My pet theory about the human nose: breastfeeding

Why is the outer nose shaped as it is? Why don't humans just have two holes in the face, rather than this protuberance that we care so much to have the right shape of?

Here's the best illustration I've seen of my pet hypothesis:

Imagine that little baby had two holes rather than a nose. It would suffocate. That huge breast would block the air intake, making it impossible to breastfeed. But when the baby's nose is pushed from the front, slits are formed that enable the baby to still breathe through the nose.

In other words, humans have a proboscis so that they can breastfeed.

Some support: Cows, dogs, and cats have a slit to the side, making them able to breathe when their noses are blocked form the front.

A caveat: Snub-nosed monkeys have no external nose, but just two holes. Admittedly, they probably breastfeed. I wonder how they do it. They also sit with their faces downwards when it rains, to prevent rain from entering their nostrils. I wonder if the olfactory abilities of these monkeys are reduced?

More on the variation of nose shapes in humans and the evolutionary origin of the human nose, but nowhere can I find anyone suggesting a link between nose-shape and breastfeeding.

Carnival of Evolution #59 is up

The 59th edition of Carnival of Evolution is now up at DNA Barcoding.

Letter from the Doctor.

CoE needs hosts for the next editions, and you could host the 60th one! It's a thoroughly rewarding experience, and your blog will get lots of exposure. Plus, it's really very little work. Interested?

P.S. 

Knock, knock.

Who's there?

Doctor.

Doctor who?

or 

Knock knock.

Who's there?

How the hell did you know?

Can we predict evolution?

Evolution is not predictable, right? It has famously been said that if we were to rerun the tape of life, it would be very unlikely that something like humans would evolve again. I could add that, surely, on other planets suitable for life it would be highly unlikely that organisms looking identical to us would evolve. However, I personally would not be totally surprised if there were intelligent bipedal quadrupeds on other planets somewhere, but this is very little beyond pure speculation (I could talk about it at length - but that is not the same as writing about it).

One of the problems with predicting evolutionary outcomes is that the number of influencing factors is huge. Physics is a field of science where predictions can be very accurate - just think of sending rovers to Mars: the precision needed for that to succeed is daunting, and is a testament to the success of physics. However, this success stems from the simplicity of the physical systems that people have studied. Newton studied objects falling, which is a pretty simple thing with few factors influencing the outcome. The only relevant factor involved is gravitation, and it was fairly easy to achieve good predictability. The factors influencing the rover on its way to Mars are actually quite few as well. Biologists have a much harder time because the systems the first studied were so much more complex. Living things are just that much more complicated than a dead object falling (even if it is a living apple).

Evolution is driven by random changes to the genome and environmental factors. That's it. The problem is just that those are both highly unpredictable, whereas in physics things are easier. But here is the point I want to make: It's only easier in physics because physicists elect to study systems that are simple. There is a great joke where a physicist is able to predict the winner of any horse race to multiple decimal points - provided it was a perfectly elastic spherical horse moving through a vacuum (Wikipedia). Physicists don't contend with studying what is actually there in its messy reality, but instead reduce the problem to one that can be solved. Even though heating a pot of water involves many individual particles, boiling can be predicted with high accuracy, but only if it assumed that a meteor isn't going to crash the kitchen! Externalities are ignored and assumed not to happen. And that works well for physics, because that assumption is safe in many systems. But it is not safe at all in evolution.

The number of external effects that drive evolution is immense. But trying to predict evolution by starting with a model that includes everything is never going to work. First we must understand the simplest cases - in an approach identical to the one that has made physics so successful. We have to do this even if there is no system that actually evolves in this sort of vacuum in nature. Once we understand that system, we can build on it by adding more layers of complexity. Perhaps then one day mainstream biologists will be happy...

Population Size
The core entity of evolution is the population. It is the population that evolves, not the species or the individual. And the first thing to know about a population is its size, N. The size is the main determinant of what will happen to new mutations, such as the probability of fixation (i.e., the mutation existing in all individuals) and the strength of selection (governing whether a mutation will be under the influence of selection, or just genetic drift - note that there is always drift, which comes from the stochasticity (randomness) that is inherent in the evolutionary process, but sometimes it matters little compared to selection, which completely dominates in the case of an infinitely large population).

