Evolving Immunity

Legacy route: /cs/evolving_immunity/ Evolving Immunity Matt Inlay responds to the immunity chapter, Chapter 6, of Michael Behe's Darwin's Black Box . < A Response to Chapter 6 of Darwin's Black Box by Matt Inlay Version 1.4 [Contributed June 6, 2002; Last Modified July 17, 2002] email author for comments and feedback Introduction Immune systems are generally characterized by their ability to distinguish between self and non-self cells, tissues, or molecules, and to eliminate the non-self (for review see Janeway 2001 ). The mammalian immune system is an incredibly complex and intricate system that can recognize non-self and provide protection from a wide variety of pathogens. While there is a high degree of interconnectivity between its components, the immune system can be loosely divided into two subsystems, the innate and the adaptive immune systems. In general, innate immunity is a non-specific, inducible response to pathogens. It is immediate in action, yet short-lived. On the other hand, the adaptive immune system is much more specific, but takes longer to activate. It also features immunological memory, and can augment itself to respond more quickly and with greater specificity to future infections of similar pathogens. Both systems work together to provide protection against a diverse and rapidly-evolving array of pathogens. While much is known about the evolution of the immune system, the details of its origin remain to be elucidated. The innate immune system is the more ancient of the two systems, with roots deep in the deuterostome branch of the bilaterians, roughly one billion years ago. Conversely, the adaptive immune system appeared more recently and quite suddenly, around 450 million years ago with the emergence of the gnathostomes, more commonly called the jawed vertebrates. Because of its high degree of complexity and interconnectivity, the mammalian immune system has been labeled as "irreducibly complex", and its evolution and origin through "Darwinian" mechanisms challenged [i] . Michael Behe, in his book Darwin's Black Box ( Behe 1996 ), defines irreducible complexity (IC) as: "[A] single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease function." (page 39). Unfortunately, each of the terms "system", "well-matched", "interacting", "parts", and "function" can have multiple meanings, depending on the context, so nonarbitrarily labeling a system as IC is not an easy task. For instance, "system" in Behe's definition differs significantly from the same word in the context of "immune system". His use of "system" refers specifically to biochemical systems. What is the difference? The components, or parts of a biochemical system are molecular in nature, like proteins or complexes of molecules. "System" as is used in immune system refers not just to molecules, but to the cells and tissues involved in conferring immunity. The immune system as a whole contains numerous biochemical systems within it. It is important to note this distinction because Behe only addresses biochemical systems as IC. Systems, whether biochemical or not, are often defined according to their function. However, the term "function" is also nebulous. The "function" of the immune system is rather broad, encompassing any action involved in either immune recognition or response. Even the skin is considered to be part of the immune system. The "function" of a biochemical system is much more specific. Here, function refers to a specific biochemical function, such as the formation of a blood-clot, the spinning of a flagellum to produce locomotion, or the synthesis of a biomolecule from a precursor. This type of function usually consists of a series of biochemical reactions. This is important because Behe is trying to make a specific argument about the origin and evolution of IC biochemical systems. Immediately after defining IC, Behe states: "An irreducibly complex system cannot be produced directly (that is, by continuously improving the initial function, which continues to work by the same mechanism) by slight, successive modifications of a precursor system, because any precursor to an irreducibly complex system that is missing a part is by definition nonfunctional. An irreducibly complex biological system, if there is such a thing, would be a powerful challenge to Darwinian evolution. Since natural selection can only choose systems that are already working, then if a biological system cannot be produced gradually it would have to arise as an integrated unit, in one fell swoop, for natural selection to have anything to act on." Behe is trying to suggest that because of the nature of IC, biochemical systems that possess this quality could not have evolved through random mutation and natural selection, or "blind-watchmaking". Essentially, Behe is using IC to disprove evolution. As an alternative, Behe suggests that the only way that IC biochemical systems could have originated is through intelligent intervention. He offers as evidence the fact that all IC systems whose origins are known were designed by intelligent agents, us. There are many problems with Behe's argument. Basically, Behe is saying that because we do not know how an IC system could have evolved, it didn't evolve. This type of argumentation is referred to as an argument from ignorance . The problem with arguments from ignorance is that they make a conclusion based on a lack of knowledge. There is still much we do not know about evolution. What seems like a conundrum right now may be easily resolved in the future. Biology, and science in general, is filled with mysteries and unknowns. The whole point of science is to seek answers to those questions. Secondly, he tries to use arguments against evolution as evidence for intelligent design. Negative arguments against one theory do not necessarily provide evidence for a particular alternative. It would be like trying to show how safe a Honda Civic is by providing evidence that a Toyota Corolla is unsafe. No matter how unsafe the Corolla is, it says nothing about the Civic. Negative argumentation is scientifically useless without an alternative that can be positively tested. These fallacies do not rule out the possibility that Behe has raised an interesting and useful challenge to our current understanding of evolution. However, a closer look reveals he hasn't. In chapter 6 of Darwin's Black Box , entitled "A Dangerous World", Behe outlines three IC biochemical systems in the immune system; clonal selection, V(D)J recombination, and the complement cascade. However, in his analysis of each of these systems, Behe makes critical mistakes. For the process of clonal selection, Behe targets the antibody molecules, which have the ability to rearrange their genes, and to switch from a membrane-bound form to a secreted form. Behe assumes that an antibody molecule missing one of these features would be useless. However, there are many examples of proteins that do not undergo rearrangement, but are able to switch forms. Additionally, there is a gene closely related to antibodies which can also undergo rearrangement, but does not switch forms. Behe neglects to mention either of these intermediate-like molecules. Instead, he scrutinizes the process of antibody gene rearrangement as the second IC system. He describes the numerous features of this system and claims there is no pathway through which it could have evolved. However, he assumes that the only selectable function for the genes involved in rearrangement is rearrangement. There are well-studied systems that bear remarkable similarity to V(D)J recombination that have nothing to do with immunity. He fails to recognize the existence of these other systems, nor realizes that most of the components of recombination are already present there. Finally, in his description of the third IC system, Behe mistakenly assigns a single function to the complement pathway. He fails to realize that the complement system has several different functions, and several mechanisms of activation. This would allow for a gradual, stepwise accumulation of parts and functions. In fact, many other groups of organisms possess a complement system that lacks his primary function. According to Behe's definition of irreducible complexity, those systems shouldn't exist. In each case, Behe claims that a lack of articles in the scientific literature provides evidence that these systems could not have evolved. However, there are hundreds to thousands of articles published on these systems. Much was known about the evolution of each of these systems at the time Darwin's Black Box was published. Additionally, much has been learned since then. This article will attempt to summarize the existing knowledge about the origins of these systems, and propose a possible pathway for their evolution through a Darwinian mechanism. It is not necessary to "prove" that these systems evolved. Because the essence of his argument is "how could this have evolved?", this article merely seeks to provide an answer to that question. One issue that will not be discussed is whether or not these systems are actually irreducibly complex. The term is not very useful to the discussion of origins because of the ambiguities surrounding its definition. In fact, IC as a term has failed to make much of an impression on the scientific community, as a search on the publication database PubMed, fails to turn up any references to "irreducible complexity" [ii] . IC system I: Clonal selection The first system Behe describes in chapter 6 is the process of clonal selection, which is how the adaptive immune system detects non-self molecules and initiates a response. This system focuses on a protein called immunoglobulin (Ig), and the complex it is part of, called the B cell receptor complex (BCR). Immunoglobulins, also called antibodies, are Y-shaped proteins that can recognize and bind foreign molecules (Figure 1a,b). They are produced by lymphocytes called B cells, and are initially expressed on the cell surface. In its membrane-bound form, an immunoglobulin molecule binds to two additional proteins, Ig a and Ig b , to form the BCR (Figure 1c). The target of an immunoglobulin is referred to as its antigen (Ag). Antigen can be essentially any molecule, though there is a preference for proteins. Because of the way in which immunoglobulins are produced, the specific antigen that each Ig recognizes is different in each B cell. This occurs through a process of gene rearrangement called V(D)J recombination, which will be explained later. When an unactivated, or naïve, B cell's Ig recognizes its particular antigen, it sends a signal back into the interior of the cell to initiate activation. This signal is transmitted via a series of modifications through a series of "messenger" proteins, which ultimately trigger the transcription of a panel of genes involved in B cell activation. In order to become fully activated, further interaction with a second class of lymphocyte, called T cells, is required. Once the B cell is activated it proliferates and differentiates into a plasma cell, and the function of the immunoglobulin changes from recognition to response. The immunoglobulin switches from a membrane-bound form (mIg) to a secreted form (sIg), where it can diffuse throughout the tissues of the host organism and there bind its cognate antigen. The only difference between the two forms is at the very tip of the bottom of the Y shape, which corresponds to the very tail end of the gene. The switch from mIg to sIg production results from a switch between two alternative RNA splicing patterns, such that the terminal coding exon directs either the translation of a membrane-bound or soluble C-terminal domain, respectively (Figure 2). The process of amplifying a particular B cell (or subset of B cells) from amongst billions based on its antigen specificity is called clonal selection (Figure 3). This process allows the immune system to choose from which B cell (or subset of B cells) it will mount an immune response. Additional discussion of the clonal selection system in the context of irreducible complexity can be found in the web article: The Evolution of Improved Fitness (http://www.talkorigins.org/faqs/fitness/) Behe summarizes this first system as a group of 3 components (page 125): membrane-bound form of the antibody messenger secreted form of the antibody Behe has this to say about the evolution of the clonal selection system: "A cell hopefully trying to evolve such a system in gradual Darwinian steps would be in a quandary. What should it do first? Secreting a little bit of antibody into the great outdoors is a waste of resources if there's no way to tell if it's doing any good. Ditto for making a membrane-bound antibody. And why make a messenger protein first if there is nobody to give it a message, and nobody to receive the message if it did get one? We are led inexorably to the conclusion that even this greatly simplified clonal selection could not have come about in gradual steps." (Page 125) Switch forms or rearrange? So the question is, could the clonal selection system have evolved? Certainly. One thing to realize is that the immunoglobulins are not the only molecules that can undergo gene rearrangement to produce a diverse set of antigen-receptors. T cell receptors (TCRs) are antigen-receptors quite similar to immunoglobulins. However, TCRs exist only in a membrane-bound form, yet are able to carry out their particular function without the need to leave the cell surface. Their receptors are used only for the detection of foreign antigens, and do not directly mediate an effector response. So even if immunoglobulins would be unable to effectively carry out their job if they were restricted to the membrane-bound form, they could have evolved from a TCR-like gene, which does not require secretion. If that occurred, then the ability to rearrange and the ability to switch forms could evolve in