Evolutionary Biology and Complex Systems
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The rewiring of transcription circuits in evolution

The binding of transcription regulators to cis-regulatory sequences is a key step through which all cells regulate expression of their genes. Due to gains and losses of cis-regulatory sequences and changes in the transcription regulators themselves, the binding connections between regulators and their target genes rapidly change over evolutionary time and constitute a major source of biological novelty. This review covers recent work, carried out in a wide range of species, that addresses the overall extent of these evolutionary changes, their consequences, and some of the molecular mechanisms that lie behind them.
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Sequestration and activation of plant toxins protect the western corn rootworm from enemies at multiple trophic levels

Sequestration and activation of plant toxins protect the western corn rootworm from enemies at multiple trophic levels | Evolutionary Biology and Complex Systems | Scoop.it
The western corn rootworm escapes biological control by entomopathogenic nematodes by partitioning and phenocopying the plant defense system for self-protection.

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Current Biology - Repeated Origin of Three-Dimensional Leaf Venation Releases Constraints on the Evolution of Succulence in Plants

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A common developmental programme can produce diverse leaf shapes

A common developmental programme can produce diverse leaf shapes | Evolutionary Biology and Complex Systems | Scoop.it

Eudicot leaves have astoundingly diverse shapes. The central problem addressed in this paper is the developmental origin of this diversity. To investigate this problem, we propose a computational model of leaf development that generalizes the largely conserved molecular programme for the reference plants Arabidopsis thaliana, Cardamine hirsuta and Solanum lycopersicum. The model characterizes leaf development as a product of three interwoven processes: the patterning of serrations, lobes and/or leaflets on the leaf margin; the patterning of the vascular system; and the growth of the leaf blade spanning the main veins. The veins play a significant morphogenetic role as a local determinant of growth directions. We show that small variations of this model can produce diverse leaf shapes, from simple to lobed to compound. It is thus plausible that diverse shapes of eudicot leaves result from small variations of a common developmental programme.

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Plant immune and growth receptors share common signalling components but localise to distinct plasma membrane nanodomains

Plant immune and growth receptors share common signalling components but localise to distinct plasma membrane nanodomains | Cell surface receptors govern a multitude of signalling pathways in multicellular organisms. In plants, prominent examples are the receptor kinases FLS2 and BRI1, which activate immunity and steroid-mediated growth, respectively. Intriguingly, despite inducing distinct signalling outputs, both receptors employ common downstream signalling components, which exist in plasma membrane (PM)-localised protein complexes. An important question is thus how these receptor complexes maintain signalling specificity. Live-cell imaging revealed that FLS2 and BRI1 form PM nanoclusters. Using single-particle tracking we could discriminate both cluster populations and we observed spatiotemporal separation between immune and growth signalling platforms. This finding was confirmed by visualising FLS2 and BRI1 within distinct PM nanodomains marked by specific remorin proteins and differential co-localisation with the cytoskeleton. Our results thus suggest that signalling specificity between these pathways may be explained by the spatial separation of FLS2 and BRI1 with their associated signalling components within dedicated PM nanodomains.
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From Singularity to Polysingularity: An Evolutionary Approach

From Singularity to Polysingularity: An Evolutionary Approach | Evolutionary Biology and Complex Systems | Scoop.it
Digital technology affects us on many different levels. The implicit and explicit choices that developers make find their expression in interfaces and algorithms that we use in our daily lives. Those choices have a certain pragmatic and ideological basis, which is best captured with the concept of Singularity.
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An Evolutionary Method for Financial Forecasting in Microscopic High-Speed Trading Environment

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence, where neuroscientists, cognitive scientists, engineers, psychologists, physicists, computer scientists, and artificial intelligence investigators among others can publish their work in one periodical that bridges the gap between neuroscience, artificial intelligence and engineering.
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Researchers use mathematical modeling to explain evolutionary phenomenon that leads to treatment resistance

Reserachers are using mathematical models to explain how bacteria and cancer cells exploit an evolutionary process known as bet-hedging to resist medical intervention.
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Cooperators trade off ecological resilience and evolutionary stability in public goods games