Mutation Rate
The second parameter to know is the rate at which new mutations appear, the mutation rate, µ. If there are no mutations (no more realistic in nature than an infinitely large population size), then evolution comes to a halt when genetic drift or natural selection has eliminated all variation. If µ is very large, the the population will experience a mutational meltdown and the loss fitness due to the accumulation of harmful mutations. The product of the population size and mutation rate is called the mutation-supply rate, µN. This quantity determines how many mutations occur: if it's low, then there are few mutations. In much of evolutionary theory it is assumed that the mutation-supply rate is so low that each mutation appears and goes to fixation (or most likely is lost) before the next mutation appears. This makes mathematics much easier, but it is not a realistic assumption. Rather, pretty much all natural populations have a mutation-supple rate large enough that there is always many different mutations present in the population. This is true for humans and for bacteria - and especially for viruses.

Lots of advances have been made in evolutionary theory (population genetics) given only these two parameters. But there is another that makes a huge difference, which describes what effect mutations have.

Fitness Landscape
The fitness landscape is a map where fitness is given as a function of the genotype or the phenotype. In other words, this function describes the expected reproductive success of every type of organisms in the population. If the fitness landscape is known, then the effect of every mutation is known, and it can be predicted statistically how the population will evolve. If the landscape is smooth (see Fig. 1) then there is only one outcome possible: the population will ascend the peak and then stay there. But if the landscape is rugged, then the population risks getting stuck on a local peak (at least for a very long time), or it may be fortuitous enough that it finds the global peak.

Figure 1. Smooth and rugged one-dimensional fitness landscapes. Both are simply fitness given as a function of either genotype or phenotype. Smooth landscapes have a single peak and the dynamics on it is very predictable: for non-zero mutations rates, the top of the peak will be located sooner or later. The only exception is if the mutation rate is very high, in which case all offspring have deleterious mutations that take them off the peak, aka as a mutational meltdown. The rugged landscape have multiple peaks separate by valleys of lower fitness. In the NK landscape rugged landscapes also have a greater range in fitness than smooth landscapes. This is an effect of pleiotropy, which in NK is coupled to the K-parameter, which determines the amount of genetic interaction. P.S. Not sure why I didn't indicate the axes here. Amateur! Again, it's fitness on the vertical axis, and genotype or phenotype on the horizontal axis. I could add, though, that a picture like this is more consistent with fitness as a function of a continuous phenotype (e.g., body size), while functions of genotype will most often be discrete. From Østman and Adami (2013).

Evolutionary dynamics in rugged landscapes are much harder to predict. That goes for the endpoint of evolution (the genotype or phenotype that the population ends at), which is easy to predict in smooth landscapes, and it is also true for the path that is taken. In one dimension the path is not hard to predict (if the population start to the left in the smooth landscape in Fig. 1, then it will move to the right until it hits the peak), but if there are many paths, then the situation is not so simple. If we look at the fitness landscape in Fig. 2A, which is reconstructed from Khan et al (2011), we see that there are many paths that can lead from genotype 00000 to 11111. Note that in this figure fitness is given for each of the 32 genotypes, but are plotted as a function of how many mutations away they are from the (arbitrarily chosen) 00000 genotype.

A population that starts at 00000 will end up on 11111, but it can take a number of routes there. Because the fitness gains are highest going through 00010 first, and then through 00110, 10110, 11110 to 11111, that route is most likely, but because evolution is a stochastic process (mutations are random and high fitness makes it more likely that you get to reproduce, but not certain), other routes are possible. In this example, Khan et al. actually found that the path taken was via 01000.

Figure 2. Two fitness graphs where fitness are given by the genotype. The genotype consists of five loci - each of which can take the value of 0 or 1 - and the horizontal axis therefore cannot contain that information. Instead the axis shows the number of mutations each genotype is away from an arbitrarily chosen "wild-type". An edge indicates a one-mutant connection between genotypes.  (A) Taken from Khan et al. (2011), evolution in this landscape is easy to predict. With a population starting at 00000, genotype 11111 will eventually be located. There are no other peaks than 11111 that the population can get stuck on. Which path will actually be taken is a different matter: even though it is most likely the population will go to 00010 first, and then 00110, 10110, and 11110 to 11111, the actual path taken was different, going through 00001. (B) This instance of an NK fitness landscape is rugged, with three distinct peaks at 11110, 11101, and 00000. Starting a population at 11111 it cannot be said with certainty where the population will end up. Most likely the population will go though one or both of the local peaks at 11110 and 11101. If the mutation-supply rate is low there won't be enough mutations available for the population to cross the valley going through genotypes 10101 or 01001. However, for higher mutation-supply rates (which can be achieved both by increasing the population size and the mutation rate) the higher number of mutations will ensure that the valley is eventually crossed (without having to wait for eons), and the global peak is reached.


