The Origin of Biological Information, Part 2: E. Coli vs. ID

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March 24, 2011 Tags: Design

Today's entry was written by Dennis Venema. You can read more about what we believe here.

The Origin of Biological Information, Part 2: E. Coli vs. ID
If your heart is right, then every creature is a mirror of life to you and a book of holy learning, for there is no creature - no matter how tiny or how lowly - that does not reveal God’s goodness.

Thomas a Kempis - Of the Imitation of Christ (c.1420)

In the first post in this series , we explored the claim made by Stephen Meyer, a leader in the Intelligent Design Movement (IDM), that “specified, complex information” cannot arise through natural means. This is crucial to Meyer’s argument, since any natural mechanism that can be shown to produce information would render his argument that information only arises from intelligent sources null and void.

A second member of the IDM who frequently makes this argument is Douglas Axe, a researcher at the Biologic Institute. Axe’s specialty is in protein structure / function relationships, and he has published a few papers in this area in the mainstream scientific literature. Axe’s work also forms the basis for Meyer’s arguments in this area in his book Signature in the Cell. I met Axe a few years ago when I gave a presentation at Baylor, and again last year in Austin for the Vibrant Dance conference (for whatever reason, it seems we only cross paths in Texas). Axe was present in the audience for a discussion session I shared with Richard Sternberg, and we had a significant amount of back-and-forth. As such, I am familiar with his line of argument, and it matches what we saw previously in Signature (as one might expect, since Meyer bases his work on Axe).

Perhaps the best summary of Axe’s argument is his quote I highlighted previously (begins approx. 15:19):

“Basically every gene, every new protein fold… there is nothing of significance that we can show [that] can be had in that gradualistic way. It’s all a mirage. None of it happens that way.”

One of the interesting features of the IDM is that though it has not yet brought forward strong hypotheses with which to test ID, it frequently makes testable predictions about natural processes. Specifically, Axe’s hypothesis is that mutation and natural selection will be unable to produce anything significant in a gradual way.

Has natural selection been Axed?

The ideal way to test this hypothesis, of course, would be to follow a population of organisms over thousands of generations and track any genetic changes that occur to see if they result in any new functions. Even better would be the ability to determine the precise molecular mutations that brought about these changes, and compare the offspring side-by-side with their ancestors. An experiment with this level of detail might sound too good to be true, but one of exactly this sort has been going on since the late 1980s, studying the bacterium, E. Coli. It’s called the Long Term Evolution Experiment (LTEE), and it’s the brainchild of Dr Richard Lenski at Michigan State University.

The LTEE started in 1988 with twelve populations of E. Coli all derived from one ancestral cell. The design of the experiment is straightforward: each day, each of the twelve cultures grow in 10ml of liquid medium with glucose as the limiting resource. In this medium, the bacteria compete to replicate for about seven generations and then stop dividing once the food runs out. After 24 hours, 1/10th of a ml of each culture is transferred to 9.9 ml of fresh food, and the cycle repeats itself. Every so often, the remaining 9.9 ml of leftover bacterial culture is frozen down to preserve a sample of the population at that point in time – with the proper treatment, bacteria can survive for decades in suspended animation. Early in the experiment this was done every 100 generations, and later this was shifted to every 500 generations. A significant feature of the LTEE is that these frozen ancestors can be brought to life again for comparison with their evolved descendants: in essence, the freezers in the Lenski lab are a nearly perfect “living fossil record” of the experiment.

It is important to note several things about the LTEE. First, there is no artificial selection taking place. The environment for the bacteria is kept constant: the same food, the same temperature and the same dilution routine are maintained each day. Second, the bacteria in the experiment are asexual: this means that genetic recombination, a hugely important source of genetic variation in sexual organisms, is absent. New genetic combinations in the LTEE must arise solely by mutation. Third, the bacterial populations that started the experiment are unlike any natural population, since they are all identical clones of each other. (In other words, genetic variation in the original 12 cultures was essentially zero). While natural populations have genetic variation to draw on, these twelve cultures started from scratch.

