| 1. |
- Patra, B., et al.
(author)
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A genome wide dosage suppressor network reveals genomic robustness
- 2017
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In: Nucleic Acids Research. - : Oxford University Press. - 0305-1048 .- 1362-4962. ; 45:1, s. 255-270
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Journal article (peer-reviewed)abstract
- Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high cooccurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures. © 2016 The Author(s).
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| 2. |
- Goldsby, H. J., et al.
(author)
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Serendipitous scaffolding to improve a genetic algorithm's speed and quality
- 2018
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In: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450356183 ; , s. 959-966
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Conference paper (peer-reviewed)abstract
- A central challenge to evolutionary computation is enabling techniques to evolve increasingly complex target end products. Frequently, direct approaches that reward only the target end product itself are not successful because the path between the starting conditions and the target end product traverses through a complex fitness landscape, where the directly accessible intermediary states may be require deleterious or even simply neutral mutations. As such, a host of techniques have sprung up to support evolutionary computation techniques taking these paths. One technique is scaffolding where intermediary targets are used to provide a path from the starting state to the end state. While scaffolding can be successful within well-understood domains it also poses the challenge of identifying useful intermediaries. Within this paper we first identify some shortcomings of scaffolding approaches ' namely, that poorly selected intermediaries may in fact hurt the evolutionary computation's chance of producing the desired target end product. We then describe a light-weight approach to selecting intermediate scaffolding states that improve the efficacy of the evolutionary computation. © 2018 Association for Computing Machinery.
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| 3. |
- Ålund, Murielle, et al.
(author)
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Academic ecosystems must evolve to support a sustainable postdoc workforce
- 2020
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In: Nature Ecology and Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 4:6, s. 777-781
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Research review (peer-reviewed)abstract
- The postdoctoral workforce comprises a growing proportion of the science, technology, engineering and mathematics (STEM) community, and plays a vital role in advancing science. Postdoc professional development, however, remains rooted in outdated realities. We propose enhancements to postdoc-centred policies and practices to better align this career stage with contemporary job markets and work life. By facilitating productivity, wellness and career advancement, the proposed changes will benefit all stakeholders in postdoc success—including research teams, institutions, professional societies and the scientific community as a whole. To catalyse reform, we outline recommendations for (1) skills-based training tailored to the current career landscape, and (2) supportive policies and tools outlined in postdoc handbooks. We also invite the ecology and evolution community to lead further progressive reform.
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| 4. |
- Adami, C., et al.
(author)
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Evolution and stability of altruist strategies in microbial games
- 2012
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In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics. - 1539-3755 .- 1550-2376. ; 85:1
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Journal article (peer-reviewed)abstract
- When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters. © 2012 American Physical Society.
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| 5. |
- Adami, C., et al.
(author)
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Evolutionary game theory using agent-based methods
- 2016
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In: Physics of Life Reviews. - : Elsevier B.V.. - 1571-0645 .- 1873-1457. ; 19, s. 1-26
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Journal article (peer-reviewed)abstract
- Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection–strong mutation limit), but that mathematics is crucial to validate the computational simulations. © 2016 Elsevier B.V.
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| 6. |
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| 7. |
- Schossau, J., et al.
(author)
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Information-theoretic neuro-correlates boost evolution of cognitive systems
- 2016
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In: Entropy. - : MDPI AG. - 1099-4300. ; 18:1
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Journal article (peer-reviewed)abstract
- Genetic Algorithms (GA) are a powerful set of tools for search and optimization that mimic the process of natural selection, and have been used successfully in a wide variety of problems, including evolving neural networks to solve cognitive tasks. Despite their success, GAs sometimes fail to locate the highest peaks of the fitness landscape, in particular if the landscape is rugged and contains multiple peaks. Reaching distant and higher peaks is difficult because valleys need to be crossed, in a process that (at least temporarily) runs against the fitness maximization objective. Here we propose and test a number of information-theoretic (as well as network-based) measures that can be used in conjunction with a fitness maximization objective (so-called "neuro-correlates") to evolve neural controllers for two widely different tasks: A behavioral task that requires information integration, and a cognitive task that requires memory and logic. We find that judiciously chosen neuro-correlates can significantly aid GAs to find the highest peaks. © 2015 by the authors.
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| 8. |
- Schossau, J., et al.
(author)
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Neuronal variation as a cognitive evolutionary adaptation
- 2020
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In: ALIFE 2018 - 2018 Conference on Artificial Life. - : MIT Press. ; , s. 57-58
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Conference paper (peer-reviewed)abstract
- Computational scientists studying cognition, robotics, and Artificial Intelligence have discovered that variation is beneficial for many applications of problem-solving. With the addition of variation to a simple algorithm, local attractors may be avoided (breaking out of poor behaviors), generalizations discovered (leading to robustness), and exploration of new state spaces made. But exactly how much variation and where it should be applied is still difficult to generalize between implementations and problems as there is no guiding theory or broad understanding for why variation should help cognitive systems and in what contexts. Historically, computational scientists could look to biology for insights, in this case to understand variation and its effect on cognition. However, neuroscientists also struggle with explaining the variation observed in neural circuitry (neuronal variation) so cannot offer strong insights whether it originates externally, internally, or is merely the result of an incomplete neural model. Here, we show preliminary data suggesting that a small amount of internal variation is preferentially selected through evolution for problem domains where a balance of cognitive strategies must be used. This finding suggests an evolutionary explanation for the existence of and reason for internal neuronal variation, and lays the groundwork for understanding when and why to apply variation in Artificial Intelligences. Copyright © ALIFE 2018.All rights reserved.
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| 9. |
- Sheneman, L., et al.
(author)
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The evolution of neuroplasticity and the effect on integrated information
- 2019
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In: Entropy. - : MDPI AG. - 1099-4300. ; 21:5
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Journal article (peer-reviewed)abstract
- Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (Φ) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value Φ can change over many generations, and complex tasks require systems with at least a minimum Φ. This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to Φ due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change Φ. One can find arguments to expect one of three possible outcomes: Φ might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying Φ over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure Φ, the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system's ability to increase Φ correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding Φ learn better than those that are not. © 2019 by the authors.
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