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Reality is stranger than fiction: Exploring the Evolutionary Role of RNA Editing with Computational Models| old_uid | 9266 |
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| title | Reality is stranger than fiction: Exploring the Evolutionary Role of RNA Editing with Computational Models |
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| start_date | 2010/11/18 |
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| schedule | 17h |
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| online | no |
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| summary | During the last two decades, biology witnessed tremendous advances in our understanding of life. Most of these have resulted from the emphasis on genomics. During the same time, the field of Artificial Life gave us a scientific methodology to consider and study the general principles of life at large. The main assumption of the field was that instead of focusing on the carbon-based, living organization, life could be better explained by synthesizing its « logical forms » from simple—where, « fictional » machines substituted real biochemistry. The expectation was that this « out-of-the-box », synthetic methodology would gain us a wider scientific understanding of life. We would be able to entertain alternative scenarios for life, challenge the dogmas of biology, and ultimately discover the design principles of life. Yet, it is from biology, not artificial life, that the strangest and most exciting discoveries and design principles of life arise today.
One concept that has been lacking and needs updating in artificial life is its view of the genotype/phenotype relationship. Indeed, very few artificial life models deal with the complexity of genomic-proteomic interaction. Genotype and phenotype are intertwined in a complex manner, but each operates under different principles that are often overlooked in artificial life. Thus, artificial life rarely approaches issues of genomic structure and regulation, or the co-existence of DNA and RNA as different types of informational carriers. This could well be because artificial life models tend to regard genes solely as mechanisms of generational (vertical) inheritance, rather than as (informational) mechanisms of ontogenetic (horizontal) development, regulation, maintenance, phenotypic plasticity, and response to environmental change. This way, most artificial life models do not test, or even deal with, possible genomic structure architectures and their impact on development and evolution. This is a big shortcoming in the field since, as we have seen in the last two decades, the molecular biology gene and the genomic structure it implies are behind many essential principles of life.
In this talk, we will describe computational model for studying the evolutionary implications of RNA Editing: the genomic post-transcription alteration of genetic information. Our latest agent-based model of genotype editing is defined by two distinct genetic components: a coding portion encoding phenotypic solutions, and a non- coding portion used to edit the coding material. This set up leads to an indirect, stochastic genotype/phenotype mapping which captures essential aspects of RNA editing found in Nature. Previously, we have established the quantitative performance advantages of genotype editing against the canonical evolutionary algorithm in static and dynamic environments of various types. In this talk, we present a study of the qualitatively different evolutionary solutions attainable via genotype editing in drastically changing environments.
In particular, we show how genotype editing can be advantageous in dynamical environments, namely with the emergence of regulatory signals and memory of previous environment—a capacity not attainable by evolutionary algorithms that use only coding genetic material. Our research goal is twofold: (1) to study the role of RNA Editing regulation in the evolutionary process, and (2) to investigate the conditions under which genotype edition improves the optimization performance of bio-inspired evolutionary algorithms. We have shown that genotype edition allows evolving agents to perform better in several classes of fitness functions, both in static and dynamic environments. We are also investigating the ways in which the indirect genotype/phenotype mapping resulting from genotype editing leads to a better exploration/exploitation compromise in the search process. |
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| responsibles | Legrand |
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