separate steps, with each step offering a selectable advantage. It remains unclear whether a TCR-like gene appeared first or an Ig-like gene. However, it is also possible that the ability to rearrange came after the evolution of the alternative splicing pattern, resulting in the ability to switch. This would require that antigen receptors with a single specificity be effective mediators of immunity. Could this have occurred? Absolutely. There are several families of innate receptors that do not rearrange, called pattern recognition receptors (PRRs) ( Medzhitov and Janeway 1997 ). Bacteria possess many unique and essential surface molecules that are distinct from metazoan surface molecules. For example, organisms ranging from insects to humans bear numerous receptors capable of recognizing the bacterial cell wall component lipopolysaccharide (LPS) ( Fenton and Golenbock 1998 ). Examples include the macrophage scavenger receptor, CD14, and the b 2 -integrins. Binding to these receptors induces a variety of responses, from the release of inflammatory agents to the phagocytosis of the LPS-carrying particle. Some of these receptors are membrane-bound and some are secreted. Interestingly, there are some that can exist in both forms, such as CD14. So here we have examples of receptors that do not rearrange, can be membrane-bound, secreted, or both, and can mount an effective immune response. Recently, a gene was discovered in the silkmoth Hyalophora cecropia whose expression is upregulated upon exposure to LPS ( Sun, Lindstrom et al. 1990 ). The gene product, a protein called hemolin, is secreted into the hemolymph (the invertebrate "blood") and can bind to the most toxic region of LPS ( Daffre and Faye 1997 ). This protein is also constitutively expressed in a membrane-bound form on the surface of hemocytes (the invertebrate white blood cell equivalents). Interestingly, the structure of this protein was found to contain 4 Ig domains, and is a member of the Ig superfamily ( Lindstrom-Dinnetz, Sun et al. 1995 ). Ig domains are a type of 3D protein structure first identified in immunoglobulins, consisting of 7 or 8 b -pleated sheets. Additionally, the crystal structure was recently solved and revealed a shape loosely similar to an immunoglobulin arm ( Su, Gastinel et al. 1998 ). However, sequence analysis shows that hemolin most likely does not represent a precursor to immunoglobulins as the type of Ig-domain it possesses (the I-set) is one not found in vertebrates. Hemolin does show similarities both structurally and functionally to cell-adhesion molecules, and also contains some cell-adhesion activity. This dual function, both for immune response and cell-adhesion, suggests that immune molecules arose from cell-adhesion molecule precursors. Hemolin is a clear example of an inducible immune molecule that can exist in both membrane-bound and secreted forms. Immunoglobulin signal transduction When an antibody recognizes an antigen, it sends a signal back into the cell, which "primes" the cell for activation. But what is the nature of this signal? How exactly does the binding of antigen, which occurs on the exterior of the cell, induce a signaling cascade on the interior? The answer lies in the branched nature of the immunoglobulin. As previously stated, immunoglobulins are loosely shaped like a Y, with two arms sticking out into the extracellular space, and the bottom embedded into the membrane. Associated with each Ig molecule are the two accessory proteins Ig a and Ig b . These accessory molecules have long cytoplasmic tails, and contain motifs specialized for attracting the type of kinases required to initiate the cascade. These motifs are called ITAMs ( I mmunoreceptor T yrosine-based A ctivation M otifs). These motifs serve as targets for protein tyrosine kinases, which phosphorylate certain tyrosine residues on the ITAMs. Because most antigens have multiple epitopes (i.e. multiple binding sites for antibodies), and because the immunoglobulins have multiple arms, the recognition of an antigen results in a clustering of immunoglobulins in the area of the membrane where the antigen comes in contact with the B cell (Figure 4). This process is often called crosslinking, and is the key to activating the cell. Visually, it resembles a "hands across America", where each arm of the Ig is linked to an arm of another Ig, and so on and so on. As more and more Igs cluster around the antigen, a family of kinases, called the src family kinases, which are loosely associated with the cytoplasmic tails of Ig a and Ig b , begin to phosphorylate the ITAMs. Because the src kinases bind to phosphorylated tyrosines, this sets up a positive feedback loop, and more and more src kinases are recruited to the site. The phosphorylated tyrosines also bind another kinase called syk. The newly bound syk kinase begins to phosphorylate and activate other syk molecules, which in turn phosphorylate and activate the next molecule in the signaling cascade, thereby propagating and amplifying the signal. The downstream components of this cascade are also expressed in other cell types, and are considered more general signal transducers. In this way, a general transduction pathway can be co-opted by more specific signal molecules. This leads to the activation of transcription factors (TFs), which then enter the nucleus and initiate the transcription of genes involved in cell proliferation and differentiation. In this particular situation, the B cell becomes "primed" for activation by T cells. Most of the "messengers", or components of the BCR signaling pathway are homologous to many other signaling pathways present in other tissues and in other organisms. For instance, one of the final components in the pathway is the activation of the transcription factor NF- k B. This transcription factor is used in mul


Legacy route: /cs/flagellum_background/ Background to "Evolution in (Brownian) space: a model for the origin of the bacterial flagellum" Nick Matzke provides a brief article covering some of the background information needed to understand arguments made about bacterial flagella and Michael Behe's claims of 'irreducible complexity' for that organelle. Copyright 2003 by N. J. Matzke Version 1.0 (last updated November 10, 2003) Please send comments to: matzke@ATncseweb.org (remove obvious anti-spam modification) Introduction The article " Evolution in (Brownian) space: a model for the origin of the bacterial flagellum " is an attempt to put forward a reasonably detailed model for the evolutionary origin of the bacterial flagellum. The flagellum is a complex structure that some bacteria use for swimming, and it has featured prominently in the arguments of the "Intelligent Design" movement. The article is long and somewhat technical, and may not be readily accessible to many readers. This page attempts to give readers some background, and recommend some introductory material that will (hopefully) make the full article much more digestible. Suggestions for further improvements along these lines are welcome. Background For some time, advocates of "Intelligent Design" (ID) have been promoting Mike Behe's "irreducible complexity" argument. Behe argues that biological systems with multiple required components could not have evolved gradually, because intermediates lacking components would be nonfunctional. The argument has been answered in general terms numerous times (see http://www.talkorigins.org/faqs/behe.html for a survey), and detailed treatments of the evolution of specific "irreducibly complex" biochemical systems are now available in the case of blood clotting (see Ken Miller's article on the Evolution of Vertebrate Blood Clotting ) and the immune system (see Matt Inlay's very detailed article Evolving Immunity ). However, the ID movement's favorite example of irreducible complexity, the bacterial flagellum, has not received similar treatment. The bacterial flagellum was only discussed briefly in Behe's Darwin's Black Box , but perhaps because of the counterarguments and literature available on the evolution of other systems, the bacterial flagellum soon became the favorite example of irreducible complexity, and has ascended to near-iconic status for the ID movement. Part of the problem with discerning the evolutionary origin of the bacterial flagellum is that the flagellum is billions of years older than Behe's other example IC systems. With blood-clotting and the immune system, homologs of many of the proteins have been well-known for decades. As the phylogeny of multicellular animals is reasonably well understood, the identification of ancestral systems is not too difficult. In bacteria, on the other hand, phylogeny is confused, and for a long time not many homologs of flagellar proteins were known. Only recently have enough homologs of flagellar proteins been identified to make it possible to piece together a reasonable scenario. The question of the origin of the bacterial flagellum has intrigued me for some time, and it always struck me as a topic that "someone" should examine in more detail. It seemed clear that more could be said on the topic than "flagella don't fossilize." For awhile I followed debates on the topic, and slowly accumulated relevant literature. With the publication of thorough, up-to-date reviews of flagellum function and assembly by Berg (2003) and Macnab (2003) , I realized that I probably had enough information to give it a try, and made it my extracurricular summer project. It took rather longer than I planned to synthesize the data into an article, but a version is finally done and I think that several of the findings advance our understanding of flagellar origins significantly. Like any scientific model, this one will have some inadequacies. Some of them are due to our basic lack of knowledge: the functions of many flagellar proteins are uncharacterized, the molecular mechanisms of motor function and protein export are vague, and large-scale bacterial phylogeny is unresolved. There may also be some errors attributable to the inexpert nature of the author; formally I have nothing more than an undergraduate background in biochemistry, although I daresay that I've read enough of the relevant literature to at least avoid major errors, and even to correct a few errors in the peer-reviewed literature. As no one has ever really given the topic of flagellar origins the serious treatment it deserves, there is currently no such thing as an expert. If the article is deemed sufficiently provocative, some of this material may eventually find its way into a journal; however, the article is a bit long and unconventional compared to most journal articles, so releasing it first on the web seemed appropriate. Comments, corrections, suggestions for improvement, and suggestions about more formal publication venues are welcome and should be directed to my email (above). Alternatively, you may post comments at the EvoWiki page for Comments on "Evolution in (Brownian) space" . Introductory resources As not every reader will be very familiar with the relevant molecular systems and proteins, I have included some background links that should help bring readers up to speed. To have any hope of following the article, readers should read and understand the below material first. Berg's popular article is particularly recommended as a starting point. Flagellum A good short introduction to the three unrelated kinds of "flagella" can be found at Wikipedia: flagellum A detailed popular article on the bacterial flagellum by Howard Berg Chapter 15 of Alberts et al. (1994), Molecular Biology of the Cell , 3rd edn. Section 34.4 of Berg, Tymoczko, and Stryer (2002), Biochemistry F 1 F 0 ATP synthetase (also known as ATP synthase or ATPase) Hongyun Wang's webpage on his ATP synthase research Chapter 14 of Alberts et al. (1994), Molecular Biology of the Cell , 3rd edn. Section 18.4 of Berg, Tymoczko, and Stryer (2002), Biochemistry Life in the viscous microscopic world of bacteria Life at Low Reynolds Number by Purcell (1976) Intelligent Design arguments about the flagellum The article is an attempt at a "straight" science piece, with no attempt made to address the various tenditious arguments and equivocating definitions of ID proponents. For articles discussing ID arguments about the flagellum specifically, see the articles below by Miller and Musgrave. At some point I may attempt to write an article comparing ID proponents' wild misconceptions about evolutionary models of flagellar origins (e.g., Bill Dembski's use of cake-baking as an analogy for the evolutionary origin of the flagellum) to an actual serious evolutionary model of flagellar origins. For the moment, however, I encourage readers to look up some of the ID proponents' articles on the topic, and to compare them to the proposed model. Relevant links are given below. Behe on the flagellum in Darwin's Black Box (1996) Molecular Machines and Irreducible Complexity at the Access Research Network Dembski on the flagellum: Chapter 5, No Free Lunch (2002) Bracht, The Bacterial Flagellum: A Response to Ursula Goodenough (2002) Mike Gene, Evolving the Bacterial Flagellum Through Mutation and Cooption (2001-2) Rebuttals to ID flagellum arguments Answering the biochemical argument from design , by Kenneth Miller (2003), in: Manson, N. (Ed.), God and design: the teleological argument and modern science , Routledge, London, pp. 292-307. The Flagellum Unspun , by Kenneth Miller (2004), in: Dembski, W., and Ruse, M. (Eds.), Debating Design: from Darwin to DNA , Cambridge University Press, New York. See also Ken Miller's website . Dembski responded to Miller in an online essay Still Spinning Just Fine on his Design Inference website Musgrave, Ian (2004). "Evolution of the Bacterial Flagellum", in: Young, M., and Edis, T. (Eds.), Why Intelligent Design Fails: A Scientific Critique of the Neocreationism, forthcoming from Rutgers University Press, Piscataway, N.J.


