 from the standpoint of the common and linear that they were talking about. And then what Rick did is convert those numbers into equivalent numbers for the whale transition given what we know about generation times and mutation rates and so forth for those animals. So we have a big numbers problem. But the problem doesn't stop there. We have a second problem which we call the problem of combinatorials sometimes the problem of combinatorials and specificity. If you think back to 1953 and the discovery of the structure of the DNA molecule and the recognition soon thereafter that DNA encodes information in the form of a four character alphabet code, four character chemical code that had a profound effect on thinking in evolutionary biology. And it affected thinking in neo-Darwinian theory in that people postulated changes in the arrangement of those chemical letters in the DNA molecule could be the source of variation. And we call those changes now mutations. But neo-Darwinism made an assumption that was conceded in Rick's analysis that may not actually be true. In fact the talk we're going to see here next is going to show that this assumption is false. And so therefore the problem that Dr. Sturmburg has just alluded to is actually more severe than even he has indicated. And the assumption that neo-Darwinism made was that mutations can generate new genetic information and new traits rather readily. If you change the sequence of the genes, the sequence of bases in the genes can generate new proteins rather readily. Dr. Axe is now going to look at whether or not that's really true. Here's a nice picture of the DNA molecule. And here's a way to kind of put this in context. If we begin to think about the informational nature of biology and that DNA encodes information in the form of a four character code we begin to then think about other codes and languages that we know that use similar ways of conveying information. The question arises. If you begin to, here's the question. If you begin to degrade the, or rather if you begin to change the characters in a message that has a digital or alphabetic nature, you begin to change them at random, you begin to be more likely to enhance the message or to degrade it. Intuitively, especially when you think of English code, English text or computer code, we want to say it seems more likely you're going to degrade it. So time and time waits for no man to start changing the letters around it. Pretty soon you're going to face the information that was there, original. It might be lucky that one letter changed, but keep doing it for very long you're going to face the original message. Now the question is, is that the same, well, first of all, why is that? Well the reason for that turns out to be what we call the combinatorial problem. There are so many different, if you have a say 20 letter sequence, there are so many different ways of arranging 20 characters in say English that the number of functional sequences of 20 letters long is a tiny, tiny fraction of the total number of ways of combining the 26 letters in a 20 letter sequence. So the space of functional sequences is tiny compared to the number of possible combinations of letters there are. Now early on biologists thinking about or mathematicians thinking about the neo-Darwinian mechanism began to wonder if the same thing wasn't true of the information coded in DNA, the arrangements of bases in DNA and the corresponding arrangement of amino acids and proteins. Because if it is the case that there are very few functional sequences in comparison to all the combinations of possible arrangements that are out there, then if you think about a long, mutational search, finding those new functional sequences, you've got real problems. So a crucial question became one that was in fact posed by Doug Axe in his laboratory when he was working in Cambridge. And the question is, how rare or common are functional proteins in relation to all the possible combinations, corresponding combinations of amino acids? Same question, you could ask the same question about functional genes in relation to all the possible combinations of nucleotide bases. Are they very rare or are they relatively common? If the functional sequences are common, then neo-Darwinism's got a shock. It can blindly stumble from one little island of functions for the next. But if they're rare or prohibitively rare, then the mechanism is not going to get it done and it's going to be insufficient to create even new genes or proteins that work as Dr. Axe has tested it, even a single new protein fold. Just one more little illustration, and I'll get him up here to talk about some of the cutting-edge aspects of this debate, or this research. How many combinations are possible here before a dial walk? Ten on each dot, ten characters on each dial. We might tend to say ten times ten times ten, ten times ten. At a poor dial-up, you've got ten thousand combinations. Now, the artist mocked myself, I've never seen a lot like this, but this was really incredible. If you've got a ten dial-up, you don't just have a few more. You've got ten times ten, you've got ten as intense as possible. And that's the problem. If you've got your thief out there on the quad trying to pick a lot like that, there's only one functional combination in relation to ten to ten possibilities. Is your bike pretty safe? Obviously. I mean, it's not just to tell a cow to come home, he's going to run out of universes. So that's what that's kind of been talking about. What is the ratio of the number of functional amino acids to all the possible combinations of a given length? Now, what Doug has done is he's asked this question, how common or rare are functional sequences, and he proteins, among all the possible combinations of amino acids. He's tested this for a very modest length sequence of about 150 amino acids. It's enough to make a protein bowl. Most proteins are on average about 300 amino acids in length. And he's found in his research, when his method was described in the film called Site-Directed Mutagenesis, that the ratio turns out to be a very, very scary number. And he's allowed me to kind of get to the punchline here. In other words, how many folded proteins, what protein folds are there for all the different combinations of amino acids? How many, for every one of us, how many are there are those? And based on his experimental research, he's conducted over a dozen years. He's established that it's about one over 10 to 74 of power. Now that's the result that