I've been shopping around trying to find a fairly easy way to combine audio with PowerPoint slides to post lectures online. I tried some movie-editing software, but that had some problems. Then I saw that Carl Zimmer over at The Loom used a piece of software called Soundslides. I thought I'd try Soundslides with a lecture I delivered recently.
(The lecture was to help inaugurate a new organization at UCSB called SUB - Society for Undergraduate Biologists. I was asked to speak about undergraduate research. I chose to talk about "paths to undergraduate research"; my own path, the path of one undergraduate from my lab, and the path that others might take to undergraduate research.)
I concur with Carl's comments that Soundslides is mostly nice, but determining the timing for when the slides change is a bit of a hassle. The quality of the slides ends up to be very high, because it uses jpeg graphics within Macromedia Flash, I gather. Other methods make a movie, and the graphics don't come across as well, or they take much more memory. Anyway, I post the result from soundslides below, which took me a few hours to complete. There is one more program I want to try that was recommended to me, I will try to post comparisons here, in case anyone is interested. After that, I want to buy the program I like best and then get many of my lectures from my Macroevolution course online, including lectures on the basics of phylogeny reconstruction. Then I just point students to the online lectures, retire from teaching, and focus on research (wink)...
Friday, November 28, 2008
Monday, November 24, 2008
How Complexity Evolves
[Slightly altered excerpt from an invited review on the evolution of (nervous system) complexity]
The evolution of complexity is an enduring and fundamental topic in biology. Recent research is allowing new insights into the origins of complexity. Namely, scientists now have access to the components of complex structures, and their evolutionary histories. "The eye" is no longer an anonymous collection of components - Darwin may have known an eye is composed of lens retina and nerve - but he did not know what lenses or retinas were composed of, nor did he know in much detail how they functioned. We now know many of the protein components of structures like eyes and how they function, and we know that these components have evolutionary histories. Since biological entities from genes to ecosystems arise from existing entities, general patterns emerge as to where those entities came from. They either duplicate/diverge; split/diverge; or fuse in new combinations.
What is biological complexity?
Numerous definitions have been suggested for biological complexity, from mathematical and information theoretic formulas [2,3] to intuitive definitions, such as the numbers of parts or components that can be counted in a system [4,5]. In practice, a researcher’s choice of definition for complexity often boils down to his ability to measure it. In investigator-defined computer simulations [2] or circumscribed biological systems [3], information theoretic definitions using explicit mathematical formulas are tractable. However, in extensive biological systems, especially those that encompass multiple levels of biological organization with varied properties, the relationship between structure and function (e.g. genotype and phenotype) is often largely unknown, and may itself be complicated and variable. These unknowns complicate the formulation of explicit, yet general, mathematical definitions of complexity for extensive biological systems. Instead, some have argued that counting the numbers of parts of a system is an appropriate measure of complexity [4]. Although often focused on structural units, such as cell types, genes, or species, and not on the functions of those units, McShea [5] has also argued that counting structural parts should often be a good estimate of functional complexity.
Herein, we follow previous authors [4,5] and define more complex systems as those that have more parts. “Part” is used as a term that spans levels of biological organization [4]. Species are parts of ecological communities. Organs (like brains or ganglia) are parts of species. Cell types (like neuronal types) are parts of organs and of species. Proteins are parts of cells and of networks, and domains and amino acids are parts of proteins. Many other biological units are also parts [6]. For the current discussion, we are concerned less with defining or counting parts, and concerned more with how new parts originate during evolution. According to the definition of complexity that we employ, new parts (that do not come at the expense of existing parts) increase biological complexity. Therefore, those mechanisms that cause the evolution of new parts are of particular interest because those are the mechanisms that cause increases in biology complexity.
General patterns of increased complexity
By definition, complex systems have many parts, and the histories of those parts are varied. As such, we cannot expect a simple, one dimensional answer to the question of how complexity evolved. Nevertheless, at all levels of biological organization, conceptually similar patterns (Figure 1) have resulted from a varied array of mechanisms that historically increased complexity.