Grist for the EF mill

Legacy route: /cs/grist_for_the_ef_mill/ Grist for the EF mill Original: Grist for the EF mill , by Matt Brauer, posted on March 25, 2004 06:36 AM. Dembski's "Explanatory Filter" (EF) claims to be a reliable technique for detecting design. To date, the EF is the only method presented by the "science" of ID. How well does it do? Nobody knows. It has been applied precisely once, by Dembski in his book No Free Lunch . And that application was a dismal failure. Before going into the reasons that the EF is a psuedo-algorithm, I'd like to present an example of what Dawkins calls a "designoid," that is, something that appears designed but isn't. A "false positive" for the EF, if you will. Objects almost identical to these were dredged from a shallow bay at the bottom of which some think is the remnants of an ancient city. The objects are made of a concrete-like carbonate, and have a single bore-hole down the center. They are about 4 cm in length. The natural questions to ask are: 1. Are the objects designed? 2. How would you apply the EF to determine if they are designed? 3. Are they "specified" (whatever that means)? 4. What objective method could one use to determine if they are specified? 5. what other information do you need to apply the EF to these objects? Do you need to know anything of the actual history of their formation? These objects are just the latest in a long line of designoids which would seem to be natural candidates for the EF (other examples include the visibility of solar eclipses, geological formations on Mars and Arctic stone circles.) So far in all cases these questions have been met with stony silence. But then again, Dembski may be doing something right: if there's no way to apply your method, you are guaranteed to have no false positives. (Thanks to Paul Heinrich of the Louisiana Geological Survey. Links to his pages will be posted later, after the design implications of these objects have been pondered awhile.)


Irreducible Complexity Demystified

Legacy route: /cs/ic_demystified/ Irreducible Complexity Demystified Pete Dunkelberg's essay will prove helpful to educators, boards of education, legislators and members of the press who may be wondering about "Irreducible Complexity". by Pete Dunkelberg [Posted: 26 April 2003] "Evolution is cleverer than you are." -biologists' proverb Introduction The Argument That Irreducible Complexity Cannot Evolve How Might Irreducible Complexity Evolve? Irreducible Complexity in Nature Venus' Flytrap How to Eat Pentachlorophenol Hemoglobin for the Active Life The Blood Clotting System: is it IC? Swimming Systems The Eukaryote Cilium The Archaeal Flagellum The Bacterial Flagellum IC Cores How Does Irreducible Complexity Get Its Charm? IC, ID, and Creationism Conclusions References Introduction new term, irreducibly complex , (IC) has been introduced into public discussions of evolution. The term was defined by Michael Behe in 1996 in his book Darwin's Black Box: The Biochemical Challenge to Evolution ( 1 ). Irreducible complexity (also denoted IC) has gained prominence as the evidence for the intelligent design (ID) movement, which argues that life is so complicated that it must be the work of an intelligent designer (aka God) rather than the result of evolution. As you may have heard, the ID movement wants this taught in public schools as a new scientific theory. This essay will, I hope, prove helpful to any school teachers, boards of education, legislators and members of the press who may be wondering about it. The argument from IC to ID is simply: IC things cannot evolve If it can't have evolved it must have been designed This article just looks at the first part, the argument that irreducibly complex systems cannot be produced by evolution, either because they just can't evolve, or because their evolution is so improbable that the possibility can be ignored. Let's take a look at the definition of IC, and then see if we can figure out its relation to evolution, and why scientists are so unimpressed. Here is the definition, from page 39 (page numbers refer to Darwin's Black Box unless otherwise noted): "By irreducibly complex I mean a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning." [emphasis in original] IC is now a single defined term. The new definition, not the ordinary meaning of the words, is now our guide. IC refers to an organism doing something (the function) in such a way that the system (that portion of the organism that directly performs the function) has no more parts than are strictly necessary. How do we decide when the term IC applies? Organisms don't come with parts, functions and systems labeled, nor are 'part', 'system' and 'function' technical terms in biology. They are terms of convenience. We might say, for instance, that the function of a leg is to walk, and call legs walking systems. But what are the parts? If we divide a leg into three major parts, removal of any part results in loss of the function. Thus legs are IC. On the other hand, if we count each bone as a part then several parts, even a whole toe, may be removed and we still have a walking system. We will see later that Behe's treatment of cilia and flagella follows this pattern. What about the boundary of the system? This too is up to us. Take the digestive system for example. We may be interested only in the action of acids and enzymes in the stomach, or we may include saliva and chewing, or the lower intestine where some extraction of water and nutrients continues. As a mental exercise, try before reading on to formulate an argument to prove that IC systems cannot evolve. IC is supposed to be the biochemical challenge to evolution, and thus the case when the parts are molecules, usually proteins, is the important case. So of course there may be multiple copies of a part. Losing a part means losing all copies of it, or at least so many that the function is effectively lost. The Argument That Irreducible Complexity Cannot Evolve Behe's argument that IC cannot evolve is central to ID, so it deserves our attention. His method is to divide evolution into what he calls 'direct', which he defines in a special way, and 'indirect' (everything else). He finds that direct evolution of IC is logically impossible, and indirect evolution of IC is too improbable. The argument against 'direct' evolution of IC is contained in this long sentence right after the definition: "An irreducibly complex system cannot be produced directly (that is, by continuously improving the initial function, which continues to work by the same mechanism) by slight, successive modifications of a precursor system because any precursor to an irreducibly complex system that is missing a part is by definition nonfunctional." The last part of the sentence, "...because any precursor to an irreducibly complex system that is missing a part is by definition nonfunctional." is why we should agree to the rest of the sentence. There are some problems: The first part of the sentence refers to slight changes. Removing a whole part is a major change; this is a major 'disconnect' between the parts of Behe's argument. It is not true that a precursor missing a part must be nonfunctional. It need only lack the function we specified. Even a single protein does something. The actual precursor may have had more parts, not fewer. If the individual parts evolve, the precursor may have had the same number of parts, not yet codependent. We will learn more about this possibility shortly. How can one construct a valid argument that IC cannot be produced directly? ID proponents have not found a way. Yet it's easy (and left as an exercise for the reader) once you realize that a valid argument from definitions requires carefully defining the terms so that the argument becomes a tautology. This may be accomplished by redefining 'direct' or 'IC', or (best, I think) by defining Behe's expression 'be produced' which he uses in place of 'evolve'. A precursor to IC lacking a part can have any functions except the specified one, which brings us to 'indirect' evolution. Consider a cow's tail. So far as I know, the main thing a cow uses its tail for is to swat flies. Did tails originally evolve for this function? Hardly. There were tails before there were flies. Long ago, tails helped early chordates to swim. Going back still farther, some very early animals started to have two distinct ends; one end for food intake (with sense organs for locating food) and the other end for excretions. As a consequence, this back end, and muscular extensions of it, could also be used to help the animal move. This illustrates yet another important facet of evolution: not only single mutations, but even large organs may begin more or less accidentally. It also illustrates that biological functions evolve. Indeed organisms and ecosystems evolve. It may not even make sense to expect a precursor to have had the same function. The long term evolution of most features of life has not been what Behe, or indeed most people, would call direct. And even short term evolution can be indirect in Behe's terms. So it is surprising to read, on page 40, Behe's argument against indirect evolution of IC systems. Here is the crux of it: "Even if a system is irreducibly complex (and thus cannot have been produced directly), however, one can not definitely rule out the possibility of an indirect, circuitous route. As the complexity of an interacting system increases, though, the likelihood of such an indirect route drops precipitously." (page 40) He simply asserts that evolution of irreducible complexity by an indirect route is so improbable as to be virtually out of the question, except in simple cases. He makes no special connection between indirect evolution and IC. He offers no evidence. He just asserts that it is too improbable. Actually, a more complex system probably has a long evolutionary history. Since evolution does not aim at anything in advance, the longer the history, the more circuitous it may be. And his very limited meaning of 'direct' renders much indirect that is not circuitous at all. Yet he insists: "An irreducibly complex biological system, if there is such a thing, would be a powerful challenge to Darwinian evolution." (page 39) Here's another exercise: before reading on, try to think of ways that IC systems, including biochemical ones, might evolve after all. How Might Irreducible Complexity Evolve? How might an IC system evolve? One possibility is that in the past, the function may have been done with more parts than are strictly necessary. Then an 'extra' part may be lost, leaving an IC system. Or the parts may become co-adapted to perform even better, but become unable to perform the specified function at all without each other. This brings up another point: the parts themselves evolve. Behe's parts are usually whole proteins or even larger. A protein is made up of hundreds of smaller parts called amino acids, of which twenty different kinds may be used. Evolution usually changes these one by one. Another important fact is that DNA evolves. What difference does this make, compared