Cooperators trade off ecological resilience and evolutionary stability in public goods games | Evolutionary Biology and Complex Systems | Scoop.it
Microbial populations often rely on the cooperative production of extracellular ‘public goods’ molecules. The cooperative nature of public good production may lead to minimum viable population sizes, below which populations collapse. In addition, ‘cooperator’ public goods producing individuals face evolutionary competition from non-producing mutants, or ‘freeloaders’. Thus, public goods cooperators should be resilient not only to the invasion of freeloaders, but also to ecological perturbations that may push their populations below a sustainable threshold. Through a mathematical analysis of the Ecological Public Goods Game, we show that game parameters that improve the cooperating population's stability to freeloader invasion also lead to a low ecological resilience. Complex regulatory strategies mimicking those used by microbes in nature may allow cooperators to beat this trade-off and become evolutionarily stable to invading freeloaders while at the same time maximizing their ecological resilience. Our results thus identify the coupling between resilience to evolutionary and ecological challenges as a key factor for the long-term viability of public goods cooperators.
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The effect of fecundity derivatives on the condition of evolutionary branching in spatial models

Publication date: 7 March 2017
Source:Journal of Theoretical Biology, Volume 416
Author(s): Kalle Parvinen, Hisashi Ohtsuki, Joe Yuichiro Wakano
By investigating metapopulation fitness, we present analytical expressions for the selection gradient and conditions for convergence stability and evolutionary stability in Wright's island model in terms of fecundity function. Coefficients of each derivative of fecundity function appearing in these conditions have fixed signs. This illustrates which kind of interaction promotes or inhibits evolutionary branching in spatial models. We observe that Taylor's cancellation result holds for any fecundity function: Not only singular strategies but also their convergence stability is identical to that in the corresponding well-mixed model. We show that evolutionary branching never occurs when the dispersal rate is close to zero. Furthermore, for a wide class of fecundity functions (including those determined by any pairwise game), evolutionary branching is impossible for any dispersal rate if branching does not occur in the corresponding well-mixed model. Spatial structure thus often inhibits evolutionary branching, although we can construct a fecundity function for which evolutionary branching only occurs for intermediate dispersal rates.
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Bone remodeling as a spatial evolutionary game

Publication date: 7 April 2017
Source:Journal of Theoretical Biology, Volume 418
Author(s): Marc D. Ryser, Kevin A. Murgas
Bone remodeling is a complex process involving cell–cell interactions, biochemical signaling and mechanical stimuli. Early models of the biological aspects of remodeling were non-spatial and focused on the local dynamics at a fixed location in the bone. Several spatial extensions of these models have been proposed, but they generally suffer from two limitations: first, they are not amenable to analysis and are computationally expensive, and second, they neglect the role played by bone-embedded osteocytes. To address these issues, we developed a novel model of spatial remodeling based on the principles of evolutionary game theory. The analytically tractable framework describes the spatial interactions between zones of bone resorption, bone formation and quiescent bone, and explicitly accounts for regulation of remodeling by bone-embedded, mechanotransducing osteocytes. Using tools from the theory of interacting particle systems we systematically classified the different dynamic regimes of the spatial model and identified regions of parameter space that allow for global coexistence of resorption, formation and quiescence, as observed in physiological remodeling. In coexistence scenarios, three-dimensional simulations revealed the emergence of sponge-like bone clusters. Comparison between spatial and non-spatial dynamics revealed substantial differences and suggested a stabilizing role of space. Our findings emphasize the importance of accounting for spatial structure and bone-embedded osteocytes when modeling the process of bone remodeling. Thanks to the lattice-based framework, the proposed model can easily be coupled to a mechanical model of bone loading.
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Models of evolution and evolutionary game theory: A comment on “Evolutionary game theory using agent based models” by Christoph Adami, Jory Schossau, Arend Hintze

Publication date: December 2016
Source:Physics of Life Reviews, Volume 19
Author(s): Peter Schuster
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In an age of rampant narcissism and social cheating – the importance of teaching social evolutionary mechanisms. | Sci-Ed

In an age of rampant narcissism and social cheating – the importance of teaching social evolutionary mechanisms. | Sci-Ed | Evolutionary Biology and Complex Systems | Scoop.it
In the face of unrestrained greed and dishonestly, we consider the importance of understanding social evolution and defenses against social cheaters.
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A New Lineage of Eukaryotes Illuminates Early Mitochondrial Genome Reduction

A New Lineage of Eukaryotes Illuminates Early Mitochondrial Genome Reduction | Evolutionary Biology and Complex Systems | Scoop.it