Figure 1 - Generalized patterns of increased biological complexity. The top of the figure represents an ancestral state, the bottom a descendant state. Shapes represent “parts”, a generic term for a biological feature at any level of organization. Parts can be species, genes, protein domains, pathways, brain regions, and many other biological units [see 6]. A. Copying and divergence B. Fission and divergence C. Copying and fusion. Without copying or fission, complexity does not increase because divergence (or fusion) maintains the same number of parts. Without differential divergence or fusion of parts, complexity only increases marginally.
First, parts may exhibit a pattern consistent with differential divergence of copied elements (Fig. 1a). A prime example of a specific mechanism leading to this pattern is gene duplication plus divergence. Duplicated genes are initially identical, and they gradually diverge over time, increasing genomic complexity. Second, parts may exhibit a pattern consistent with differential divergence of split elements (Fig. 1b). Here, a prime example is speciation, where populations of individual organisms, originally all of the same species, split into multiple populations that diverge to the point of becoming separate species. Splitting (fission) can also occur in an asymmetric fashion (Fig 1c), generating two uncoupled parts that together would sum to one ancestral part. Third, parts may exhibit a pattern consistent with fusion of copied parts (Fig. 1d). For example, copied protein domains often join together to generate a new gene. Another example of fusion is expression of genes in new combinations, a process termed co-option [reviewed in 7]. A primary goal of this paper is to review cases in nervous system evolution that provide specific and more detailed mechanisms that account for these patterns at different levels of biological organization.
The patterns in figure 1 are produce by a number of different mechanisms, including gene duplication, alternative splicing, retrotransposition, co-option, etc.
[The specific mechanisms leading to the general patterns is the topic of the rest of the paper from which this excerpt is taken].
References Cited
1. Striedter GF: Principles of Brain Evolution. Sunderland, MA: Sinauer; 2005.
2. Adami C, Ofria C, Collier TC: Evolution of biological complexity. Proceedings of the National Academy of Sciences of the United States of America 2000, 97:4463-4468.
3. Adamowicz SJ, Purvis A, Wills MA: Increasing morphological complexity in multiple parallel lineages of the Crustacea. Proceedings of the National Academy of Sciences of the United States of America 2008, 105:4786-4791.
4. Bonner JT: The Evolution of Complexity. Princeton: Princeton University Press; 1988.
5. McShea DW: Functional complexity in organisms: Parts as proxies. Biology & Philosophy 2000, 15:641-668.
6. McShea DW, Venit EP: What is a part? In The Character Concept in Evolutionary Biology. Edited by Wagner GP: Academic Press; 2001:259-284.
7. True JR, Carroll SB: Gene co-option in physiological and morphological evolution. Annual Review of Cell and Developmental Biology 2002, 18:53-80.
The evolution of complexity is an enduring and fundamental topic in biology. Recent research is allowing new insights into the origins of complexity. Namely, scientists now have access to the components of complex structures, and their evolutionary histories. "The eye" is no longer an anonymous collection of components - Darwin may have known an eye is composed of lens retina and nerve - but he did not know what lenses or retinas were composed of, nor did he know in much detail how they functioned. We now know many of the protein components of structures like eyes and how they function, and we know that these components have evolutionary histories. Since biological entities from genes to ecosystems arise from existing entities, general patterns emerge as to where those entities came from. They either duplicate/diverge; split/diverge; or fuse in new combinations.
What is biological complexity?
Numerous definitions have been suggested for biological complexity, from mathematical and information theoretic formulas [2,3] to intuitive definitions, such as the numbers of parts or components that can be counted in a system [4,5]. In practice, a researcher’s choice of definition for complexity often boils down to his ability to measure it. In investigator-defined computer simulations [2] or circumscribed biological systems [3], information theoretic definitions using explicit mathematical formulas are tractable. However, in extensive biological systems, especially those that encompass multiple levels of biological organization with varied properties, the relationship between structure and function (e.g. genotype and phenotype) is often largely unknown, and may itself be complicated and variable. These unknowns complicate the formulation of explicit, yet general, mathematical definitions of complexity for extensive biological systems. Instead, some have argued that counting the numbers of parts of a system is an appropriate measure of complexity [4]. Although often focused on structural units, such as cell types, genes, or species, and not on the functions of those units, McShea [5] has also argued that counting structural parts should often be a good estimate of functional complexity.