to saying that proteins evolve? If you think about it, each protein that your body makes is made at just the right time, in just the right place and in just the right amount. These details are also coded in your DNA (with timing and quantity susceptible to outside influences) and so are subject to mutation and evolution. For our purposes we can refer to this as deployment of parts. When a protein is deployed out of its usual context, it may be co-opted for a different function. A fourth noteworthy possibility is that brand new parts are created. This typically comes from gene duplication, which is well known in biology. At first the duplicate genes make the same protein, but these genes may evolve to make slightly different proteins that depend on each other. We can summarize these four possibilities this way: Previously using more parts than necessary for the function. The parts themselves evolve. Deployment of parts (gene regulation) evolves. New parts are created (gene duplication) and may then evolve. The first of these only comes up if we are looking for IC. The others are the major forms of molecular evolution observed by biologists, phrased in terms of parts. They can lead to new protein functions, sometimes slowly and sometimes, especially when parts are redeployed, abruptly. Gene duplication and changes in protein deployment may introduce a new protein 'part' into a system. Then the parts may coevolve to do something better, but in a codependent manner so that all are required, without further change in the number of parts. But what happens in nature? Irreducible Complexity in Nature Can evolution lead to IC or not? It is time to look at living examples and let nature decide. Behe's most famous example is a mousetrap. But since a mousetrap is not alive, it doesn't tell us much about whether or how living IC systems might evolve. How about a flytrap instead? Venus' Flytrap The Venus' flytrap, Dionaea muscipula , is a small flowering plant which grows naturally in acidic wetlands in North and South Carolina. It has a ferocious looking tooth-edged trap for unwary creatures . It traps and digests insects to make up for the lack of nitrogen in the soils of its habitat. Here's how the trap works. When an insect brushes against the trigger hairs in the center, the lobes snap most of the way shut with surprising speed. If a small insect is caught, it may escape between the teeth, and then the trap reopens without fully closing. If a good sized bug is caught it is digested over the next few days as the trap closes the rest of the way. Then the trap reopens. A trap can only be fully closed about 4 times, so it must be used sparingly. Do we have an IC system here? We must specify a function and all the parts needed to carry it out (and no extra parts). The function of interest is trapping insects for food in a manner that brings the plant more benefit than the cost of the trap. The parts are the two lobes, the hinge between the lobes (the midrib of the leaf, which anchors the lobes), the trigger hairs, and spines projecting from the edges of the lobes that make a set of bars as the trap closes. The system is just all these parts, and the trap needs all its parts in order to work. Hence it is an IC system. How might this trap have evolved? I say 'might' have because Venus' flytraps haven't left any fossils that I know of, except a few grains of pollen. Are there any related plants that might provide a clue? Let's look at the well known sundews ( Drosera ). Sundews trap insects using flypaper traps, slowly closing around insects that get stuck. Darwin, whose book Insectivorous Plants ( 2 ) is now available online , made careful observations of these remarkable plants, especially the round leaf sundew D. rotundifolia . As Darwin notes, If a small organic or inorganic object be placed on the glands in the centre of a leaf, these transmit a motor impulse to the marginal tentacles. The nearer ones are first affected and slowly bend towards the centre, and then those farther off, until at last all become closely inflected over the object. This takes place in from one hour to four or five or more hours. [...] Not only the tentacles, but the blade of the leaf often, but by no means always, becomes much incurved, when any strongly exciting substance or fluid is placed on the disc. Drops of milk and of a solution of nitrate of ammonia or soda are particularly apt to produce this effect. The blade is thus converted into a little cup. The manner in which it bends varies greatly. ( 2 , pp 9, 12) Here is D. rotundifolia with a fly; Makoto Honda ( 3 ) shows the action with a faster species, D. intermedia . Recent genetic research confirms that Venus's flytrap and the waterwheel plant Aldrovanda are related and are in the sundew family Droseraceae, and that snap-traps very likely evolved from flypaper traps ( 4 ) as Darwin thought: CONCLUDING REMARKS ON THE DROSERACEAE. The six known genera composing this family have now been described in relation to our present subject, as far as my means have permitted. They all capture insects. This is effected by Drosophyllum, Roridula, and Byblis, solely by the viscid fluid secreted from their glands; by Drosera, through the same means, together with the movements of the tentacles; by Dionaea and Aldrovanda, through the closing of the blades of the leaf. In these two last genera rapid movement makes up for the loss of viscid secretion. [...] The parent form of Dionaea and Aldrovanda seems to have been closely allied to Drosera, and to have had rounded leaves, supported on distinct footstalks, and furnished with tentacles all round the circumference, with other tentacles and sessile glands on the upper surface. ( 2 , pp 355-6, 360). How did the Venus' flytrap avoid the argument that IC can't evolve? In two ways. First, rather than gaining a part, it lost a part - the glue that the sundews use. Even more interestingly, the trap was able to evolve because the parts evolved. The trap started out as a Drosera -like leaf, and the parts of the leaf were progressively changed. This makes a striking contrast with the mousetrap which Behe has repeatedly presented to illustrate why IC cannot evolve. As a manufactured item the mousetrap neatly illustrates his definition, but with its static parts it cannot model evolution. With evolving parts, nature can create a snap-trap after all. The mechanical and manufacturing analogies so influential in Behe's thinking miss the flexibility of living things. How to Eat Pentachlorophenol Pentachlorophenol (PCP) is a highly toxic chemical, not known to occur naturally, that has been used as a wood preservative since the 1930's. It is now recognized as a dangerous pollutant that we need to dispose of. But how? Evolution to the rescue! A few soil bacteria have already worked out a way to break it down and even eat it. And conveniently for us, they do it in an irreducibly complex way. The best known of these bacteria is called Sphingomonas chlorophenolica (also called Sphingobium chlorophenolicum ). The PCP molecule is a six carbon ring with five chlorine atoms and one hydroxyl (OH) group attached. The chlorines and the ring structure are both problems for bacteria. S. chlorophenolica uses three enzymes in succession to break it down, as follows: the first one replaces one chlorine with OH. The resulting compound is toxic, but not quite as bad as PCP itself. The second enzyme is able to act on this compound to replace two chlorines, one after the other, with hydrogen atoms. The resulting compound, while still bad, is much easier to deal with, and the third enzyme is able to break the ring open. At this point, what is left of PCP is well on its way to being food for the bacterium. All three enzymes are required, so we have IC. How could this IC system have evolved? First of all, bacteria of this type could already metabolize some milder chlorophenols which occur naturally in small amounts. In fact the first and third enzymes were used for this. As a result the cell is triggered to produce them in the presence of chlorophenols. The second enzyme (called PcpC) is the most interesting one; the cell produces it in sufficient quantity to be effective all the time instead of just when it is needed in its normal metabolic role. Thanks to this unusual situation PcpC is available when it is needed to help eat PCP. The inefficient regulation of PcpC is evidently the key to the whole process. So far as biologists can tell, a recent mutation that changed the deployment of this enzyme is what made PCP degradation possible for this bacterium. It also happens that both PcpC and the first enzyme in the process are now slightly optimized for dealing with PCP; they handle it better than the corresponding enzymes in strains of S. chlorophenolica that use PcpC only in its normal role, but not nearly as well as would be expected for an old, well adapted system. These factors, combined with the fact that PCP is not known to occur naturally, make a strong circumstantial case that this system has evolved very recently. The chemistry and probable evolution of this system are explained in much greater detail in Shelly Copley's article "Evolution of a metabolic pathway for degradation of a toxic xenobiotic: the patchwork approach" in Trends in Biochemical Sciences ( 5 ). Hemoglobin for the Active Life Hemoglobin is a wonderful protein that picks up oxygen in our lungs and delivers it to the rest of our cells. Oxygen binds to hemoglobin very quickly in our lungs and stays bound. Then in our tissues oxygen is released very quickly. How does this happen? What we call a hemoglobin molecule is a complex of four hemoglobin chains, or subunits. There are two each of two different chains called alpha and beta hemoglobin. The complex binds reversibly to oxygen, one O 2 molecule per each subunit. It tends not to bind to the first oxygen until the oxygen concentration is fairly high, which is the usual situation in our lungs. Then the complex changes shape so that the next O 2 binds more readily, the third still faster, and the fourth faster yet. Then it holds the oxygen until the surrounding oxygen concentration is quite low, which happens in our tissues. When finally one oxygen is released, the next is released faster and so on. This mechanism for oxygen transport is much more efficient than can be achieved with alpha or beta hemoglobin alone, and allows for our active life style. It takes all four parts to do this; take away part of the complex and it doesn't work ( 6 ). So we have another IC system. Behe discusses hemoglobin briefly (pp 206-207), mainly commenting that it makes a poor case fo


Legacy route: /cs/icon_of_obfuscation/ Icon of Obfuscation: Jonathan Wells' book Icons of Evolution and why most of what it teaches about evolution is wrong Nick Matzke reviews Jonathan Wells's book, Icons of Evolution . Icons of Evolution purports to document that "students and the public are being systematically misinformed about the evidence for evolution." But it is Icons itself that is really misinforming the public. Read the full article (off-site) .