Since its inception, the twelve cultures have gone their separate ways for over 50,000 generations. Early on, the cultures quickly adapted to their new environment, with variants in each population arising and outcompeting others. In order to confirm that the new variants indeed represented increases in function (and thus, an increase in “information”) the evolved variants were tested head-to-head against their revivified ancestors. Numerous papers from the Lenski group have documented these changes in great detail. What was remarkable about the early work from the Lenski group was that tracking the 12 cultures showed that evolution in the different populations was both contingent and convergent: similar, but not identical, mutations appeared in many of the lines, and the different populations had similar, but not identical, increases in fitness relative to the ancestral populations. In the details, evolution was contingent, but overall, the pattern was convergent. As Lenski puts it:

To my surprise, evolution was pretty repeatable. All 12 populations improved quickly early on, then more slowly as the generations ticked by. Despite substantial fitness gains compared to the common ancestor, the performance of the evolved lines relative to each other hardly diverged. As we looked for other changes—and the “we” grew as outstanding students and collaborators put their brains and hands to work on this experiment—the generations flew by. We observed changes in the size and shape of the bacterial cells, in their food preferences, and in their genes. Although the lineages certainly diverged in many details, I was struck by the parallel trajectories of their evolution, with similar changes in so many phenotypic traits and even gene sequences that we examined.

In other words, there were many possible genetic states of higher fitness available to the original strain, and random mutation and natural selection had explored several paths, all leading to a higher amount of “specified information” – information that specifies increased reproduction and survival in the original environment. All this was by demonstrably natural mechanisms, with a complete history of the relevant mutations, the relative advantages they conferred, and the dynamics of how those variants spread through a population. The LTEE is at once a very simple experiment, and an incredibly detailed window into the inner workings of evolution.

And so the work continued, day in and day out, for years – until one day, a completely new biological function showed up in one of the cultures.

One of the defining features of E. Coli is that it is unable to use citrate as a food source. The food used to culture the strains, however, has a large amount of citrate in it – a potential food source that remained beyond the reach of the evolving strains. For tens of thousands of generations, no variants arose that could make use of this potential resource – even though every possible single DNA letter mutation (and every possible double mutation combination) had been “tested” at some point along the way. There seemed no way to for the populations to generate “specified information” to use citrate as a food source – they couldn’t “get there from here.” Then one day, the fateful change occurred in one of the 12 populations. Lenski puts it this way:

Although glucose is the only sugar in their environment, another source of energy, a compound called citrate, was also there all along as part of an old microbiological recipe. One of the defining features of E. coli as a species is that it can’t grow on citrate because it’s unable to transport citrate into the cell. For 15 years, billions of mutations were tested in every population, but none produced a cell that could exploit this opening. It was as though the bacteria ate dinner and went straight to bed, without realizing a dessert was there waiting for them.

But in 2003, a mutant tasted the forbidden fruit. And it was good, very good.

Details, details

Tracking down the nature of this dramatic change led to some interesting findings. The ability to use citrate as a food source did not arise in a single step, but rather as a series of steps, some of which are separated by thousands of generations:

  1. The first step is a mutation that arose at around generation 20,000. This mutation on its own does not allow the bacteria to use citrate, but without this mutation in place, later generations cannot evolve the ability to use citrate. Lenski and colleagues were careful to determine that this mutation is not simply a mutation that increases the background mutation rate. In other words, a portion of what later becomes “specified information for using citrate” arises thousands of generations before citrate is ever used.

  2. The earliest mutants that can use citrate as a food source do so very, very poorly – once they use up the available glucose, they take a long time to switch over to using citrate. These “early adopters” are a tiny fraction of the overall population. The “specified information for using citrate” at this stage is pretty poor.

  3. Once the (poor) ability to use citrate shows up, other mutations arise that greatly improve this new ability. Soon, bacteria that use citrate dominate the population. The “specified information for using citrate” has now been honed by further mutation and natural selection.