Herein, we follow previous authors [4,5] and define more complex systems as those that have more parts. “Part” is used as a term that spans levels of biological organization [4]. Species are parts of ecological communities. Organs (like brains or ganglia) are parts of species. Cell types (like neuronal types) are parts of organs and of species. Proteins are parts of cells and of networks, and domains and amino acids are parts of proteins. Many other biological units are also parts [6]. For the current discussion, we are concerned less with defining or counting parts, and concerned more with how new parts originate during evolution. According to the definition of complexity that we employ, new parts (that do not come at the expense of existing parts) increase biological complexity. Therefore, those mechanisms that cause the evolution of new parts are of particular interest because those are the mechanisms that cause increases in biology complexity.
"those mechanisms that cause the evolution of new parts are of particular interest because those are the mechanisms that cause increases in biology complexity."
General patterns of increased complexity
By definition, complex systems have many parts, and the histories of those parts are varied. As such, we cannot expect a simple, one dimensional answer to the question of how complexity evolved. Nevertheless, at all levels of biological organization, conceptually similar patterns (Figure 1) have resulted from a varied array of mechanisms that historically increased complexity.
Figure 1 - Generalized patterns of increased biological complexity. The top of the figure represents an ancestral state, the bottom a descendant state. Shapes represent “parts”, a generic term for a biological feature at any level of organization. Parts can be species, genes, protein domains, pathways, brain regions, and many other biological units [see 6]. A. Copying and divergence B. Fission and divergence C. Copying and fusion. Without copying or fission, complexity does not increase because divergence (or fusion) maintains the same number of parts. Without differential divergence or fusion of parts, complexity only increases marginally.
First, parts may exhibit a pattern consistent with differential divergence of copied elements (Fig. 1a). A prime example of a specific mechanism leading to this pattern is gene duplication plus divergence. Duplicated genes are initially identical, and they gradually diverge over time, increasing genomic complexity. Second, parts may exhibit a pattern consistent with differential divergence of split elements (Fig. 1b). Here, a prime example is speciation, where populations of individual organisms, originally all of the same species, split into multiple populations that diverge to the point of becoming separate species. Splitting (fission) can also occur in an asymmetric fashion (Fig 1c), generating two uncoupled parts that together would sum to one ancestral part. Third, parts may exhibit a pattern consistent with fusion of copied parts (Fig. 1d). For example, copied protein domains often join together to generate a new gene. Another example of fusion is expression of genes in new combinations, a process termed co-option [reviewed in 7]. A primary goal of this paper is to review cases in nervous system evolution that provide specific and more detailed mechanisms that account for these patterns at different levels of biological organization.
The patterns in figure 1 are produce by a number of different mechanisms, including gene duplication, alternative splicing, retrotransposition, co-option, etc.
[The specific mechanisms leading to the general patterns is the topic of the rest of the paper from which this excerpt is taken].
References Cited
1. Striedter GF: Principles of Brain Evolution. Sunderland, MA: Sinauer; 2005.
2. Adami C, Ofria C, Collier TC: Evolution of biological complexity. Proceedings of the National Academy of Sciences of the United States of America 2000, 97:4463-4468.
3. Adamowicz SJ, Purvis A, Wills MA: Increasing morphological complexity in multiple parallel lineages of the Crustacea. Proceedings of the National Academy of Sciences of the United States of America 2008, 105:4786-4791.
4. Bonner JT: The Evolution of Complexity. Princeton: Princeton University Press; 1988.
5. McShea DW: Functional complexity in organisms: Parts as proxies. Biology & Philosophy 2000, 15:641-668.
6. McShea DW, Venit EP: What is a part? In The Character Concept in Evolutionary Biology. Edited by Wagner GP: Academic Press; 2001:259-284.
7. True JR, Carroll SB: Gene co-option in physiological and morphological evolution. Annual Review of Cell and Developmental Biology 2002, 18:53-80.
Thursday, November 20, 2008
Evolutionary novelty: Photosynthetic slug
All of biology, from genes to species, is united by common descent. Therefore new biological entities – novelties – must come from the modification of existing entities. Lightening does not strike and impart new features into organisms; new features evolve from existing ones. New research in PNAS provides fascinating new insights into the evolutionary origin of a 'photosynthetic slug'.