Meyer's Hopeless Monster

Legacy route: /cs/meyers_hopeless_monster/ Meyer's Hopeless Monster Alan Gishlick, Nick Matzke, and Wesley R. Elsberry critique the paper published by "intelligent design" advocate Stephen C. Meyer in Proceedings of the Biological Society of Washington in August, 2004. They conclude that Meyer's review paper presents an incomplete, misleading, and false impression of the biological evidence, and that his conclusion that "intelligent design" is supported because evolutionary alternatives are eliminated is illegitimate. Posted by Wesley R. Elsberry on August 24, 2004 05:56 PM Review of Meyer, Stephen C. 2004. The origin of biological information and the higher taxonomic categories. Proceedings of the Biological Society of Washington 117(2):213-239. by Alan Gishlick, Nick Matzke, and Wesley R. Elsberry [The views and statements expressed here are our own and not necessarily those of NCSE or its supporters.] “Intelligent design” (ID) advocate Stephen C. Meyer has produced a “review article” that folds the various lines of “intelligent design” antievolutionary argumentation into one lump. The article is published in the journal Proceedings of the Biological Society of Washington . We congratulate ID on finally getting an article in a peer-reviewed biology journal, a mere fifteen years after the publication of the 1989 ID textbook Of Pandas and People , a textbook aimed at inserting ID into public schools. It is gratifying to see the ID movement finally attempt to make their case to the only scientifically relevant group, professional biologists. This is therefore the beginning (not the end) of the review process for ID. Perhaps one day the scientific community will be convinced that ID is worthwhile. Only through this route — convincing the scientific community, a route already taken by plate tectonics, endosymbiosis, and other revolutionary scientific ideas — can ID earn a legitimate place in textbooks. Unfortunately, the ID movement will likely ignore the above considerations about how scientific review actually works, and instead trumpet the paper from coast to coast as proving the scientific legitimacy of ID. Therefore, we would like to do our part in the review process by providing a preliminary evaluation of the claims made in Meyer’s paper. Given the scientific stakes, we may assume that Meyer, Program Director of the Discovery Institute ’s Center for Science and Culture , the major organization promoting ID, has put forward the best case that ID has to offer. Discouragingly, it appears that ID’s best case is not very good. We cannot review every problem with Meyer’s article in this initial post, but we would like to highlight some of the most serious mistakes. These include errors in facts and reasoning. Even more seriously, Meyer’s paper omits discussion or even citation of vast amounts of directly relevant work available in the scientific literature. Summary of the paper Meyer’s paper predictably follows the same pattern that has characterized “intelligent design” since its inception: deny the sufficiency of evolutionary processes to account for life’s history and diversity, then assert that an “intelligent designer” provides a better explanation. Although ID is discussed in the concluding section of the paper, there is no positive account of “intelligent design” presented, just as in all previous work on “intelligent design”. Just as a detective doesn’t have a case against someone without motive, means, and opportunity, ID doesn’t stand a scientific chance without some kind of model of what happened, how, and why. Only a reasonably detailed model could provide explanatory hypotheses that can be empirically tested. “An unknown intelligent designer did something, somewhere, somehow, for no apparent reason” is not a model. Meyer’s paper, therefore, is almost entirely based on negative argument. He focuses upon the Cambrian explosion as an event he thinks that evolutionary biology is unable to account for. Meyer asserts that the Cambrian explosion represented an actual sudden origin of higher taxa; that these taxa (such as phyla) are “real” and not an artifact of human retrospective classification; and that morphological disparity coincides with phyletic categories. Meyer then argues that the origin of these phyla would require dramatic increases in biological “information,” namely new proteins and new genes (and some vaguer forms of “information” at higher levels of biological organization). He argues that genes/proteins are highly “complex” and “specified,” and that therefore the evolutionary origin of new genes is so improbable as to be effectively impossible. Meyer briefly considers and rejects several theories proposed within evolutionary biology that deal with macroevolutionary phenomena. Having rejected these, Meyer argues that ID is a better alternative explanation for the emergence of new taxa in the Cambrian explosion, based solely upon an analogy between “designs” in biology and the designs of human designers observed in everyday experience. The mistakes and omissions in Meyer’s work are many and varied, and often layered on top of each other. Not every aspect of Meyer’s work can be addressed in this initial review, so we have chosen several of Meyer’s major claims to assess. Among these, we will take up the Cambrian explosion and its relation to paleontology and systematics. We will examine Meyer’s negative arguments concerning evolutionary theories and the origin of biological “information” in the form of genes. An expanded critique of this paper is in preparation. Playing with Dynamite: The Cambrian Explosion The Cambrian explosion is a standard topic for antievolutionists. There are several reasons for this: many taxa make their first appearance in the Cambrian explosion; the amount of time within the period of the Cambrian explosion is geologically brief; and we have limited evidence from both within and before the Cambrian explosion on which to base analysis. The first two factors form the basis of an antievolutionary argument that evolutionary processes are insufficient to generate the observed range of diversity within the limited time available. The last factor is a general feature of the sorts of phenomena that antievolutionists prefer: not enough evidence has yet accrued to single out a definitive scientific account, so it is rhetorically easy for a pseudoscientific “alternative” to be offered as a competitor. In Meyer’s closing paragraph, he mentions “experience-based analysis.” The consistent experience of biologists is that when we have sufficient evidence bearing upon some aspect of biological origins, evolutionary theories form the basis of explanation of those phenomena (an example where much evidence has become available recently is the origin of birds and bird flight; see Gishlick 2004). Problems with Meyer’s discussion of the Cambrian Explosion: 1. Meyer tries to evaluate morphological evolution by counting taxa, a totally meaningless endeavor for investigating the evolution of morphology. Most paleontologists gave up taxa-counting long ago and moved on to more useful realms of research regarding the Cambrian (see Budd and Jensen 2000). This is perhaps why most of Meyer’s citations for this section are to his own articles (themselves not in relevant scientific journals). 2. Meyer repeats the claim that there are no transitional fossils for the Cambrian phyla. This is a standard ploy of the Young-Earth Creationists (see Padian and Angielczyk 1999 for extended discussion of this tactic and its problems). Meyer shows a complete lack of understanding of both the fossil record and the transitional morphologies it exhibits (even during the Cambrian explosion; for a recent example of transitional forms in the Cambrian explosion see Shu et al. 2004) as well as the literature he himself cites. (This topic has been dealt with before, as with DI Fellow Jonathan Wells. See Gishlick 2002 at http://www.ncseweb.org/icons/icon2tol.html .) 3. Meyer attempts to argue that the “gaps” in the fossil record reflect an actual lack of ancestors for Cambrian phyla and subphyla. To support this, Meyer cites some papers by University of Chicago reasearcher Mike Foote. However, of the two papers by Foote cited by Meyer, neither deals with the Cambrian/Precambrian records (one concerns the Middle and Late Paleozoic records of crinoids and brachiopods, the other the Mesozoic record of mammal clade divergence), or even transitional fossils. Foote’s papers deal with issues of taxonomic sampling: How well does a fossil record sample for a given time period reflect the biodiversity of that period? How well does a given fossil record pinpoint divergence times? Foote’s conclusions are that we have a good handle on past biodiversity, and that divergence times probably match appearance in the fossil record relatively closely. But Foote’s work utilizes organisms that are readily preserved. It doesn’t deal with organisms that aren’t readily preserved, a trait that almost certainly applies to the near-microscopic, soft-bodied ancestors of the Cambrian animals. According to Meyer’s argument, which doesn’t take into account preservation potential, microscopic metazoans such as rotifers must have arisen recently because they entirely lack a fossil record. Neither of Foote’s papers supports Meyer’s contention that the lack of transitional fossils prior to the Cambrian indicates a lack of ancestors. Lastly, it appears that fossils of the long-hypothesized small, soft-bodied precambrian worms have recently been discovered (Chen et al. 2004). Information and Misinformation For some, “information theory” is simply another source of bafflegab. And that appears to be the only role Meyer sees for “information theory”. After brief nods to Shannon and algorithmic information theory, Meyer leaves the realm of established and accepted information theoretic work entirely. 1. Meyer invokes Dembski’s “specified complexity”/”complex specified information” (SC/CSI) as somehow relevant to the Cambrian explosion. However, under Dembski’s technical definition, CSI is not just the conjoint use of the nontechnical words “specified” (as in “functional”) and “complexity”, as Meyer erroneously asserts. According to Dembski’s technical definition, improbability of appearance under natural causes is part of the *definition* of CSI. Only after one has determined that something is wildly improbable under natural causes can one conclude that something has CSI. You can’t just say, “boy, that sure is specific and complicated, it must have lots of CSI” and conclude that evolution is impossible. Therefore, Meyer’s waving about of the term “CSI” as evidence against evolution is both useless for his argument, and an incorrect usage of Dembski (although Dembski himself is very inconsistent, conflating popular and technical uses of his “CSI,” which is almost certainly why Meyer made this mistake. See here for examples of definitional inconsistency.). 2. Meyer relies on Dembski’s “specified complexity,” but even if he used it correctly (by rigorously applying Dembski’s filter, criteria, and probability calculations), Dembski’s filter has never been demonstrated to be able to distinguish anything in the biological realm — it has never been successfully applied by anyone to any biological phenomena (Elsberry and Shallit, 2003). 