We can of course discuss the relevance of different paths. Does it really matter which path is taken? Where the population ends up seems more relevant, but suppose the population is a virus that we are fighting with anti-virals. In that case, we'd like to kill all the viruses, but if they evolve resistance, we could perhaps combat them with targeted drugs if we know which path they were going to take. 

Figure 2B shows a rugged landscape with several peaks (at 11110, 11101, and 00000). If the population starts at 11111, it is most likely to go through 11110 and/or 11101. However, if the supply of mutations is low (WM, weak-mutaiton regime), the population risks getting permanently stuck there, and thus not able to locate the global optimum at 00000. By "permanently" I mean that the population will sit there for a very long time. Eventually it will move up, but it can take longer than other events that can change the landscape, and, if the fitness of those suboptimal genotypes are too low, the population can go extinct before they are able to escape to higher fitness. Fitness also affects population size, so that a population sitting on 11110 would in this case be about 10% lower than if it were sitting on 00000. In the simulation shown in Vid. 1 the population size is fixed at N=1,000, so this effect does therefore not occur. The mutation rate is very high, so the population is able to escape 11101 and 11110 by crossing the valley to reach 00000.

Video 1. Evolution in a rugged landscape. A population of 1,000 individuals evolving on a fitness graph all starting at genotype 11111. Mutation rate is 0.1 per generation (= chance to have one mutation; no double-mutations allowed here). The simulation is a Moran process (one individual is replaced by the offspring of another every update). The area of the circles are proportional to the number of individuals at that genotype.

One way to quantify how predictable evolution is is using information theory.  The Shannon entropy was used by Szendro et al. (2013) to quantify how predictable evolution is in simulations of evolution in the Aspergillus niger fitness landscape (Fig. 3). One simply counts the frequency, p_i,  of each endpoint in a large number of replicate simulations (here, 100), and calculates the entropy as 

.

The lower the entropy S is, the higher predictability is. When all states are the same, p_i=1, and S=0.

Figure 3. Predictability of endpoints of evolutionary paths measured using entropy. When the entropy is  low, predictability is high. Entropy is basically a measure for how uniform the results are: if all endpoints are the same, then entropy is zero. The different colors are for different mutation rates (black highest). Predictability is given as a function of population size (A), mutation-supply rate (B), and rate of double-mutants (C). The finding is that predictability is not monotonic, that is, at very high population sizes predictability goes down again (entropy increases). d=4 indicates the Hamming distance from the global optimum, which is easier to find the more mutations there are, which is why predictability is higher for larger N. Szendro et al. (2013) explains this decrease in predictability as "a consequence of increased appearance of double mutants", by which they might mean that the number of mutations is so high that the mutational load (decrease in average fitness due to deleterious mutations) makes finding the global optimum harder.

Evolution in smooth landscapes is very predictable (in terms of endpoints), while rugged landscapes can decrease predictability because the population can get stuck on different fitness peaks (which doesn't mean that a rugged landscape can cause speciation - for that, other factors are needed, like reproductive incompatibilities between different genotypes or negative frequency-dependent selection). Just as is seen in Fig. 3, increasing the mutation rate (up to a point) in rugged NK landscapes increases the chance that the population will find the global peak (Østman et al,, 2012).

Yes, we can predict evolution, but to achieve high predictability, certain conditions have to be met. A large mutation-supply rate helps, as does low ruggedness of the fitness landscape. So too does static landscapes; if the fitness landscape changes on time-scales comparable to the population dynamics, then predictability will suffer. There is very little research done on such dynamic fitness landscapes and how they affect evolutionary dynamics. One obvious question to tackle before answering this question is just how the landscape changes over time. Not much is known about this in natural populations, but it could be studied computational for different classes of changes, like peaks suddenly disappearing vs. more subtle changes in fitness values.

In summary, evolution is predictable if we know population size, mutation rate, and the fitness landscape.

References
Khan A, Dinh D, Schneider D, Lenski R, and Cooper T (2011). Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population Science, 332 (6034), 1193-1196 DOI: 10.1126/science.1203801

Szendro IG, Franke J, de Visser JA, and Krug J (2013). Predictability of evolution depends nonmonotonically on population size. Proceedings of the National Academy of Sciences of the United States of America, 110 (2), 571-6 PMID: 23267075

Østman B and Adami C (2013). Predicting evolution and visualizing high-dimensional fitness landscapes in "Recent Advances in the Theory and Application of Fitness Landscapes" (A. Engelbrecht and H. Richter, eds.). Springer Series in Emergence, Complexity, and Computation (to appear). arXiv: 1302.2906v2

Østman B, Hintze A, and Adami C (2012). Impact of epistasis and pleiotropy on evolutionary adaptation. Proceedings. Biological sciences / The Royal Society, 279 (1727), 247-56 PMID: 21697174

The war on science™

According to Science Left Behind (book review), there's a war on science, and it isn't raged by conservatives alone. Progressives are also ideologues who refuse to listen to the science - it's just other areas that they dislike.