Given new features evolve from existing ones, one way novelties originate is through duplication and divergence. Another way is through new combinations of existing biological entities. In fact, biological entities can be recombined at many levels. Protein domains fuse to form new genes, genes become expressed together in new combinations in developmental time or space, even species can merge together to form new species, as occurred at the origin of eukaryotic cells when one species merged with a bacteria that became our cells’ energy factories, the mitochondria.
Imagine if evolution happened to produce a photosynthetic animal, and ask, what are some of the ways it might happen? One likely way is to utilize existing organisms (or their genes) that already have the ability to convert light energy into chemical energy. This is exactly what has happened during the evolution of the gastropod mollusk Elysia chlorotica, a green “sea slug”. Like other types of animal including reef-building corals, E. chlorotica harbors the photosynthetic machinery of other organisms. In the case of reef corals, a symbiotic relationship with dinoflagellates provides photosynthetic ability. But in the green sea slug, only the photosynthetic machinery itself is sequestered, by ingesting an algae, and using the algae’s plastids, the photosynthetic sub-cellular structure of the algae (interestingly, the plastid joined the algal cell in an ancient novel merger of species).
This presents a puzzle. The algae’s plastid, which is being used by the green sea slug for photosynthesis, does not itself contain all of the machinery required for photosynthesis. Instead, many of the photosynthesis genes reside in the algae, only some reside in the plastid. Yet the green sea slug can photosynthesize for months using only the plastid, even in the absence of algae, and therefore in the absence of the algae’s photosynthesis machinery. How is this possible?
The authors found that at least one gene (psbO) is integrated into the genome of the green sea slug. This gene is identical in sequence to an algal gene, yet the sequence adjacent to the gene in the slug analyses make clear that the gene is in the slug’s genome and not an experimental artifact, like contamination.
As the authors indicate, this work raises many interesting questions. How does the slug’s gene target the plastids? What about all the other genes absent from the plastid that are required for photosynthesis – are those transferred to the slug, too? Or could some of the slug’s genes replace the function of the missing genes? What is the specific mechanism for horizontal transfer of genes from one species to another? Clearly, the authors are thinking about these interesting questions, and likely future research will provide us with answers.
I noticed that Carl Zimmer already posted on this article in his fine blog, The Loom
Self-promotion*
As I wrote in an article with Michael Rose called “The New Biology: Beyond the Modern Synthesis”, acceptance of biological mergers was slow, perhaps because the modern synthesis viewed genomes as sleekly functional, finely tuned to current utility. As such, moving genes from one genome to another seems like it should be suboptimal, and therefore rare. No one doubts the importance of biological mergers any more, but they are still fascinating and under-documented, in part because they were neglected for so long.
*One reason I write blog entries is to present my research interests and ideas to a potentially broader audience. As such, I like to like papers of mine when possible.
M. E. Rumpho, J. M. Worful, J. Lee, K. Kannan, M. S. Tyler, D. Bhattacharya, A. Moustafa, J. R. Manhart (2008). From the Cover: Horizontal gene transfer of the algal nuclear gene psbO to the photosynthetic sea slug Elysia chlorotica Proceedings of the National Academy of Sciences, 105 (46), 17867-17871 DOI: 10.1073/pnas.0804968105
Tuesday, November 18, 2008
There once was a man named Chuck
I decided to write a quick Darwin Limerick, inspired by the contest over at Dispersal of Darwin, and by the concepts of pluralistic Darwinism and common descent:
I'm still working on "There once was a man named Chuck"
There once was a man from Down House
Who convinced me I'm cousin to a brown mouse
I'm glad as can be
That all life is a tree
Toe fungus to red grouse to crown louse
I'm still working on "There once was a man named Chuck"
Tuesday, November 11, 2008
Evolutionary Novelty: Hair
Mammals have hair but no other animals do. As such, hair is a clear evolutionary novelty, present in one group but absent in all others. In my macroevolution course (EEMB 102), I use hair as a clear character that can be used in phylogenetics. Hair groups all mammals to the exclusion of other organisms. In systematics jargon, hair is therefore a “synapomorphy”, grouping mammals together.
We can map the trait of hair on a family tree of animals. From this perspective, we can infer that the ancestor of all mammals very likely had hair, but that the ancestor of sauropods (birds, reptiles, and mammals) lacked hair. Therefore, hair originated prior to the common ancestor of all mammals.