3. Meyer claims, “The Cambrian explosion represents a remarkable jump in the specified complexity or ‘complex specified information’ (CSI) of the biological world.” Yet to substantiate this, Meyer would have to yield up the details of the application of Dembski’s “generic chance elimination argument” to this event, which he does not do. There’s small wonder in that, for the total number of attempted uses of Dembski’s CSI in any even partially rigorous way number a meager four (Elsberry and Shallit, 2003). 4. Meyer claims, “One way to estimate the amount of new CSI that appeared with the Cambrian animals is to count the number of new cell types that emerged with them (Valentine 1995:91-93)” (p.217). This may be an estimate of something, and at least signals some sort of quantitative approach, but we may be certain that the quantity found has nothing to do with Dembski’s CSI. The quantitative element of Dembski’s CSI is an estimate of the probability of appearance (under natural processes or random assembly, as Dembski shifts background assumptions opportunistically), and has nothing to do with counting numbers of cell types. Of Text and Peptides 1. Meyer argues that “many scientists and mathematicians have questioned the ability of mutation and selection to generate information in the form of novel genes and proteins” (p. 218). He makes statements to this effect throughout the paper. Meyer does not say who these scientists are, and in particular does not say whether or not any of them are biologists. The origin of new genes and proteins is actually a common, fairly trivial event, well-known to anyone who spends a modicum of effort investigating the scientific literature. The evolution of new genes has been observed in the lab, in the wild, inferred in great detail between closely-related modern species, and reconstructed in hundreds of cases by comparing the genomes from organisms sequenced in genome projects over the last decade (see Long 2001 and related articles , and below). 2. Meyer compares DNA sequences to human language. In this he follows Denton’s (1986) Evolution: A Theory in Crisis . Denton (1986) argued that meaningful sentences are isolated from each other: it is usually impossible to convert one sentence to another via a series of random letter changes, where each intermediate sentence has meaning. Like Denton (1986), Meyer applies the same argument to gene and protein sequences, concluding that they, like meaningful sentences, must have been produced by intelligent agents. The analogy between language and biological sequence is poor for many reasons; starting with the most obvious point of disanalogy, proteins can lose 80% or more of their sequence similarity and retain the same structure and function (a random example is here ). Let’s examine an English phrase where four out of five characters have been replaced with a randomly generated text string. See if you can determine the original meaning of this text string: Tnbpursutd euckilecuitn tiioismdeetneia niophvlgorciizooltccilhseema er [1] Eighty percent loss of sequence identity is fatal to English sentences. Clearly proteins are much less specified than language. 3. Meyer cites Denton (1986) unhesitatingly. This is surprising because, while Denton advocated in 1986 that biology adopt a typological view of life, he has abandoned this view (Denton 1998). Among other things, Denton wrote, “One of the most surprising discoveries which has arisen from DNA sequencing has been the remarkable finding that the genomes of all organisms are clustered very close together in a tiny region of DNA sequence space forming a tree of related sequences that can all be interconverted via a series of tiny incremental natural steps.” (p. 276) Denton now accepts common descent and disagrees with the “intelligent design” advocates who conjecture the special creation of biological groups, regularly criticizing them for ignoring the overwhelming evidence (Denton 1999). 4. Meyer’s case that the evolution of new genes and proteins is essentially impossible relies on just a few references from the scientific literature. For example, Meyer references Taylor et al. 2001, a paper entitled “Searching sequence space for protein catalysts” and available online at the PNAS website. But Taylor et al.’s recommendation for intelligent protein design is actually that it should mimic natural evolution: “[A]s in natural evolution, the design of new enzymes will require incremental strategies…”. There is a large mass of evidence supporting the view that proteins are far less “specified” than Meyer asserts. Fully reviewing this would require an article in itself, and would be somewhat beside the point since Meyer’s claim is categorically disproven by the recent origin of novel genes by natural processes. (Another way in which “experience-based analysis” leads one to conclusions other than those Meyer asserts.) However, some idea of the diversity of protein solutions to any given enzymatic “problem” is given at the NCBI’s Analogous Enzymes webpage , which includes hundreds of examples. There is more than one way to skin a cat, and there are many more ways to evolve a solution to any given functional “problem” in biology. The origin of novel genes/proteins Meyer makes his case that evolution can’t produce new genes in complete neglect of the relevant scientific literature documenting the origin of new genes. 1. A central claim of Meyer’s is that novel genes have too much “CSI” to be produced by evolution. The first problem with this is that Meyer does not demonstrate that genes have CSI under Dembski’s definition (see above). The second problem is that Meyer cites absolutely none of the literature documenting the origin of new genes. For example, Meyer missed the recent paper in Current Opinion in Genetics and Development with the unambiguous title, “Evolution of novel genes.” The paper and 183 related papers can be found here . Many other references can be found linked from here . It is worth listing a few in-text to make crystal-clear the kinds of references that Meyer missed: Copley, S. D. (2000). “Evolution of a metabolic pathway for degradation of a toxic xenobiotic: the patchwork approach.” Trends Biochem Sci 25(6): 261-265. PubMed Harding, M. M., Anderberg, P. I. and Haymet, A. D. (2003). “‘Antifreeze’ glycoproteins from polar fish.” Eur J Biochem 270(7): 1381-1392. PubMed Johnson, G. R., Jain, R. K. and Spain, J. C. (2002). “Origins of the 2,4-dinitrotoluene pathway.” J Bacteriol 184(15): 4219-4232. PubMed Long, M., Betran, E., Thornton, K. and Wang, W. (2003). “The origin of new genes: glimpses from the young and old.” Nat Rev Genet 4(11): 865-875. PubMed Nurminsky, D., Aguiar, D. D., Bustamante, C. D. and Hartl, D. L. (2001). “Chromosomal effects of rapid gene evolution in Drosophila melanogaster.” Science 291(5501): 128-130. PubMed Patthy, L. (2003). “Modular assembly of genes and the evolution of new functions.” Genetica 118(2-3): 217-231. PubMed Prijambada I. D., Negoro S., Yomo T., Urabe I. (1995). “Emergence of nylon oligomer degradation enzymes in Pseudomonas aeruginosa PAO through experimental evolution.” Appl Environ Microbiol. 61(5):2020-2. PubMed Ranz, J. M., Ponce, A. R., Hartl, D. L. and Nurminsky, D. (2003). “Origin and evolution of a new gene expressed in the Drosophila sperm axoneme.” Genetica 118(2-3): 233-244. PubMed Seffernick, J. L. and Wackett, L. P. (2001). “Rapid evolution of bacterial catabolic enzymes: a case study with atrazine chlorohydrolase.” Biochemistry 40(43): 12747-12753. PubMed 2. Meyer cites Axe (2000) as a counter to the evolutionary scenario of successive modifications of genes leading to new protein products. But Axe (2000) is not in any sense about “successive modifications”; Axe modified proteins in several locations at a time. ID advocates love to cite certain Axe papers that indicate that functional proteins are rare in sequence space, but not others that indicate the opposite (Axe et al., 1996). Axe apparently said in 1999 that his work had no relevance to intelligent design. 3. Meyer portrays protein function as all-or-nothing. But protein function is not all-or-nothing. Recent research highlights several evolutionary mechanisms “tinkering” with existing genes to arrive at new genes (Prijambada et al. 1995; Long 2001). But you won’t learn about that from Meyer. 4. As far as we can tell, Meyer uses the word “duplication” or something similar only twice in the entire 26-page article. One of these usages is in the references, in the title of an article referring to cent


Legacy route: /cs/moonshine/ Moonshine: Why the Peppered Moth Remains an Icon of Evolution Matt Young takes up the criticisms that "intelligent design" advocates make about peppered moths and the studies showing that natural selection acted on their populations. The peppered moth example is still an excellent example of natural selection in action. by Matt Young Department of Physics Colorado School of Mines www.mines.edu/~mmyoung Version 1.0, Copyright © 2004 by Matt Young [Posted: February 11, 2004] Contents Introduction Kettlewell's Experiments Table 1: Kettlewell's release-and-recapture data Figure 1: The number of moths Kettlewell recaptured as a function of the number he released the previous night. Figure 2: Kettlewell's recapture rate as a function of time. Mathematical Model Figure 3: A mathematical model fitted to Kettlewell's data. Moonlight Figure 4: Kettlewell's daily recapture rate as a function of relative exposure to moonlight. Conclusion References Introduction ernard Kettlewell did not cheat, or, more precisely, the evidence does not support the insinuation, widely repeated on the Internet, that he cheated. Kettlewell was a distinguished naturalist whose studies on predation in peppered moths were a landmark in demonstrating natural selection in the wild. His studies are widely quoted and often used in textbook accounts of natural selection. Judith Hooper (2002), a journalist, strongly suggests, however, that Kettlewell fraudulently altered the results of his famous studies, and others have uncritically accepted her suggestion. Reviews of Hooper's book in the scientific literature are at best mixed (Coyne 2002, Grant 2002, Shapiro 2002), and experts on moth behavior remain convinced that the story of the peppered moth is sound (Cook, 2000, 2003; Grant 1999; Majerus 1998, 2003; Mallet 2004). More pertinently, Kettlewell's data are completely consistent with normal experimental variation, and Hooper's insinuations are groundless. Kettlewell's Experiments What did Kettlewell do, and why does Hooper think he fudged his data? Beginning in the mid-1800's, successive generations of peppered moths ( Biston betularia ) in Britain gradually darkened in response to the air pollution in the industrialized parts of the country. Specifically, a genetically determined dark, or melanic, form of the moth replaced the lighter form as industrial pollution killed lichens on the barks of trees and also coated the bark with a layer of soot. The effect has come to be known as industrial melanism, and its existence is not in dispute (see [Forrest and Gross, 2004: 107-111] for a review). Kettlewell (1955, 1956, 1959) showed that the melanic form of the moth predominated primarily because of predation by birds. He did not think that predation