Another day, another panspermia article. So transistor density and publication frequency follow a very strict exponential rule and that rule can in turn be used to trace the time elapsed to their origins. Most systems though don't follow such a rule, and if they do they generally don't at the extremes of the graph (near the beginning and after a long time).
That said, the estimated 9.7 ± 2.5 billion years for life to evolve is at least within an order of magnitude correct, which makes it pretty cool. Also, we don't know how long abiogenesis takes and through which stages (presumably: many) the whole thing has to go until even the most basic building blocks as we know them today are set up. So you can add X billion years on top of that estimate while you're at it.
Does that prove anything? Absolutely not. But even without evoking the tiring panspermia hypothesis this result is interesting, because the result is not completely off. It means that, within a certain period at least, the complexity of life does follow this exponential rule. Clearly, the formula needs to be tuned (based on evidence, not on open-mouth extraterrestrial finger-pointing), but it's a great start. I would venture that a more detailed genetic analysis of relatively recent organisms might result in a curve that is much more accurate.
Assuming something will always double within a certain interval is not detailed enough as a model for this.
There is usually not a lot of actual science involved. The motivations for presenting panspermia papers are in most cases philosophical in nature and it shows. This paper, too, implies that's what happened even though the accuracy of the model is highly dubious for a number of reasons.
Compare that to when Hubble first attempted to calculate the age of the universe by measuring the history of its expansion. It was quite a similar method: simply roll the equation back in time to where the universe's size was zero. He got a result that was lower than the geologically known age of the Earth, in fact we now know he was off by a factor of ten! Refreshingly, people didn't jump to conclusions though and in time everything fell into place. Hubble had fallen victim to a systematic measuring error. We are also now pretty certain that the expansion of the universe was not always linear. Hubble's method is still valid and impressive, but the tools and models needed to be refined. It's easy to see that the same applies to this finding.
There's discussion of measuring DNA complexity; issues in extrapolation (there's evidence suggesting recent increases are hyperexponential, which would push back the origin of life even further); section 8 notes function complexity has increased much faster with the "mind" (as in what birds and mammals have) which is not reflected in DNA complexity (chimp-human encephalization rate is x100 faster).
I love the fresh perspective of not tying the origin of life to the origin of earth - nice and non-geocentric!
The rise of life doesn't have to be a universally unique event. It can easily rise in isolation on Earth, and in numerous other places that obey the laws of organic chemistry.
Ach, this is terrible. Genome size is influenced by a number of factors, including:
- Generation length (faster-reproducing organisms tend to have smaller genomes)
- Transposons (sections of DNA that use cellular machinery to copy themselves to multiple places in the genome; they're usually viruses that got incorporated into the host germline some time in the past)
Genome size isn't anything like a direct measure of "complexity" or "development"; some plants have genome sizes that are orders of magnitude larger than the human genome.
The worst part is that they take genome size sampled in the present day across multiple organisms, and then they assume that some of those present-day genomes represent genomes from particular points in the past. So the x-axis in the regression is basically a guess.
I'm not a statistician, but I imagine that fitting an exponential distribution can make the x-intercept sensitive to data errors.
> Genome size isn't anything like a direct measure of "complexity" or "development"; some plants have genome sizes that are orders of magnitude larger than the human genome.
What makes you think those plants aren't more genetically complex and developed than we are? A lot of our complexity is non-genetic.
> I'm not a statistician, but I imagine that fitting an exponential distribution can make the x-intercept sensitive to data errors.
You mean exponential growth, not an exponential distribution, and exponential growth never hits the x-axis.
> What makes you think those plants aren't more genetically complex and developed than we are? A lot of our complexity is non-genetic.
The real problem is that "complexity" and "development" in the context of evolution aren't well-defined. So the way that they place genomes on the x-axis is really arbitrary.
> You mean exponential growth, not an exponential distribution, and exponential growth never hits the x-axis.
Right, exponential growth. And yeah, the horizontal line that they're extrapolating back to isn't the x-axis, it's the y=1 line. The point remains: roughly speaking, it seems like small changes to the curve could produce large changes to the x-coord where y=1, since the curve is so close to horizontal there (on a linear-linear plot), right?
I imagine they're using genome size (number of bases in exons) as their measure of complexity, not something ill-defined.
I don't think it's sensitive the way you say, since they're basically just linearly extrapolating the logarithm, but I haven't read the paper. If you somehow manage to shoot high or low by a factor of 100, that's only a temporal error of six or seven doublings. The bigger issue is that they're extrapolating over orders of magnitude that we have no direct evidence about.
> I imagine they're using genome size (number of bases in exons) as their measure of complexity, not something ill-defined.
They're taking genome size and trying to relate it to time. And the way that they're doing that is to take organisms from the present day, and imagine some of them in the past. The less "developed" or "complex" an organism is, the further in the past they put it. That process is the flawed part.
In other words, the y-axis (genome size) is well-defined, but the mapping of organisms onto the x-axis (time) is not.
Regarding the first point: those plants and lungfish etc with massive genomes aren't more phenotypically more complex. They just contain more duplication. If anything, it's likely a slight impediment to reproduction, but not enough to get selected against.
I'm not sure about this. Plants synthesize a whole lot of biochemicals no animal does, for example, including a lot of amino acids. They can't run away from insect parasites or swat them with their tails, so they have to be able to poison them — without killing the plant or, say, the birds that spread their seeds. There's a lot more phenotypic complexity in corn than you can see without a chemistry lab, probably including things we haven't discovered yet. Do you work in botanical bioinformatics, or are you just guessing?