Conservatives:

• Climate change (science: it is happening, and is caused by humans)
• Evolution (science: it is true)

Progressives:

• Homeopathy (science: it doesn't work)
• Vaccines (science: they work and are do not cause autism)
• GMOs (science: they are not harmful)
• Organic foods (science: they are not always better)
• In vitro meat (science: it is fine, and better in so many ways)
• Biological gender differences (science: there are some)

Anything else?

My guide to better talks

I like public speaking, and would like to be better at it. Two rules I have been living by are:

1. Love the words that you speak
2. Always have something to say

Number 2 is a general rule, as it means that you should have done something, thought about something, and have formed an opinion that you can speak about.

All very well, until the day I saw myself on video giving a talk that I had not practiced, thinking I'd be fine and convey the points I wanted to. I did convey those points, but was also horrified at some things that I did. I made some observations, and here is the list to remedy those deficiencies:

3. Stand up straight (I have bad posture)
4. Speak clearly (no mumbling)
5. Finish sentences (no trailing sentences)
6. Speak precisely (say only what you need to say)
7. Shave and get a hair-cut (don't look like a bum)
8. Be modest (don't be smug)
9. Arms down (or think about your gesturing, at least)

3 to 9 apply to me, and are not general rules. I do think different styles of speaking can allow for breaking each of these, but in general they are good to follow. Either way, I really need to work on them.

Here's Hans Rosling, my favorite speaker.

 

Professorship in France

On EvolDir there is this job-posting:

*The lab of "Biometry and Evolutionary Biology" UMR CNRS 5558, (***University of ******Lyon**, France)* offers a permanent position for 2013 : Assistant professor (Maître de conférences) in Modelling approach in Genetics-Ecology *

*Teaching :Mathematics**and statistics applied to biology, animal biology*

The applicant will join the teaching staff of the "Agronomy section" of the Biotechnology Departmentof the Technology Institute at Lyon 1 University.He or She will teach mathematical functions and data analysis (1^st year students). The applicant may also be called upon to teach zoology (anatomy and histology of mammals and insects) and genetics (1^st year students). Finally, He or She will participate to the supervision of the numerous tutoring works made by the students of the 2^nd year of the Agronomy section (breeding of animal laboratory models, experimental methodology, reports...) and to the monitoring of the students during their work placement.Beyond teaching, He or She will engage in collective responsibilities of the department and be willing to develop new vocational training.

*Research: Evolution in fluctuating environments: modelling approach in Genetics-Ecology*

Understanding fast evolution of traits (morphological, behaviour, life histories) and population evolvability in fluctuating environments needs on taking into account genetic architecture of these traits in complement to phenotypic approach. The aim is to build models (genetic-ecology) to study how the genetic architecture influences trait evolution. Temporal and spatial components of the environment will be considered. The candidate will be theoretician and modeller with good experiment at the interface between theory, modelling and biological data. He/she will use the data basis (insects and vertebrates) of the evolutionary ecology department in order to build realistic models. He/she will interact greatly with the field ecologists. The candidate must be very familiar with the concepts and modelling/programming tools in evolutionary ecology and, quantitative and population genetic and be able to connect teaching (including animal and vegetal biology) and research activities.

Seriously, what is up with that? The work sounds really interesting, and it sounds like they have really good data. However, this reads like a postdoc position in some professors's lab - not like an assistant professor position! It's a permanent job, and yet they want to dictate this level of detail for someone who is supposed to be an independent researcher. I want to scream that this is not acceptable, but I suppose I should just be glad that things are much more liberal in academia in the United States.

Titles in evolution

Can you spot the odd one out in today's list of titles in evolutionary biology? The category is open, though. For example, the journal Evolution insists on its titles having all-caps, which annoys me like an oyster (but since there are two of those...).