was the only cause of industrial melanism and in fact speculated as to the relative strengths of other causes. Briefly, he performed a number of experiments (Musgrave 2004, Grant 1999, Kettlewell 1959): Release-recapture experiments. Kettlewell marked and released both light-colored and melanic moths early in the morning, and recaptured some the next night. In polluted woods, he and his assistants recaptured more melanic moths than light-colored (1955, 1956), whereas in unpolluted woods they captured more light-colored than melanic (1956). Direct observation (1955, 1956) and filming (1956). Kettlewell and others observed birds eating moths directly off trunks of trees. Camouflage. Kettlewell visually ranked the effectiveness of camouflage of moths on different backgrounds and compared the effectiveness of camouflage with predation rates both in an aviary and in the field. He did not know that birds had ultraviolet vision, which his observers lacked, but got nevertheless a good correlation between camouflage and predation. Later research has shown that the moths are camouflaged in the ultraviolet as well as in the visible (Musgrave 2004). Geographical distribution. He noted that the distribution of the melanic moths in the country closely matched the areas of industrialization (Bishop and Cook 1957). I will discuss only the release-recapture experiments reported in (Kettlewell 1955), because these are the experiments that are under fire and because (unlike Kettlewell's critics) we can bring quantitative tools to bear. For a more general analysis, see (Musgrave 2004; Grant 1999). Kettlewell reported releasing and recapturing moths during an 11-day period in 1953. His data are reproduced in Table 1 (1955: 332). The numbering of the days is mine. Table 1: Kettlewell's release-and-recapture data. Day Date a Number released Number recaptured 0 25 June 32 - 1 26 June 0 5 2 27 June 59 2 3 28 June 63 4 4 29 June 0 9 5 30 June 102 2 6 1 July 114 23 7 2 July 98 34 8 3 July 83 29 9 4 July 79 25 10 5 July 0 16 Totals 630 149 a Recaptures reported as 26 June took place between the evening of 25 June and the morning of 26 June. Hooper has noted that the number of recaptures increased sharply on 1 July, the same day that E. B. Ford sent a letter to Kettlewell. Ford's letter commiserated with Kettlewell for the low recapture rates but suggested that the data would be worthwhile anyway. The letter is unremarkable, and two facts militate against a finding of fraud. First, Kettlewell finished collecting data in the wee hours of the morning and therefore could not have received the letter before collecting his data on 1 July. He markedly increased the number of moths he released on 30 June, the day before the letter was mailed, not 1 July. Additionally, as Hooper admits, he continued to release more moths after 30 June. Not surprisingly, he also captured more moths: more moths released, more captured. Figure 1: The number of moths Kettlewell recaptured as a function of the number he released the previous night. The relationship is linear with a high correlation. Indeed, Figure 1 plots recapture rate as a function of the number of moths released on any day. The line is a line of best fit constrained to pass through the origin on the assumption that no moths are recaptured if none are released. Figure 1 shows that the recapture rate is very nearly a linear function of the number released. The square r 2 of the correlation coefficient is 0.80 and suggests that most of the variation of the number of recaptures is accounted for by variation of the number of releases. The fit improves only slightly if the line is not constrained. Why did Kettlewell release more moths beginning on 30 June? He released both moths he had reared and moths he had captured. Because the moths were just hatching, he had limited control of the number he could release on any given day. There is no reason to suspect that the increased numbers of releases reflect anything other than the number of moths that were available. At any rate, Ford's letter could not have influenced his decision to release more moths because it arrived after Kettlewell's first big release on 30 June. Figure 2: Kettlewell's recapture rate as a function of time, ignoring the 2 days following 0 releases. Note that, owing to the vagaries of graphing programs, the days are not numbered correctly. Still, his recapture rate, as well as the absolute number of moths recaptured, increased from 12 % over the first 3 days of his experiment to 26 % over the last 3 days. More pointedly, if we plot his recapture rate as a function of time, as in Figure 2, we find what looks to the eye as a sudden increase. Figure 2 omits those days, 27 June and 30 June, that were preceded by 0 releases. It is hard to make much out of a mere 8 data points, but the recapture rate certainly appears to the casual observer to increase sharply after 1 day of inactivity. Biological field data, however, display significant random variation, and the eye often infers patterns in random data, so let us perform a quantitative analysis to see whether Kettlewell's data are what we would expect given normal experimental variations. Specifically, let us construct a mathematical model and see how well it describes Kettlewell's data. Mathematical Model Kettlewell recaptured most of his moths after they had been in the wild for only 1 day, but he recaptured some after 2 days. Let us therefore define a 1-day recapture rate R 1 and a 2-day recapture rate R 2 as the ratios of the numbers of moths recaptured after 1 and 2 days in the wild. Kettlewell reported no 3-day recaptures. We may estimate the 2-day recapture rate by looking at the 4 moths captured on days 2 and 5. No moths were released on the preceding days, but 2 days before, a total of 63 -- 32 = 95 moths had been released, so R 2 = 4/95, or approximately 4 %. The overall recapture rate is given by the row labeled "Totals" and is R = 149/630, or approximately 24 %. The 1-day recapture rate is the difference R 1 = R - R 2 between the two values, or about 19 % (the numbers do not add exactly because of round-off error). R 2 is very nearly equal to the square of R 1 , as we would expect if the model is appropriate. Our mathematical model is straightforward: The number of moths captured on any given night is equal to the number of moths released the day before times the 1-day recapture rate, plus a similar term, the number of moths released 2 days before times the 2-day recapture rate. The results of a calculation based on this model are shown as the solid curve in Figure 3. Note that I have made no artificial assumptions, such as adjusting the recapture rates to get a good fit to the data, in constructing Figure 3. The points in Figure 3 are Kettlewell's data, and the solid curve is the model. How well does the model fit the data? To answer that question, we have to estimate the normal range of variability in the data. In statistical terms, we calculate the standard uncertainty of the data points. The standard uncertainty is a number that tells us, in this case, how much variation we might expect if we repeated the experiment many times. Figure 3: A mathematical model fitted to Kettlewell's data. The points are the experimental data; the solid curve is the model, and the error bars are 95% confidence. The dashed curve is the model corrected for exposure to moonlight. By way of introduction, suppose that you toss N marbles at a hole in a table. Count the number of marbles that fall through the hole, and repeat the experiment many times. Suppose that the average number of marbles that fall through the hole is M . You will not count M marbles every time you perform the experiment; to the contrary, the number will vary about M and very possibly will never exactly equal M . Thus, we talk of the probability p that any one marble passes through the hole and set it equal to the ratio M / N . The mean number of marbles that pass through the hole is equal to Np . How much will any one toss differ from M ? Assume that the number of marbles that pass through the hole is described by a binomial distribution. Then the standard deviation of M is . On approximately 19 tosses in 20, you will record a number that is between M - 2 σ and M + 2 σ , so 2 σ is most commonly used as a measure of uncertainty. The uncertainty 2 σ can be surprisingly large. For example, if p is 0.24 (the average recapture rate in Kettlewell's experiment) and N is 102 (the number of moths Kettlewell released on 30 June), then M is 24 and 2 σ is about 8. You can expect anywhere between 16 and 32 marbles to fall through the hole on any given toss. You should not be especially surprised by any number unless it is much less than 12 or much more than 36. Thus, the day-to-day variation in an experiment such as Kettlewell's can easily be 100 % or more. This fact alone should militate against a charge of fraud. On any given day, Kettlewell released N moths and recaptured M moths. Because the 2-day recapture rate is small, the statistics are essentially the same as those of the marble example. The number M is highly variable, as we have seen; on another day, he might capture a substantially different number, even if conditions were unchanged. The standard uncertainty u estimates the probable variation of M quantitatively (ISO 1993). It is given by , where the recapture rate R replaces the probability p . We apply this formula to each data point, using the overall recapture rate R for most days but the 2-day recapture rate R 2 for the 2 days that were preceded by 0 releases. The result is shown in Figure 2 as a series of error bars. The error bars represent ±2 u , an interval called the 95 % confidence interval. If we take a single measurement, then we may estimate that the true value (the average of a great many measurements) falls within the error bars, with 95 % probability. Inasmuch as the model (the solid curve) passes through virtually every error bar, it may be said to be a nearly perfect fit to the data, however poor it might appear in the absence of error bars. The points on days 7, 8, and 9 lie noticeably above the curve. If the data were completely unbiased, then we would expect about a 50-50 chance that any one of those points lay above the curve. The odds that 3 consecutive points lie above the curve are 1 in 8, exactly the same as the odds against tossing 3 heads in a row and by no means improbable enough to base a charge of cheating. Even if 5 points lay above the curve, the odds against would be 1 in 32, again, not very impressive in its improbability. Additionally, 2 consecutive data points lie noticeably below the curve. In summary, the last 5 of Kettlewell's data points are higher than the first 5. This meager fact, combined with the anecdotal evidence of Ford's letter, is all that led Hooper (2003) to infer that Kettlewell cheated. In reality, the timing of Ford's letter belies Hooper's inference, and Kettlewell's data are completely consistent with normal experimental variation. Moonlight The differences between the data and the curve are not statistically significant; the observed variation very probably is the result of chance. It is, however, possible that the deviations from the curve are "real", that is, due to some systematic effect, or systematic error, not due solely to random error. It