Highlights 


•Ancoracysta twista is a new predatory protist with a novel type of extrusome 

•A. twista represents an uncharacterized, deep-branching lineage of eukaryotes 

•It encodes a gene-rich mitochondrial genome and two cytochrome c maturation systems 

•Early, parallel, and exponential reduction describes mitochondrial genome evolution 


Summary 


The origin of eukaryotic cells represents a key transition in cellular evolution and is closely tied to outstanding questions about mitochondrial endosymbiosis [1, 2]. For example, gene-rich mitochondrial genomes are thought to be indicative of an ancient divergence, but this relies on unexamined assumptions about endosymbiont-to-host gene transfer [3, 4, 5]. Here, we characterize Ancoracysta twista, a new predatory flagellate that is not closely related to any known lineage in 201-protein phylogenomic trees and has a unique morphology, including a novel type of extrusome (ancoracyst). The Ancoracysta mitochondrion has a gene-rich genome with a coding capacity exceeding that of all other eukaryotes except the distantly related jakobids and Diphylleia, and it uniquely possesses heterologous, nucleus-, and mitochondrion-encoded cytochrome c maturase systems. To comprehensively examine mitochondrial genome reduction, we also assembled mitochondrial genomes from picozoans and colponemids and re-annotated existing mitochondrial genomes using hidden Markov model gene profiles. This revealed over a dozen previously overlooked mitochondrial genes at the level of eukaryotic supergroups. Analysis of trends over evolutionary time demonstrates that gene transfer to the nucleus was non-linear, that it occurred in waves of exponential decrease, and that much of it took place comparatively early, massively independently, and with lineage-specific rates. This process has led to differential gene retention, suggesting that gene-rich mitochondrial genomes are not a product of their early divergence. Parallel transfer of mitochondrial genes and their functional replacement by new nuclear factors are important in models for the origin of eukaryotes, especially as major gaps in our knowledge of eukaryotic diversity at the deepest level remain unfilled.

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Stephen's Web ~ A Model of Personal Learning (Take Two) ~ Stephen Downes

Stephen's Web ~ A Model of Personal Learning (Take Two) ~ Stephen Downes | Evolutionary Biology and Complex Systems | Scoop.it
Online learning, new media, connectivism, MOOCs, personal learning environments, new literacy, and more from Stephen Downes

Via Dr. Susan Bainbridge
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Evolving as a holobiont

Evolving as a holobiont | Evolutionary Biology and Complex Systems | Scoop.it
Some of the most exciting recent advances in biology have been in our understanding of how the microbiome—the community of bacteria, fungi, and other single-celled microorganisms—influences host functions and behaviors. From the way we eat, to the way we think, to our susceptibility to diseases (just to name a few), the microbiome has a huge impact on human physiology. But microbiomes aren’t just for humans, or even just for mammals. The composition and function of microbiomes are critical for most animals and plants, so much so that many scientists believe that hosts and their microbiomes should be considered as single ecological unit—the holobiont. Given their ubiquity and importance, researchers are now investigating how this symbiotic relationship between hosts and microbes has evolved over time.
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Remnants of an Ancient Metabolism without Phosphate

Highlights 
•We computationally test the plausibility of an ancient metabolism without phosphate 
•A phosphate-independent network exists within biosphere-level metabolism 
•This network displays hallmarks of prebiotic chemistry, e.g., iron-sulfur cofactors 
•This could represent a “metabolic fossil” of early thioester-driven biochemistry 

Summary 
Phosphate is essential for all living systems, serving as a building block of genetic and metabolic machinery. However, it is unclear how phosphate could have assumed these central roles on primordial Earth, given its poor geochemical accessibility. We used systems biology approaches to explore the alternative hypothesis that a protometabolism could have emerged prior to the incorporation of phosphate. Surprisingly, we identified a cryptic phosphate-independent core metabolism producible from simple prebiotic compounds. This network is predicted to support the biosynthesis of a broad category of key biomolecules. Its enrichment for enzymes utilizing iron-sulfur clusters, and the fact that thermodynamic bottlenecks are more readily overcome by thioester rather than phosphate couplings, suggest that this network may constitute a “metabolic fossil” of an early phosphate-free nonenzymatic biochemistry. Our results corroborate and expand previous proposals that a putative thioester-based metabolism could have predated the incorporation of phosphate and an RNA-based genetic system.
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Experimental Evolution of Metabolic Dependency in Bacteria