The problem with your argument is that most plants don't actually have larger genomes. Just some of them. So, if we found a plant with a small genome that synthesized a large range of chemicals, than we could simply assume that the plants with larger genomes aren't getting that kind of benefit. And that is indeed the case. Nobody has found anything phenotypic in these large-genome plants/fish that shows them to be more complex or anything.
My PhD is in Biophysics, my BA in Biochemistry/Molecular Biology, although I don't work in botanical bioinformatics, my education provides me with the tools to make rational decisions based on data.
Extrapolating back to when the genome size was 1 probably doesn't make sense, but what this approach might be able to do is suggest the smallest functional genome size, about 10,000bp based on my reading of the graph.
It's also worth pointing out that we don't, in general, have a way to know what portions of the genome are truly functional, nor even a really great definition of functional (the ENCODE consortium caught a lot of flak for theirs as overly broad). Also, almost all the genome sizes we know are from the modern day: bacteria as a class may be 3 billion years old, but they've been evolving over those 3 billion years too!
Notice how they cherry-pick among genomes to get their fitted line. A common fallacy is to associate genome size with the "complexity" of an organism. Even assuming that there is a simplistic linear ordering of organismal life (i.e., "lower" and "higher" organisms), a "lower" organism like the poplar tree has more than two times the number of genes as the human genome.
But there has to be some characteristic that certain species required many evolutionary "steps" to get to. For example, there were no humans OR poplars one billion years ago nor the possibility yet since their / our ancestors hadn't yet made the scene.
Had the same thought. There are also multiple problems with using the "functional, non-redundant" part of the genome, which presumably means the exons.
I think the problem here is that we don't have the original prokaryote genome. Today's prokaryotes have gone through billions of years of evolution and might well have longer genomes than their distant ancestors. So the leftmost points on the graph might have shifted up over time (albeit more slowly than the rightmost).
Way worse. Evolution is not about gaining complexity but about fitting to (current state of) the environment; in this manner, Procaryota are actually the most "evolved" form of life since they can rapidly adapt thanks to their short life cycle and uncluttered physiology. And this can be easily seen in genetic diversity -- the whole spread of animals or plants is almost negligible in comparison (http://en.wikipedia.org/wiki/File:Tree_of_life_1500px_colour...).
Another good example is the fact that practically all procaryotic germs are highly specific, so must have evolved after their host species did.
There is a lot wrong with this approach, but even if you agree with their method there is one thing that seems odd in their analysis.
Let's suppose that life originated before the birth of the earth. Obviously it managed to reach earth, which would mean it (the organism) had to travel through space for an extended period of time - the authors claim that it was frozen - so you should account for that time in the graph, and obviously life couldn't settle on earth for quite some time because in the beginning our planet was extremely hot and unsuitable for any kind of life. That would shift their postulated origin of life further into the past, most likely to a point where there wasn't even a universe to begin with, or at least no planets that would allow for the formation of life. [Note: I didn't read the whole paper, maybe they discussed these points. Also please correct me if I'm wrong on anything]
This is fun!
I took the boys average weight data given in [1]
months from conception, average weight (kilos)
(9, 3.25)
(15, 7.5)
(21, 9.97)
(33, 12.88)
(45, 14.97)
(69, 18.97)
and saw that it really looks like a logarithmic function. So, fitted y to log(x), got y~7.4685*log(x)-13, with R^2=0.9965 (thank you R!). Extrapolated back, and found that at conception, the fetus weighs -inf kilos. Not surprising result, I must say. However, I was surprised to find that only after five months of gestation, the embryo reaches the critical mass of 0 kilos.
Science!
Their measure of complexity is Genome size. That's a very simple measure of complexity.
Just a dumb example-- humans have a single gene for a protein in muscles. But that single genes has multiple exons and depending on whether it's being produced in heart, usual muscles, or soft-tissue muscle it's transcribed differently. Most of the protein is re-used, but a couple of parts are swapped out.
So we have genetic code being used in three different proteins. Our genome is being efficient in size, yet it's increasing in complexity.
That being said, I have no idea how to measure complexity well.
You know something is wrong when the complex and evolutionary trees of "mammals" and "eukaryotes" are single points on a graph. If the geekbait Moore's Law didn't tip you off, the paper is garbage.
They try to put organism's compexity on a logarithmic scale and look where this line "reaches zero". Then they find out it was very very long ago, before the Earth coalesced. They then proceed to imply that life is older than Earth.
The problem here is: you can't do that.
For example, if you look at human population, it was growing kind of exponentially for as long as we know. And if we continue this trend long enough to the past we'll infer that Earth featured several dozens of humans even when it was still in a liquid magmatic state five billion years ago!
Exponential growth becomes glacially slow when we go back in time. Therefore, any growth seems to be not exp(t) but rather max(ct, exp(t)) - not slower than linear.
It doesn't matter once we hit present history, but it does matter when we talk about a linear part in the past.
That said, the estimated 9.7 ± 2.5 billion years for life to evolve is at least within an order of magnitude correct, which makes it pretty cool. Also, we don't know how long abiogenesis takes and through which stages (presumably: many) the whole thing has to go until even the most basic building blocks as we know them today are set up. So you can add X billion years on top of that estimate while you're at it.
Does that prove anything? Absolutely not. But even without evoking the tiring panspermia hypothesis this result is interesting, because the result is not completely off. It means that, within a certain period at least, the complexity of life does follow this exponential rule. Clearly, the formula needs to be tuned (based on evidence, not on open-mouth extraterrestrial finger-pointing), but it's a great start. I would venture that a more detailed genetic analysis of relatively recent organisms might result in a curve that is much more accurate.
Assuming something will always double within a certain interval is not detailed enough as a model for this.