary is critical.
Leigh believes that anisogamy arose to promote uniparental transmission of cytoplasmic organelles, whereas Lessells emphasizes the importance of the conflict between gamete number and gamete size. I would have liked to see this caution applied more widely; the development of hypotheses is essential for progress, but one should not confuse them with reality. Reprints and Permissions.