Figure 1 – Hair originated before mammals, but after the common ancestor of birds, reptiles and mammals. Grey ellipse (hard to see except as a broken branch, I'd fix it but I'm too lazy) is the origin of hair keratin protein.
So where did hair come from - how did this evolutionary novelty evolve? A new paper by Eckhart et al in PNAS [link] provides evidence that the building blocks of hair pre-date the origin of hair itself. Namely, they found alpha-keratin (“hair keratin”) proteins are encoded in the genomes of chickens and the green anole lizard. In the green anole they studied, ‘hair keratin’ proteins were used in claws.
Just ten years ago, results like this clarifying the molecular components of trait evolution were rare, but they have become common now that genome sequences are available for many species. Before we had some idea of gene function, and before genome sequencing, scientists could only examine one level of biological organization – the trait (hair in this case). And that could only get science so far. In the case of hair, it mainly got science as far as Figure 1, which leads to the inference that hair evolved a bit before the common ancestor of living mammals. But “hair” is not one thing. It is a complex of building blocks, including structural genes (like keratin) and developmental processes. Today, scientists can decompose a trait, like hair, into its components and study the evolutionary history of each part separately, tracing the parts through various genomes.
What do we expect for the evolution of hair’s components? Figure 1 suggests that “hair” and all its components arise at the same time, near the origin of mammals. The origin of “hair” on figure 1 can be considered a first-pass hypothesis for the origins of ALL the components of hair. If hair itself originated near the origin of mammals, a logical idea is that the components originated then too.
Today, we can test this first-pass hypothesis because we know some of the molecular components of hair. A particularly important part is “hair keratin”. Mutate this protein and the hair built from that mutant protein is fragile and brittle. The expectation based on figure 1 is that hair keratin proteins originated with hair itself. But the discovery of these genes in an anole indicates an earlier origin for this component. In other words, components of hair originated before hair itself. In this case the protein “hardened” by mutations to cysteine amino acids that may have functioned to molecularly harden the proteins. Since these changes were later useful in the structure of hair, they may be considered exaptations, features that originated for functions other than current utility: keratins may have hardened before that feature became useful for hair formation. [Note for scientific accuracy – the biochemistry of the anole protein has not been studied, so while it is cysteine rich, we don’t know yet if the anole protein is ‘hardened’].
This work also illustrates that in evolution, new things do not appear from nowhere [see my post Coming to Grips]. In evolution, new things come from the duplication/differential modification and recombination of existing parts. Morphologists know this, as one dominant idea about the origin of hair is that hair evolved by modification of scales. Hair keratin is not expressed in anole scales, so the scale hypothesis is not supported by the new PNAS paper. Also unfortunate for the scale hypothesis is the fact that the fossil record retains no transitional forms between scale and hair. Even though morphological relatives of hair are ambiguous, the molecular relatives in this case are clear. Hardened keratin comes as two types, which share an evolutionary relationship, and hardened keratins may share an evolutionary relationship with soft keratins, proteins that are present in numerous tetrapods, and therefore have a more ancient origin than the hard variety. In sum, keratin has an ancient heritage, and through gene duplications and differential modification, two related groups of these proteins have specialized as hair keratins. Fascinatingly, some of the hair keratin modifications pre-dated hair itself.
If you are interested phylogenetic analyses of trait evolution, and the evolutionary history of trait components, this is a common theme of research in my lab.
We’ve found:
Synaptic components are present in sponges and therefore may predate synapses. [paper] [blog]
Phototransduction components were first assembled for vision in the eumetazoan ancestor (cnidaria + bilateria), yet some components pre-date animals [blog] [blog] [paper] [paper]
See also: Red Herring Blog
We can map the trait of hair on a family tree of animals. From this perspective, we can infer that the ancestor of all mammals very likely had hair, but that the ancestor of sauropods (birds, reptiles, and mammals) lacked hair. Therefore, hair originated prior to the common ancestor of all mammals.
Figure 1 – Hair originated before mammals, but after the common ancestor of birds, reptiles and mammals. Grey ellipse (hard to see except as a broken branch, I'd fix it but I'm too lazy) is the origin of hair keratin protein.
