Hasil untuk "Evolution"

P. Lockhart, M. Steel, M. Hendy et al.

Hj Jersion

D. Mindell

G. F. Joyce

J. T.

M. Gray, G. Burger, B. Lang

K. Laland, J. Odling-Smee, M. Feldman

L. Keller

C. Hawkesworth, B. Dhuime, A. Pietranik et al.

A. Bhide

Susan Oyama, Paul E. Griffiths, Russell D. Gray

M. Wyatt

K. Esvelt, Jacob C. Carlson, David R. Liu

Laboratory evolution has generated many biomolecules with desired properties, but a single round of mutation, gene expression, screening or selection, and replication typically requires days or longer with frequent human intervention. Because evolutionary success is dependent on the total number of rounds performed, a means of performing laboratory evolution continuously and rapidly could dramatically enhance its effectiveness. Although researchers have accelerated individual steps in the evolutionary cycle, the only previous example of continuous directed evolution was the landmark study of Wright and Joyce, who continuously evolved RNA ligase ribozymes with an in vitro replication cycle that unfortunately cannot be easily adapted to other biomolecules. Here we describe a system that enables the continuous directed evolution of gene-encoded molecules that can be linked to protein production in Escherichia coli. During phage-assisted continuous evolution (PACE), evolving genes are transferred from host cell to host cell through a modified bacteriophage life cycle in a manner that is dependent on the activity of interest. Dozens of rounds of evolution can occur in a single day of PACE without human intervention. Using PACE, we evolved T7 RNA polymerase (RNAP) variants that recognize a distinct promoter, initiate transcripts with ATP instead of GTP, and initiate transcripts with CTP. In one example, PACE executed 200 rounds of protein evolution over the course of 8 days. Starting from undetectable activity levels in two of these cases, enzymes with each of the three target activities emerged in less than 1 week of PACE. In all three cases, PACE-evolved polymerase activities exceeded or were comparable to that of the wild-type T7 RNAP on its wild-type promoter, representing improvements of up to several hundred-fold. By greatly accelerating laboratory evolution, PACE may provide solutions to otherwise intractable directed evolution problems and address novel questions about molecular evolution.

A. Mesoudi

Charles Darwin changed the course of scientific thinking by showing how evolution accounts for the stunning diversity and biological complexity of life on earth. Recently, there has also been increased interest in the social sciences in how Darwinian theory can explain human culture. Covering a wide range of topics, including fads, public policy, the spread of religion, and herd behavior in markets, Alex Mesoudi shows that human culture is itself an evolutionary process that exhibits the key Darwinian mechanisms of variation, competition, and inheritance. This cross-disciplinary volume focuses on the ways cultural phenomena can be studied scientifically - from theoretical modeling to lab experiments, archaeological fieldwork to ethnographic studies - and shows how apparently disparate methods can complement one another to the mutual benefit of the various social science disciplines. Along the way, this book reveals how new insights arise from looking at culture from an evolutionary angle. "Cultural Evolution" provides a thought-provoking argument that Darwinian evolutionary theory can both unify different branches of inquiry and enhance understanding of human behavior.

Dominic A. Edward, T. Chapman

Kenneth D Harris

As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, in which the traits of AI systems are shaped by the success of earlier AIs in designing and propagating their descendants. There is a rich mathematical theory modeling how behavioral traits are shaped by biological evolution, but AI evolution will be radically different: biological DNA mutations are random and approximately reversible, but descendant design in AIs will be strongly directed. Here we develop a mathematical model of evolution in self-designing AI systems, replacing random mutations with a directed tree of possible AI programs. Current programs determine the design of their descendants, while humans retain partial control through a "fitness function" that allocates limited computational resources across lineages. We show that evolutionary dynamics reflects not just current fitness but factors related to the long-run growth potential of descendant lineages. Without further assumptions, fitness need not increase over time. However, assuming bounded fitness and a fixed probability that any AI reproduces a "locked" copy of itself, we show that fitness concentrates on the maximum reachable value. We consider the implications of this for AI alignment, specifically for cases where fitness and human utility are not perfectly correlated. We show in an additive model that if deception increases fitness beyond genuine utility, evolution will select for deception. This risk could be mitigated if reproduction is based on purely objective criteria, rather than human judgment.

Elliot M. Butterworth, Tim Rogers

Species sharing a habitat will co-evolve to make use of the available resources, as consumption is modulated by competition and negative feedback loops between consumers and resources. The dietary range of a given species determines the resources it has access to and thus the other species with which it competes. A narrow dietary range avoids competition at the cost of over-reliance on a small selection of resources; conversely a wide dietary range provides more alternatives but also more chance of competition with other species. Here, we investigate the evolution of dietary range within a mathematical model of niche formation. We find highly path dependent co-evolution dynamics characterised by long-lived quasi-stable states. Ultimately, stochastic effects drive the evolution of generalist diets, as we uncover in our analysis and simulations.

Carver Mead

Mohammad Salahshour, Iain D. Couzin

Decades of scientific inquiry have sought to understand how evolution fosters cooperation, a concept seemingly at odds with the belief that evolution should produce rational, self-interested individuals. Most previous work has focused on the evolution of cooperation among boundedly rational individuals whose decisions are governed by behavioral rules that do not need to be rational. Here, using an evolutionary model, we study how altruism can evolve in a community of rational agents and promote cooperation. We show that in both well-mixed and structured populations, a population of objectively rational agents is readily invaded by mutant individuals who make rational decisions but evolve a distorted (i.e., subjective) perception of their payoffs. This promotes behavioral diversity and gives rise to the evolution of rational, other-regarding agents who naturally solve all the known strategic problems of two-person, two-strategy games by perceiving their games as pure coordination games.

Luijim Jose

Background/purpose. The persistent risk of semantic anachronism challenges both literary interpretation and pedagogy, as modern readers frequently impose contemporary meanings onto historically charged vocabulary. This study introduces the Contextual Diachronic Semantic Framework (CDSF), a five-layered analytical model designed to trace the evolution of word meaning over time. The primary aim is to demonstrate how CDSF uncovers semantic complexity and prevents misreading in canonical literature, while offering practical applications in literature instruction and critical reading. Materials/methods. The study employs a qualitative, text-centered methodology, applying the CDSF to five lexical items in Shirley Jackson’s The Lottery: “lottery,” “village,” “tradition,” “black box,” and “stones.” The five analytic layers—Etymological Trajectory Analysis, Diachronic Semantic Mapping, Contextual Literary Function, Cultural-Hermeneutic Embedding, and Interpretive Reconstruction—draw from historical dictionaries, linguistic corpora, literary criticism, and classroom pedagogy. Educational implications were derived by aligning findings with strategies for teaching vocabulary and symbolic language. Results. Findings reveal that each term operates as a site of historical memory, cultural critique, and thematic irony. The CDSF allows for context-sensitive interpretation, helping both scholars and students decode deeper meanings. In pedagogical terms, the framework provides a replicable tool for guiding learners beyond surface-level readings toward historically grounded literary analysis. Conclusion. The CDSF is a rigorous, interdisciplinary model that enhances scholarly interpretation and supports literature instruction. It promotes critical reading, prevents semantic misinterpretation, and equips teachers with a research-informed strategy for fostering historical empathy and interpretive depth in the classroom.