• Conflictual speciation: species formation via genomic conflict
• Predictability of evolution depends nonmonotonically on population size
• Ecological strategies shape the insurance potential of biodiversity
• Ecological speciation along an elevational gradient in a tropical passerine bird?
• Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load
• Can a collapse of global civilization be avoided?
• Long-term culture at elevated atmospheric CO2fails to evoke specific adaptation in seven freshwater phytoplankton species
• MIGRATION ENHANCES ADAPTATION IN BACTERIOPHAGE POPULATIONS EVOLVING IN ECOLOGICAL SINKS
• RUNAWAY SEXUAL SELECTION LEADS TO GOOD GENES
• Evolution of sperm structure and energetics in passerine birds
• Wormholes record species history in space and time
• Who Speaks with a Forked Tongue?
• Evolutionary mode routinely varies among morphological traits within fossil species lineages
• The effect of spontaneous mutations on competitive ability
• Adaptive Genetic Variation on the Landscape: Methods and Cases

A general template explaining how different factors influence adaptive genetic variation in the landscape over evolutionary time (Schoville et al., 2013, Adaptive Genetic Variation on the Landscape: Methods and Cases).

Weird comments

I get quite a few anonymous comments to random posts. They are random in that there is no apparent correlation between the content of my posts and their comments.

Examples:

Anonymous has left a new comment on your post "The trouble over inclusive fitness theory and euso...":

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Anonymous has left a new comment on your post "Carnival of Evolution statistics":

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Clearly the point is to promote some site (yes, I have changed the links). But why these robotish comment with no relevance to my blog posts?

None of these comments ever get through, because I moderate submitted comments to posts over a week old. And when they do get through to newer posts, they always delete them themselves right away. Why?

I really wish I knew.

Empty talk about the evolution of complexity

PZ Myers has a post on the evolutionary origins of complexity: αEP: Complexity is not usually the product of selection I find it frustrating that people talk about terms that they don't clearly define, and assume everyone agrees on. Especially about complexity, which is hard to define, and I know not everyone has the same idea of. I'll just quote my comment on Pharyngula:

But, you have not quantified complexity, let alone say when there was an increase in it in the hypothetical example you give. If you don’t do this, you can’t talk about the evolution of complexity; it becomes a guessing game what we are talking about, and there is no chance that everyone will think of complexity as the same thing.

On top of that, there seems to be no distinction made anywhere between ‘complexity’ and ‘complex traits’. They need not be the same thing. Without defining complexity here(!), I’ll say that complexity can indeed easily arise by neutral processes, whereas complex traits cannot (but does have neutral and random processes involved) – it requires selection. And with that you’re going to ask me for a definition of a trait, so here is one: A single measurable component of the phenotype that has a function.

The key word here is function, without which I don’t know of any that can evolve without selection. Not selection every step of the way, as random processes are required (at least that’s how it occurs in nature), but selection at some point. The moment the trait acquires function, it becomes selected for.

On the other hand, ‘genomic complexity’ may not describe the state of a trait, but rather is the idea that the genome has many components that are intricately connected – which can arise by neutral processes.

Title in evolution quiz

For those unawares (prolly all), I post these titles in evolution i) as a reminder to myself when I skim the numerous eTOCs that I get in my inbox every week, ii) to point out that evolutionary biology is a very active area of research (while creationism is not), and iii) to share the awesomeness of evolution.

Today it also comes with a quiz.

• Wormholes record species history in space and time
• Quasispecies Dynamics of RNA Viruses
• Genetic background affects epistatic interactions between two beneficial mutations
• Epistasis between mutations is host-dependent for an RNA virus
• Evolution of clonal populations approaching a fitness peak
• Competition and the origins of novelty: experimental evolution of niche-width expansion in a virus
• Stochastic effects are important in intrahost HIV evolution even when viral loads are high
• Variable evolutionary routes to host establishment across repeated rabies virus host shifts among bats
• eplaying the Tape of Life: Quantification of the Predictability of Evolution
• Phenotypic landscapes: phenological patterns in wild and cultivated barley
• EVOLUTION OF TRANSCRIPTION NETWORKS IN RESPONSE TO TEMPORAL FLUCTUATIONS
• Are elder siblings helpers or competitors? Antagonistic fitness effects of sibling interactions in humans
• Ecological selection as the cause and sexual differentiation as the consequence of species divergence?
• Adaptation to a new environment allows cooperators to purge cheaters stochastically
• Fixation of mutators in asexual populations: the role of genetic drift and epistasis Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations

Guess which of the papers above this figure is from:

54th Carnival of Evolution is up

54th edition is up at ideonexus.com: Carnival of Evolution #54: A Walkabout Mount Improbable.

And it's a super-fancy one, so don't miss it, and let everyone else know, too.