So where did hair come from - how did this evolutionary novelty evolve? A new paper by Eckhart et al in PNAS [link] provides evidence that the building blocks of hair pre-date the origin of hair itself. Namely, they found alpha-keratin (“hair keratin”) proteins are encoded in the genomes of chickens and the green anole lizard. In the green anole they studied, ‘hair keratin’ proteins were used in claws.
Just ten years ago, results like this clarifying the molecular components of trait evolution were rare, but they have become common now that genome sequences are available for many species. Before we had some idea of gene function, and before genome sequencing, scientists could only examine one level of biological organization – the trait (hair in this case). And that could only get science so far. In the case of hair, it mainly got science as far as Figure 1, which leads to the inference that hair evolved a bit before the common ancestor of living mammals. But “hair” is not one thing. It is a complex of building blocks, including structural genes (like keratin) and developmental processes. Today, scientists can decompose a trait, like hair, into its components and study the evolutionary history of each part separately, tracing the parts through various genomes.
What do we expect for the evolution of hair’s components? Figure 1 suggests that “hair” and all its components arise at the same time, near the origin of mammals. The origin of “hair” on figure 1 can be considered a first-pass hypothesis for the origins of ALL the components of hair. If hair itself originated near the origin of mammals, a logical idea is that the components originated then too.
Today, we can test this first-pass hypothesis because we know some of the molecular components of hair. A particularly important part is “hair keratin”. Mutate this protein and the hair built from that mutant protein is fragile and brittle. The expectation based on figure 1 is that hair keratin proteins originated with hair itself. But the discovery of these genes in an anole indicates an earlier origin for this component. In other words, components of hair originated before hair itself. In this case the protein “hardened” by mutations to cysteine amino acids that may have functioned to molecularly harden the proteins. Since these changes were later useful in the structure of hair, they may be considered exaptations, features that originated for functions other than current utility: keratins may have hardened before that feature became useful for hair formation. [Note for scientific accuracy – the biochemistry of the anole protein has not been studied, so while it is cysteine rich, we don’t know yet if the anole protein is ‘hardened’].
This work also illustrates that in evolution, new things do not appear from nowhere [see my post Coming to Grips]. In evolution, new things come from the duplication/differential modification and recombination of existing parts. Morphologists know this, as one dominant idea about the origin of hair is that hair evolved by modification of scales. Hair keratin is not expressed in anole scales, so the scale hypothesis is not supported by the new PNAS paper. Also unfortunate for the scale hypothesis is the fact that the fossil record retains no transitional forms between scale and hair. Even though morphological relatives of hair are ambiguous, the molecular relatives in this case are clear. Hardened keratin comes as two types, which share an evolutionary relationship, and hardened keratins may share an evolutionary relationship with soft keratins, proteins that are present in numerous tetrapods, and therefore have a more ancient origin than the hard variety. In sum, keratin has an ancient heritage, and through gene duplications and differential modification, two related groups of these proteins have specialized as hair keratins. Fascinatingly, some of the hair keratin modifications pre-dated hair itself.
If you are interested phylogenetic analyses of trait evolution, and the evolutionary history of trait components, this is a common theme of research in my lab.
We’ve found:
Synaptic components are present in sponges and therefore may predate synapses. [paper] [blog]
Phototransduction components were first assembled for vision in the eumetazoan ancestor (cnidaria + bilateria), yet some components pre-date animals [blog] [blog] [paper] [paper]
See also: Red Herring Blog
Monday, November 10, 2008
Probing Darwin's Black Box
The 'God of the Gaps' strategy is to assert that anything we do not yet understand is attributable to a god or gods. Two thousand years ago there were a lot of gaps in our understanding, and plenty of room for inventing ad hoc explanations for things. There were a lot of gaps where gods might reside.
Even recently, the god of the gaps argument is sometimes used. One example is the idea of 'Darwin's black box', the false assertion that the exquisite details of molecular biology cannot be understood in an evolutionary context.
There are two facets of 'god of the gaps' that are particularly bankrupt, one scientific and one theological. Scientifically, god of the gaps is equivalent to suicide, an admission that one simply cannot imagine how to go on any farther. God of the gaps is giving up on science, with no reason to do so. Theologically, god of the gaps means that the realm of god gets smaller each time a gap in our knowledge is filled.