  4. Despite the “takeover”, a fraction of the population unable to use citrate persists as a minority. These cells eke out a living by being “glucose specialists” – they are better at using up glucose rapidly and then going into stasis before the slightly slower citrate-eaters catch up. So, new “specified information to get the glucose quickly before those pesky citrate-eaters do” allows these bacteria to survive. As such, the two lineages in this population have partitioned the available resources and now occupy two different ecological niches in the same environment. As such, they are well on their way to becoming different bacterial species.

Don’t tell the bacteria

The significance of these experiments for the Intelligent Design Movement is clear. Complex, specified information can indeed arise through natural mechanisms; it does not need to arise all at once, but rather accrue over thousands of generations; independent mutations that do not confer a specific advantage can later combine with other mutations to produce new functions; new functions can be quite inefficient when they arise and then be honed through further mutations and selection; and the entire process can occur without ever reducing the fitness of a specific lineage within a population. Moreover, these findings have been demonstrated with a full historical record of the genetic changes involved for the entire population they occurred in, as well as full knowledge of their fitness at every step along the way.

In other words, what the IDM claims is impossible, these “tiny and lowly” organisms have simply been doing – and it only took 15 years in a single lab in Michigan. Imagine what could happen over 3,500,000,000 years over millions of square miles of the earth’s surface.

In the next post in this series, we will look at an example of new information and function arising during vertebrate evolution: the elegant work of the Thornton lab on steroid hormones and their protein receptors.


Dennis Venema is Fellow of Biology for The BioLogos Foundation and associate professor of biology at Trinity Western University in Langley, British Columbia. His research is focused on the genetics of pattern formation and signalling.

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Bilbo - #57419

April 8th 2011


John:  “No, Bilbo, allowing for interpretation after you get
the data isn’t science.

But data is only data until it is interpreted.




Bilbo - #57420

April 8th 2011

John:  “If
the start mechanism is less intelligent than the stop mechanism, there
is a clear empirical prediction that can be derived from the hypothesis
that the start is less changeable than the stop.”

I agree.  So the question is, is the start mechanism “less intelligent” than the stop mechanism?  And that is a question of interpretation and investigation. 


John - #57422

April 8th 2011

“But data is only data until it is interpreted.”

But the power of science, which “Mike” fears, is about doing the interpretation BEFORE we get the data. Why do you share “Mike’s” fear in this case?

IOW, while data are interpreted, the most powerful data are those that were derived from the interpretation before we see them. Again, this works whenever you don’t have the data, even when someone else has them. What are you afraid of, Bilbo?

Do you see this whole honesty thing, Bilbo? It’s about not letting ourselves be fooled by wishful thinking, which is all you and “Mike” have.

Me: “… there is a clear empirical prediction that can be derived from the hypothesis that the start is less changeable than the stop.”


Bilbo:
“I agree.  So the question is, is the start mechanism “less intelligent” than the stop mechanism?”

No. The question is a far more empirical one: is the start mechanism less fungible than the stop mechanism? 

“And that is a question of interpretation and investigation. “

So why do you continue to falsely frame it as a debate, Bilbo? Your jo—if you choose real science over ID pseudoscience—is to interpret the hypothesis to obtain a prediction, then investigate to find the relevant data.

So, can you interpret to come up with the empirical prediction given the hint of mutants, or are you afraid to open your mind?

Bilbo - #57532

April 9th 2011

John:  “But the power of science, which “Mike” fears, is about doing the interpretation BEFORE we get the data. Why do you share “Mike’s” fear in this case?”

You mean coming up with a hypothesis and then testing it?  Yes.  The hypothesis is that the first living cells were designed.  If most of our data support that hypothesis, then we continue to pursue it.  When we come across occasional data that challenge that hypothesis, we don’t abandon the hypothesis.  We look for an explanation for the data that is consistent with the hypothesis.  This is true for any scientific hypothesis.

John:  “So, can
you interpret to come up with the empirical prediction given the hint of
mutants, or are you afraid to open your mind?”

I’ll let you come up with another hypothesis, John.  If you want to share what you come up with, I’ll be happy to read it.  Meanwhile,  I’ll pursue the design hypothesis.

By the way, for a “scientist” you do a lot of “mindreading.”  Have you ever really tested your mindreading abilities?   Or are you really just a pseudo-scientist?
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