Titles in Evolution overload

There are simply too many interesting papers published in evolutionary biology to keep up with. Not even just reading the abstracts is feasible. Here's a sample of what I find the most interesting from the last couple of weeks:

• Analyses of pig genomes provide insight into porcine demography and evolution
• Non-random gene flow: an underappreciated force in evolution and ecology
• Strengths and weaknesses of experimental evolution
• Gene duplication as a mechanism of genomic adaptation to a changing environment
• Revisiting an Old Riddle: What Determines Genetic Diversity Levels within Species?
• Selection of Penicillin-sensitive Mutants of Escherichia coli following Ultraviolet Irradiation
• Understanding specialism when the jack of all trades can be the master of all
• How does adaptation sweep through the genome? Insights from long-term selection experiments
• Evolutionary layering and the limits to cellular perfection
• The effects of competition on the strength and softness of selection
• Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments
• From nature to the laboratory: the impact of founder effects on adaptation
• Spatially explicit models of divergence and genome hitchhiking
• Why Transcription Factor Binding Sites Are Ten Nucleotides Long
• Epistasis as the primary factor in molecular evolution
• The spatial architecture of protein function and adaptation
• The effects of stochastic and episodic movement of the optimum on the evolution of the G-matrix and the response of the trait mean to selection
• TOWARDS A GENERAL THEORY OF GROUP SELECTION
• PLEIOTROPY IN THE WILD: THE DORMANCY GENE DOG1 EXERTS CASCADING CONTROL ON LIFE-CYCLES

Titles in creationism:

• Mammalian Ark Kinds

Check it out here: Answers Research Journal. They estimate that there are 137 extant kinds, which means that they must admit to substantial diversification and evolution since the Flood...?

Knowing what I know now...

I'm an evolutionary and computational biologist doing my second postdoc at Michigan State University. I have learned all sorts of thing in this short career, and Jeremy Yoder has asked for advice for a new blog canival.

If I knew then what I know now, I would have...

Focused more on my writing skills very early on. At least as early as my graduate degrees at KGI and UCSB, but perhaps I should really have gotten more into writing during my undergrad in Copenhagen. No one ever told me that being a scientists really amounts to being a writer. I have done nearly nothing but reading and writing for at least six months now, save for giving some talks at meetings and writing 32 lines of code. Write even if you have no data and no conclusions. Write your thoughts down on what you read, what you do in lab, and then it will be easier to write the thesis and papers when it really counts.

Read more. As a scientist, reading is treading water. If you stop, you drown. It's a never ending game, and it is the only way to keep abreast with what is going on. Going to talks is fine, but simply not enough. You must read constantly, or you will be left behind. Often it just means reading abstracts, sometimes also looking over figures (and reading captions) - not that I count, but I count reading abstract and figures as having read a paper. Sign up for eToCs from the major journals in your field. I recommend: Nature, Science, PNAS, Proc. R. Soc. B, Genetics, Evolution, Journal of Evolutionary Biology, The American Naturalist, Journal of Theoretical Biology, Frontiers in Evolutionary and Population Genetics,  PLoS Biology, PLoS Comp Bio. After my first year in grad school, I read the advice from a senior scientist that one should spend the entire first year of grad school mostly reading. I did read a lot, but wish I had read more.

Stopped taking myself so seriously. Actually, I haven't done that in years, but I do think this is invaluable advice. It's just science, after all. If I am wrong about the prevalence of epistasis in adaptation, nobody is going to care. No bridge will collapse and no one is going to die of a misdiagnosis. Keep that in mind, and enjoy yourself. Unless you're an engineer or an M.D, in which case you should stop reading this blog and get back to fukcing work already, or I'll sue your ass off!

Crocodilian relatives that walked upright?

I seriously have trouble believing this. Can anybody shed some light?

It's from the Royal Ontario Museum in Toronto. Wikipedia on Crurotarsans (spelling?) says nothing of it.

Pleiotropy saves the day for evolving new genes

What is the origin of new genes? In order to do new stuff, new genes are needed. Where do they come from, then?

Horizontal gene transfer (HGT) - direct transfer of a gene from one organism to another - is rampant within bacteria, so they may gain new function this way. However, that does not explain how the gene came to be in the first place.

Neofunctionalization: If the function is carried out by the original, the copy is free to evolve a new function by point mutations (etc.). However, such copies are much more likely to degrade by those mutations and lose the original function, thereby becoming a pseudogene.

Subfunctionalization: If the gene is pleiotropic, i.e. it has more than one function (expressed in more than one trait or cell-type or at different times), then the gene and its new copy can turn off gene expression differentially such that they share the set of functions. However this doesn't allow either much chance for evolving new function by mutation.

So what to do?

Näsvall et al. gave me this present for my 40th: Real-Time Evolution of New Genes by Innovation, Amplification, and Divergence.

They describe a new model/mechanism by which duplicated genes can retain the selection pressure to not succumb to deleterious mutations. They call it the innovation-amplification-divergene model (IAD).