was published in the paper that was mentioned in the Journal of Molecular Biology. There was a nice illustration in the film about what a number like one out of 10 to 74 is like. One atom in the galaxy or something like it. It's an immensely small ratio. And what that means is that mutation selection are looking blindly for those functional proteins. Remember, natural selection selects for functional advantage. There's nothing there to select and preserve until it performs a function. So you've got to get the variations to find it first before there's any evolutionary progress whatsoever. Or for development. So now, what I'd like Doug to do is come up and give us a little... This is a paper that I'm describing that he did in 2004. But he and his colleagues in the lab of biologic medicine in Washington have been doing further analysis on this problem of combinatorials and have more to share about how predictive a difficulty it actually is for the mutation selection mechanism. It's non-technical as much as I can because you guys have been listening to a lot. And I appreciate that. I'll talk about... I'll touch on the problem that this team mentioned, but I'll get to talk about an even simpler version of the problem that we've been looking at. And I'm going to try to talk about it without getting too technical by using some analogies. How do you make this go forward? Oh, there we go. Okay, so I'm the director of Biologic Institute. You can go to this website to see more about who we are and what we do. And one of the things that we focus on in particular is this information problem. And my interest in particular is information at the level of single genes or small sets of genes that produce special protein. So I'm a protein guy. And I do experiments with proteins. And you saw in the movie, I think... Does this have laser? No, it doesn't. So I have two things. Get complicated. A summary of how this works. This gene sequences with the four bases of DNA and code, the information that cells use to make proteins. And it goes through a number of steps, but it ends up being that gene sequences give the information for the cells to make protein sequences. And these are chains made up of the 20 amino acids. And if those sequences are just right, it's a very rare property, but if they're just right, then these chains collapse spontaneously into compact three-dimensional structures that are stable. And if those structures have just the right shape, they can perform useful functions. In fact, they do all the functions, virtually all the functions in a cell at the molecular level. So that is the importance of information for proteins. These sequences need to have the right properties in order to get biological function, and they function by means of structure. Just to give you an idea of the kind of variety of structure you get in proteins, there are thousands of fundamentally different protein structures that have been catalogued, and that number keeps going up every year. And this just gives you an idea of how wildly different they can be. And what I want to do is use an analogy to examine what is being claimed by the Darwinists and to raise hopefully your skepticism of that claim. And the analogy is that the Darwinism mechanism is acting something like a search engine. And I'm using that because you're familiar, you're probably more familiar with search engines than you are with Darwinism. And this is what I mean. If an organism finds itself in need of a new function, a function that it does not have because it doesn't have the right gene, then supposedly if Darwinism is true, it can appeal to the Darwinian process, this mechanism that goes out and does its thing and lo and behold, eventually it will return and will give that organism a new gene that by producing a protein of the right structure gives it the function that it needs. That's how Darwinism is supposed to work. So it's actually really kind of like a search engine. When you type something in Google, you don't care how it works, it goes out to the world, finds what you need, brings it back. And these cells, they don't care how it works, they just care that it does work. Okay. So I want to look at two scales of problems that are solved by the Darwinian search mechanism supposedly if you believe that it works. And one is the very simplest level. This is what I call the beginner search. This is Darwinism 101. Suppose you are a bacterial cell, you have the gene to make this beautiful purple protein that does its purple function, but you find yourself in need of a new function that will cause the blue function and it needs a slightly different structure in order to perform this function. Although you see these are actual protein structures, you see that they are very similar, they are slightly different, and it turns out that the slight differences are crucial. This one performs a function that this one does not and vice versa. Well, so this is an example where if Darwinism can solve this problem, all the bacterial species have to do is wait for the results of the search to come back and Darwinism will give us a gene for a variant of this gene that has the right structural changes. So that's the beginner search, but there's a much more advanced level of search that I'm also interested in. This is what the 2004 paper was looking at. And that is suppose the bacterial cell is in need of the green function and suppose the green function cannot be performed by anything that looks at all like this or like any of the other proteins that it already has. Then the task that it's giving to the Darwinism search is go out and find me a whole new protein structure all together. Do whatever you need to do, cobble together genes, mutate them. I need something radically different here. I need the green function and you're going to have to go get me a new structure. That is intuitively a much more difficult problem because you don't have something like this to begin with. You have to really invent from scratch. But if you're Darwinist, you believe that it works. So let's imagine that you're a convinced Darwinist and you go, you're convinced of this idea that it's a phenomenal search engine and you step into the headquarters of Google and you get the big shot there together and you say, I've got an idea that is going to put you out of business unless you pay me a lot of money. And it's an idea for what I'm going to call Google on steroids. And they say, we'll give you a few minutes and tell us what you got. And they say, well, this is how