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| ScienceDaily (Oct. 25, 2008) — Researchers have put forward a simple model of development and gene regulation that is capable of explaining patterns observed in the distribution of morphologies and body plans (or, more generally, phenotypes). |
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| The study, by Elhanan Borenstein of the Santa Fe Institute and Stanford University and David Krakauer of the Santa Fe Institute was published in this month's issue of PLoS Computational Biology. Nature truly displays a bewildering variety of shapes and forms. [b]Yet, with all its magnificence, this diversity still represents only a tiny fraction of the endless 'space' of possibilities, and observed phenotypes actually occupy only small, dense patches in the abstract phenotypic space.[/B] Borenstein and Krakauer demonstrate that the sparseness of variety in nature can be attributed to the interactions between multiple genes and genetic controls involved in the development of organisms – a much simpler explanation than previously suggested. Borenstein and Krakauer further integrated their model with phylogenetic dynamics, allowing developmental plans to evolve over time. They showed that this hybrid developmental-phylogenetic model reproduces patterns that are observed in the fossil record, including increasing variation between taxonomic groups, accompanied by decreasing variation within groups. This pattern is consistent with the Cambrian radiation associated with a rapid proliferation of highly disparate, multicellular animals, and suggests that much of the variation seen today is as a result of simpler genetic controls dating from much earlier in evolutionary time. |
The article continues...
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| The findings presented in this study also bear directly on issues of convergence (when very different organisms independently evolve similar features). By including a model of development, rather different genotypes can produce very similar phenotypes. Consequently, convergent evolution, which the vast space of genotypes would suggest to be rare, is allowed to become much more common. One of the paradoxical implications of this study has been to show how innovations in development that lead to an overall increase in the number of accessible phenotypes, can lead to a reduction in selective variance. In other words, while the potential for novel phenotypes increases, the fraction of space these phenotypes occupies tends to contract. They concluded that "The theory presented in our paper complements the view of development as a key component in the production of endless forms and highlights the crucial role of development in constraining (as well as generating) biotic diversity." |
The free, online peer-reviewed article:
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| These findings complement the view of development as a key component in the production of endless forms and highlight the crucial role of development in constraining biotic diversity and evolutionary trajectories. |
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| The role of development in generating, or constraining, biotic diversity has been one of the most active debates in evolutionary biology [32]–[34]. The roots of this debate go back to the study of homologies and questions over physico-chemical verses genetically-selected rules of growth. One merit of simple developmental models is to illustrate how these two positions reflect necessary, complementary properties of generic developmental programs. Regulatory epistasis introduces non-linearities into development, allowing similar genotypes to generate significant divergence among phenotypes, whereas degeneracy tends to contract the occupancy of morphospace and bias phenotypic samples. Of great interest is how these structural properties of development have themselves been modified over the course of evolutionary time, potentially changing the tempo and mode of the evolutionary process. One of the paradoxical implications of this study has been to show how innovations in development (arising through increasing regulatory dimensions) that lead to an increase in the volume of accessible phenotypes, can lead to a reduction in selective variance (through increasing regulatory epistasis), so whereas the potential for novel phenotypes increases, the fraction of space these phenotypes occupies tends to contract. Hence the evolutionary process moves from a macro-configuration, sampling distant regions of space sparsely, to a micro configuration, sampling local regions of space at high resolution. This is analogous to an annealing process, whereby as an optimization process proceeds, the solutions become more frequent and more densely localized around the putative solution points. |
Take the following into consideration:
- Evolution is constrained (biased) as a result of genetic information present in simpler genetic controls dating from the base of the evolutionary tree (preadaptations)..
- We observe many preadaptations for multicellularity in primitive unicellular organisms.
- Also several toolkits (also preadaptations) for the development of body plans (Hox genes), the nervous system and sensory organs in animals at the base of the eumetazoan tree.
- Also, spectacular examples of convergence are observed in nature.
How do stem cells become specialized?
Many Paths, Few Destinations: How Stem Cells Decide What They'll Become
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How does a stem cell decide what specialized identity to adopt -- or simply to remain a stem cell? A new study suggests that the conventional view, which assumes that cells are "instructed" to progress along prescribed signaling pathways, is too simplistic. Instead, it supports the idea that cells differentiate through the collective behavior of multiple genes in a network that ultimately leads to just a few endpoints -- just as a marble on a hilltop can travel a nearly infinite number of downward paths, only to arrive in the same valley.Evolution seems to be biased towards a few endpoints. This is partly due to the massive amounts of preadaptations in organisms at the base of the evolutionary tree. Now evolution seems to learn....
Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments
Abstract:
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| One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions. |
And now proteins that control evolution...
Evolution's new wrinkle: Proteins with cruise control provide new perspective
Related articles: Number 1
Mutagenic Evidence for the Optimal Control of Evolutionary Dynamics
| Elucidating the fitness measures optimized during the evolution of complex biological systems is a major challenge in evolutionary theory. We present experimental evidence and an analytical framework demonstrating how [biochemical networks exploit optimal control strategies in their evolutionary dynamics. Optimal control theory explains a striking pattern of extremization in the redox potentials of electron transport proteins, assuming only that their fitness measure is a control objective functional with bounded controls. |
| Evolution is guided by the optimization of fitness measures that balance functionally beneficial properties. |
Number 2:
Optimal control of evolutionary dynamics
From the conclusion:
| The observation that coevolving biopolymer sequences may optimally control each other’s evolution raises the prospect of artificial optimal control of evolutionary dynamics. Possible applications include the control of replication fidelity in nucleic acid amplification reactions and the design of therapeutics that dynamically regulate the evolution of viral populations. |
| The authors sought to identify the underlying cause for this self-correcting behavior in the observed protein chains. Standard evolutionary theory offered no clues. Applying the concepts of control theory, a body of knowledge that deals with the behavior of dynamical systems..., . |
| the researchers concluded that this self-correcting behavior could only be possible if, during the early stages of evolution, the proteins had developed a self-regulating mechanism, analogous to a car's cruise control or a home's thermostat, allowing them to fine-tune and control their subsequent evolution. |
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