IAD works like this: A gene initially has one function only (A). Then some genetic changes makes it also have a new function, b, which at first is not of too great importance. Then some environmental change favors the gene variants with the minor b-function (the innovation stage). This is then followed by duplication of the gene, such that there are now more than one copy that carries out A and b (the amplification stage). At this stage there is selection for more b, and at some point genetic changes in one of the copies results in a gene that is better at the new function, B. At this point, selection for the genes that do both A and b is relaxed, because the new gene (blue) carries out the new function. The original gene then loses the b function, and we are left with two distinct genes. Viola!

In other words, the green gene first becomes pleiotropic, is copied, followed by divergence, and then loss of pleiotropy. (How they could fail to mention pleiotropy in the article is beyond me.) The crucial feature is that at no point is the gene or any of the copes under no selection; there is always selection for them to be retained, so gene loss never occurs (pseudogenes are not created).

The researchers then look at a preexisting parental gene in Salmonella enterica that has low levels of two distinct activities that allows them to grow without the amino acids histidine and tryptophan, respectively.

Multiple evolutionary trajectories recovered through IAD. The x-axis indicates the HisA activity (assayed as growth rate in minimal glycerol medium with added tryptophan); the y axis indicates the TrpF activity (assayed as growth rate in minimal glycerol medium with added histidine). (A) Evolution of specialist enzymes (yellow) in which one activity is improved at the expense of the other. (B) Evolution of specialist enzymes (yellow) after initial evolution of a generalist enzyme (blue).

The figures here show how the generalist gene evolved to become specialists genes with increased function, doing better without both amino acids.

This is a model that explains how a gene with two functions can evolve to become two genes with distinct function under continued selection. It is this last part about selection that makes it novel, but it relies on the idea that the original gene had already evolved two distinct functions - that it was pleiotropic.

Pleiotropy comes from the Greek πλείων pleion, meaning "more", and τρέπειν trepein, meaning "to turn, to convert". It designates the occurrence of a single gene affecting multiple traits, and is a hugely important concept in evolutionary biology.

Reference:
Näsvall J, Sun L, Roth JR, and Andersson DI (2012). Real-time evolution of new genes by innovation, amplification, and divergence. Science (New York, N.Y.), 338 (6105), 384-7 PMID: 23087246

Carnival of Evolution statistics

David Morrison just hosted Carnival f Evolution #52, and now he has written a post with lots of statistics of CoE: The network history of the Carnival of Evolution.

In short, we're doing quite well compared to many other carnivals who have gone extinct. This is especially true for science carnivals, of which CoE is the only active carnival listed on BlogCarnival.com.

The steady growth of CoE through time. "Fortunately, the number of posts has shown a steady upward curve, as indicated in the sixth graph, although not always at the one-blog-post-per-day rate set in the earliest days. However, over the past 20 Carnivals there has been an average of 1.06 blog posts cited per day of passing time, so we are certainly holding our own."

Titles in evolution - and in creation?

Here we go again with the new articles on evolution. This is just a very small sample that I chose out of interest from the last couple of weeks - and just from a few journals that I get eToCs sent from. In the meantime there has been nothing in Answers Research Journal, and I don't know where else to check. If you do, please let me know.

• Variation in personality and fitness in wild female baboons
• Biodiversity tracks temperature over time
• Epistasis as the primary factor in molecular evolution
• Aposematism and the Handicap Principle
• Turning Back the Clock: Slowing the Pace of Prehistory 
• Physico-Genetic Determinants in the Evolution of Development 
• The spatial architecture of protein function and adaptation
• Complex brain and optic lobes in an early Cambrian arthropod
• Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments
• Mutational meltdown in selfing Arabidopsis lyrata
• Aging: An Evolutionarily Derived Condition
• What could arsenic bacteria teach us about life?
• Genomic Variation in Natural Populations of Drosophila melanogaster
• Clonal Interference in the Evolution of Influenza
• Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts
• Criticality Is an Emergent Property of Genetic Networks that Exhibit Evolvability

Ochman on bacterial evolution

Yesterday I went to the annual Thomas S Whittam Memorial Lecture here at MSU. Howard Ochman talked about "Evolutionary Forces Affecting Bacterial Genomes", though he had changed the title to "Determinants of Genome Size and complexity.