Here, I give two recent examples from my life where the molecular details of evolution have been explicated in greater detail. In neither case are the gaps fully filled - this can never be the case - split a gap in half and we have two smaller gaps. But the gaps are getting sooo small - is it really worth trying to stuff gods in those tiny little gaps?
First, I saw a seminar by Joe Thornton on his work on the evolution of steriod receptors. Joe uses statistical inference to reconstruct the sequence of ancestral proteins. Then he brings them to life in the lab and conducts experiments on the proteins. He is able to reconstruct the order of specific mutations that occurred and that change the function of the proteins he studies.
I found particularly interesting that one particular receptor could identify 3 different steroids at the origin of the protein. Later on, specializations occurred through particular mutations that Joe and his group could identify. When thinking about the evolution of novelty, we often assume that multiple functions are added over evolutionary time. However, Joe's results show how functional complexity can be the original state, and that structural complexity can follow by parsing an ancestral function across subsequently duplicated genes.
To view Joe's presentation, go here.
The second recent example is that a paper from my lab was recently published that reviews our progress on understanding the evolution of the molecular basis of vision (phototransduction). This paper is available for free from the Springer web site.
Even recently, the god of the gaps argument is sometimes used. One example is the idea of 'Darwin's black box', the false assertion that the exquisite details of molecular biology cannot be understood in an evolutionary context.
There are two facets of 'god of the gaps' that are particularly bankrupt, one scientific and one theological. Scientifically, god of the gaps is equivalent to suicide, an admission that one simply cannot imagine how to go on any farther. God of the gaps is giving up on science, with no reason to do so. Theologically, god of the gaps means that the realm of god gets smaller each time a gap in our knowledge is filled.
Here, I give two recent examples from my life where the molecular details of evolution have been explicated in greater detail. In neither case are the gaps fully filled - this can never be the case - split a gap in half and we have two smaller gaps. But the gaps are getting sooo small - is it really worth trying to stuff gods in those tiny little gaps?
First, I saw a seminar by Joe Thornton on his work on the evolution of steriod receptors. Joe uses statistical inference to reconstruct the sequence of ancestral proteins. Then he brings them to life in the lab and conducts experiments on the proteins. He is able to reconstruct the order of specific mutations that occurred and that change the function of the proteins he studies.
I found particularly interesting that one particular receptor could identify 3 different steroids at the origin of the protein. Later on, specializations occurred through particular mutations that Joe and his group could identify. When thinking about the evolution of novelty, we often assume that multiple functions are added over evolutionary time. However, Joe's results show how functional complexity can be the original state, and that structural complexity can follow by parsing an ancestral function across subsequently duplicated genes.
To view Joe's presentation, go here.
The second recent example is that a paper from my lab was recently published that reviews our progress on understanding the evolution of the molecular basis of vision (phototransduction). This paper is available for free from the Springer web site.
Todd Oakley and M. Sabrina Pankey (2008) Opening the "Black Box": The genetic and biochemical basis of eye evolution. Evolution Education and Outreach. [Link]
Monday, November 3, 2008
Linear Evolution: McCain and Obama
A common theme of this blog is to share cases of "linear evolutionary thinking". These are instances, usually graphics, that illustrate the common conception of evolution as a line of progress, from worst to best. Evolution actually occurs by branching processes. We could pull out a line of evolution from the tree, but that line by necessity has an arbitrary endpoint, often humans or a human-like feature. And living species or their traits cannot necessarily be equated with ancestral forms.
Ostracod eye evolution has been depicted as a straight line from simple to compound eye, illustrated here. Textbooks use such diagrams (see also this), and these diagrams can impact the way people think about evolution.
Here is another variation on the human march of progress, which shows Barack Obama as the more advanced political candidate, compared to John McCain.
Thanks to my brother for sending this to me.
Ostracod eye evolution has been depicted as a straight line from simple to compound eye, illustrated here. Textbooks use such diagrams (see also this), and these diagrams can impact the way people think about evolution.
Here is another variation on the human march of progress, which shows Barack Obama as the more advanced political candidate, compared to John McCain.
Thanks to my brother for sending this to me.
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