is very hard, unfortunately, to track down a source of systematic error when that error is itself less than the standard uncertainty of the data set; the systematic error is said to be lost in the noise. Hooper tells us that the weather was stable and could not have accounted for the increase in the number of recaptures (though her description suggests somewhat variable winds). We have, nevertheless, a strong candidate that can account for the systematic deviations of our simple model from the curve: the phase of the moon. Shapiro (2002), in his review of Hooper's book, suggests that moonlight interferes with moth trapping, a possibility that Hooper and her informant, biologist Ted Sargent, should have investigated. The moon was full on 27 June (that is, the night of 26-27 June). By 2 July, the moon was 5 days past full but visible for only part of the night. Thus, the total exposure to the moon, the product of illuminance (brightness) and time, was approximately one-quarter what it was during the full moon, and it dropped steadily over the next few days. Clarke and his colleagues (1990) have investigated the effect of the phase of the moon on capture rates of peppered moths in a single environment over 30 years and concluded that the moon does not affect capture rates. Unfortunately, theirs was a retrospective study, and they did not record weather data, that is, did not control for cloudy or rainy days. They averaged the data over 5-day periods surrounding the full moon and did not use the actual exposure to the moonlight (as defined above). All of these factors will reduce the correlation between capture rates and exposure to moonlight. Even so, they calculated a small but not statistically significant correlation that suggests a slight increase of capture rate around the full moon. In addition, when they checked the new moon against the full moon, they calculated a small, barely significant increase, which they discounted. Possibly the effect is due to the presence of streetlights, to which they refer obliquely, and which may attract moths away from the stronger mercury vapor light only when the moon is dark. At any rate, they conclude that moonlight does not affect capture rates. Kettlewell worked on clear days only; I do not think that the conclusion of Clarke and colleagues is necessarily pertinent. Figure 4: Kettlewell's daily recapture rate as a function of relative exposure to moonlight. The equation is a line of best fit that relates exposure to recapture rate. Thus, I examined Kettlewell's data in hope of quantifying the effect of the moon on his recapture rates (1955: 332, Table 5). I obtained data that gave the moon's magnitude (an astronomical term that is related to its brightness) and the duration during which the moon was visible each night during Kettlewell's experiment. I plotted Kettlewell's daily recapture rate as a function of the exposure to the moon (the product of brightness and time, as defined above). I made no effort to control for the elevation of the moon. The result is shown in Figure 4, which plots Kettlewell's daily recapture rate as a function of lunar exposure normalized to the value 1 on the night of the full moon. The equation in Figure 4 is the equation of the line of best fit to the data. The daily recapture rate rises by a factor of 3 as the brightness of the moon decreases. (We could perform a similar calculation using Kettlewell's total captures [1955: 333, Table 6], but such a calculation is complicated by the fact that the moths emerge from their cocoons haphazardly, whereas the recapture rate is based on a known distribution of released moths. Still, the calculation based on total captures yields much the same result as that outlined below.) Using the line in Figure 4, I adjusted the calculated recapture rates according to the equation, , where R represents the nightly recapture rate used in the model that led to Figure 3, R ' is the nightly recapture rate modified to include the effect of lunar exposure, is the average daily recapture rate, and E is the nightly exposure to moonlight. The result of the calculation is shown in Figure 3 as the light, dashed curve. It demonstrates a somewhat better fit to the data than the solid curve, especially during the first few days. Instead of asking why Kettlewell's recapture rate was high on 1 July and thereafter, Hooper should have asked why it was so low on 30 June and before. Conclusion Kettlewell's data are simply accounted for by the unsurprising fact that you can recapture more moths when you release more, that and normal experimental variation. When the effect of moonlight is included in the calculation, the calculated curve fits even closer to Kettlewell's data. We have no need of Hooper's perverse, ad-hoc hypothesis. Hooper's claims are moonshine; they are based on a lack of understanding of Kettlewell's experiments in particular and experimental science in general. Hooper evidently did not consider the most likely cause of the changes she saw, exposure to moonlight, let alone realize that the change in recapture numbers began before Kettlewell could have read the letter that supposedly triggered this change. Hooper and Sargent should have performed a careful analysis before Hooper presumptuously insinuated fraud. Kettlewell's conclusion, that predation by birds was a major factor in promoting industrial melanism, was based on at least 4 lines of inquiry, as detailed above. It did not rely on the release-recapture experiments alone. It is also supported by at least 30 studies of different moth species that also developed melanic forms (Grant, 1999). In other words, an enormous body of evidence supports Kettlewell's conclusion. Even if Kettlewell's release-recapture experiments were ruled out, we would still b


Introduction

Legacy route: /cs/pigdid_intro/ Introduction Reed Cartwright provides an introduction to the series of chapter reviews of Jonathan Wells's book. The Politically Incorrect Guide to Darwinism and Intelligent Design Review: Introduction Posted by Reed A. Cartwright on August 25, 2006 12:00 AM Jonathan Wells (2006) The Politically Incorrect Guide to Darwinism and Intelligent Design . Regnery Publishing, Inc. Washington, DC. Amazon Read the entire series. Jonathan Wells is one of the most notorious activists of the political ad campaign known as “intelligent design”. He is most well known for his attacks on modern biology, specifically his 2000 book, Icons of Evolution , which was panned by the scientific community for its fraudulent presentation of modern biology. Does Jonathan Wells, aiming once again at the popular market, restore his scientific and academic reputation with his latest book, The Politically Incorrect Guide to Darwinism and Intelligent Design , or is it just old trash in a new bag? To find out, you will need to read our multi-part review, which begins tomorrow. One thing is for sure, Jonathan Wells is too modest. His recently published, The Politically Incorrect Guide to Darwinism and Intelligent Design , is not only politically incorrect but incorrect in most other ways as well: scientifically, logically, historically, legally, academically, and morally. Jonathan Wells has a Masters of Religious Education from Unification Theological Seminary, a Ph.D. in Religious Studies from Yale University, and a Ph.D. in Molecular and Cell Biology from the University of California: Berkeley. His scientific output is nearly non-existent, consisting of a couple co-authored papers from his days as graduate student and postdoc. However, he was highly motivated to get advanced degrees, as he wrote in an article about his education: [Rev. Sun Myung Moon’s] words, my studies, and my prayers convinced me that I should devote my life to destroying Darwinism, just as many of my fellow Unificationists had already devoted their lives to destroying Marxism. When Father chose me (along with about a dozen other seminary graduates) to enter a Ph.D. program in 1978, I welcomed the opportunity to prepare myself for battle…. As a graduate student at Yale, I studied the whole of Christian theology but focused my attention on the Darwinian controversies. I wanted to get to the root of the conflict between Darwinian evolution and Christian doctrine…. When I finished my Yale Ph.D., I felt confident that I understood the theological basis of the conflict between Darwinism and theism. But Darwinism was clearly winning the ideological battle in the universities, the public schools, and the mass media, largely because it claimed to be supported by scientific evidence. I knew enough about biology to know that this claim was quite shaky, but few scientists were willing to challenge it. Those who did were often lumped together with young-earth biblical fundamentalists and thereby discredited in the eyes of most scholars. I eventually decided to join the fray by returning to graduate school in biology. I was convinced that embryology is the Achilles’ heel of Darwinism; one cannot understand how organisms evolve unless one understands how they develop. In 1989, I entered a second Ph.D. program, this time in biology, at the University of California at Berkeley…. ( Wells J. “Darwinism: Why I Went for a Second Ph.D.” ) Did You Know? Hundreds of scientific articles are published every month on evolution. There is no scientific controversy over evolution. The scientific community considers “intelligent design” to be unscientific. Furthermore, Jonathan Wells is a Senior Fellow with the Discovery Institute’s Center for the Renewal of Science and Culture, a public policy think tank located in Seattle, Washington. The Discovery Institute is the epicenter of “intelligent design” activism, which took a major blow when Kitzmiller v. Dover Area School District effectively declared it religiously motivated pseudoscience, unfit for public schools. Now in this first year after Dover, the “intelligent design” activists have been busy picking up the pieces, trying to hide their defeat in Dover behind a “new” marketing campaign. The Politically Incorrect Guide to Darwinism and Intelligent Design , for which the Discovery Institute is holding a party , is part of this marketing campaign, and because of all this, one might reasonably argue that, in addition to the author and the publisher, the Discovery Institute bears responsibility for the poor quality of this book. Now, an interesting thing about The Politically Incorrect Guide to Darwinism and Intelligent Design is that it is fat with specious criticisms of modern biology but nearly emaciated when it comes to “intelligent design”. Nowhere can one find any information on when a designing agent might have designed or how a designing agent manufactured its designs in matter and energy. In fact there is not a single, clear statement of what was and wasn’t designed. So while the title is modest in some respects, it’s also incorrect in one more: there’s no guide to “intelligent design” in The Politically Incorrect Guide to Darwinism and Intelligent Design . Ah, well. Maybe next year. Or the year after that. Or the year after … pigs fly. That is it for this brief introduction. Be back tomorrow for the first installment of our mutli-part review of The Politically Incorrect Guide to Darwinism and Intelligent Design , “Why Should Words Have Meanings? (Chapter 1)” by Burt Humburg .