Based on research in his lab had two conclusions about the evolution of bacterial genomes:

• Genome size is drifting
• GC-content is under selection

The very tight correlation between gene content and genome size (i.e., there is a linear relationship between number of genes and length of the genome) is driven by genetic drift, and not selection. People often say that small genomes are selected for, but clearly there are not. This is in part caused by a bias towards deletions (compared to insertions). I asked him why there is this bias, and he did not have an answer. (Well, he gave me an answer, but he also acknowledged that it didn't get to the point.) The common ancestor had a large genome, and many bacteria - particularly pathogens and endosymbionts - have subsequently lost DNA resulting in a reduced genome size (Kuo et al, 2009, but see Kuo and Ochman, 2010).

There are trends in the GC-content (amount of guanine and cytosize in the DNA) that differs among bacterial taxa. But closely related species have similar GC-content, and it turns out that genetic drift is not responsible for this, but that it is driven by selection. "Escherichia coli strains harboring G+C-rich versions of genes display higher growth rates" (Raghavan, 2012).

Genome size and abundance of pseudogenes correlates with the size of the effective population size: a larger N_e gives larger genomes, while smaller N_e results in smaller genomes. Pseudogene abundance is less straightforward, with the largest and smallest genomes and N_e both having few pseudogenes, but those intermediate in size having many.

References
Kuo CH, & Ochman H (2010). The extinction dynamics of bacterial pseudogenes. PLoS genetics, 6 (8) PMID: 20700439
Kuo CH, Moran NA, & Ochman H (2009). The consequences of genetic drift for bacterial genome complexity. Genome research, 19 (8), 1450-4 PMID: 19502381
Raghavan R, Kelkar YD, & Ochman H (2012). A selective force favoring increased G+C content in bacterial genes. Proceedings of the National Academy of Sciences of the United States of America, 109 (36), 14504-7 PMID: 22908296

Genotype-phenotype maps and mathy biology

I'm reading a book chapter by Peter Stadler from 2002 called Landscapes and Effective Fitness [1]. It has this absolutely gorgeous figure:

 I love it. But just before this figure he has this equation:

I hate it. I hate it because all it says is that each type, x, is at a frequency P_x of the total population, so those P_x sum to one. But of course. I just don't think this kind of writing is conducive to discourse, because in biology there is already a huge gap between the majority who don't read (and cite) papers with equations, and those who write them. So why muddy the waters with equations like this that says next to nothing?

However, I reiterate (and is why I'm reading the chapter) that this figure of a genotype-phenotype-fitness map is super cool.There are many more different genotypes (the genetic make-up of an organism) than there are different phenotypes (the combined physical attributes of the organism). This must be so, because we now know that each trait is affected by many genes; it takes more than one gene to make a trait (there may be exceptions where only one gene encodes a trait).

The figure is a conceptual map, but real g-p mapping is sort of the holy grail in evolutionary biology at the moment. With a real map like in hand evolutionary dynamics can be predicted, and we will be able to say which genetic changes are required to change the phenotype. However, realistically we can only map a very small portion of the genotype on to the phenotype, and there even seems to be some confusion about what the proper answer is to the question of what the genotype-phenotype map looks like. Hopefully the answer won't be too mathy...

References
[1] Peter F. Stadler, & Christopher R. Stephens (2003). Landscapes and Effective Fitness Comm. Theor. Biol DOI: 10.1080/08948550302439
[20] A testable genotype-phenotype map: Modeling evolution of RNA molecules. In: Lässig, M. and Valleriani, A., editors, Biological Evolution and Statistical Physics, pp. 56–83. Springer-Verlag, Berlin, 2002.

How to be a good speaker

Bjørn's two rules of being a good speaker:

1. Love the words that you speak
2. Always have something to say

An engaged speaker is more enjoyable to listen to than a bored one. If you love the words as they leave your mouth, you are more likely to engage the audience. Caveat: we all hate someone who loves to speak - too much. I am here talking about giving a presentation, where you are expected to deliver a monologue. In dialogue, be a good listener.

If you don't have something to say, don't give a talk. As a scientist, this is the same as not having done anything, in which case you are not doing your job. But I also mean this in a more general sense: live life learning, and have your lessons to share. If not, then it's a waste, in my opinion.

I'm at the 16th Evolutionary Biology Meeting in Marseille, and I trust I don't need to say that some of the presentations don't measure up to the science behind them. And that's a shame; people being bored listening to your talk when they really should be excited about the science. It's a total myth that all one needs to do is do good science, and people will be interested in your talk. Rather, unless it is the something you are supremely interested in (which is probably only a small fraction of what you hear at conferences and seminars), then people tend to lose interest, tune out, and sometimes even feel antipathy for the speaker.

There are other things a speaker can do, but those are not my rules.

I am speaking tomorrow evening on the Impact of Epistasis and Pleiotropy on Adaptation.