Experimental Evolution of Metabolic Dependency in Bacteria | Evolutionary Biology and Complex Systems | Scoop.it
Author Summary Bacteria frequently lose seemingly essential genes from their genomes that are required to autonomously biosynthesize building block metabolites such as amino acids. It is generally unclear whether these losses are due to chance events in small populations or favored by selection, because loss-of-function mutants may save production cost when utilizing metabolites from the environment. We discovered that populations of Escherichia coli that evolved in amino acid-replete environments rapidly lost the ability to autonomously produce several amino acids, which was beneficial when amino acids were present in the environment. Interestingly, these mutants derived amino acids not just from the growth medium, but also from other, co-occurring strains. Our findings show that nutrient-containing environments drive the loss of biosynthetic genes from bacterial genomes and facilitate the establishment of metabolic cross-feeding interactions among bacteria.
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[1511.07897] Sample Path Large Deviations for Stochastic Evolutionary Game Dynamics

We study a model of stochastic evolutionary game dynamics in which the
probabilities that agents choose suboptimal actions are dependent on payoff
consequences. We prove a sample path large deviation principle, characterizing
the rate of decay of the probability that the sample path of the evolutionary
process lies in a prespecified set as the population size approaches infinity.
We use these results to describe excursion rates and stationary distribution
asymptotics in settings where the mean dynamic admits a globally attracting
state, and we compute these rates explicitly for the case of logit choice in
potential games.
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Stabilizing evolutionary forces keep ants strong

A type of natural selection, called "stabilizing selection," is thought to maintain functional characteristics in species. But it is difficult to find evidence of this type of selection through research. Now researchers are finding evidence of natural selection that maintains the status quo among ant populations.
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Storytelling without telling: The non-linguistic nature of narratives from evolutionary and narratological perspectives

Publication date: Available online 18 November 2016
Source:Language & Communication
Author(s): Marta Sibierska
In the last two decades, ‘storytelling’ has become a popular term in the evolutionary sciences. However, this notion lacks a systematic and nuanced definition. Treated intuitively, it has usually been seen as a verbal enterprise. By approaching storytelling from a cross-disciplinary perspective, this paper aims at showing that, contrary to the common assumption, the human ability to tell stories is not restricted to the verbal medium. To show this, I define storytelling and provide a set of minimal criteria for a narrative act. Then, I proceed to identify these conditions in different semiotic resources – pictorial and gestural – to demonstrate that narrating can also transpire non-verbally and with the use of different modalities, mainly the visual, but also the vocal-auditory. I also point to the directions for further discussion of non-verbal narratives in the context of language evolution and the need for evolutionary research on storytelling to have a firm foundation in disciplines such as narratology.
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Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier

Publication date: 7 April 2017
Source:Journal of Theoretical Biology, Volume 418
Author(s): Lei Wang, Zhu-Hong You, Shi-Xiong Xia, Feng Liu, Xing Chen, Xin Yan, Yong Zhou
Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins. Specifically, the protein amino acids sequence is transformed into Position-Specific Scoring Matrix (PSSM) containing biological evolution information, and then the feature vector is extracted to present protein evolutionary information using DCT algorithm; finally, the ensemble rotation forest model is used to predict whether a given protein pair is interacting or not. When performed on Yeast and H. pylori data sets, the proposed method achieved excellent results with an average accuracy of 98.54% and 88.27%. In addition, we achieved good prediction accuracy of 98.08%, 92.75%, 98.87% and 98.72% on independent data sets (C.elegans, E.coli, H.sapiens and M.musculus). In order to further evaluate the performance of our method, we compare it with the state-of-the-art Support Vector Machine (SVM) classifier and get good results. As a web server, the source code and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/DCTRF/.
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Limiting fitness distributions in evolutionary dynamics

Publication date: 7 March 2017
Source:Journal of Theoretical Biology, Volume 416
Author(s): Matteo Smerlak, Ahmed Youssef
Natural selection works on variation in fitness, but how should we measure “variation” to predict the rate of future evolution? Fisher's fundamental theorem of natural selection provides the short-run answer: the instantaneous rate of growth of a population's mean fitness is its variance in fitness. This identity captures an important feature of the evolutionary process, but, because it does not specify how the variance itself evolves in time, it cannot be used to predict evolutionary dynamics in the long run. In this paper we reconsider the problem of computing evolutionary trajectories from limited statistical information. We identify the feature of fitness distributions which controls their late-time evolution: their (suitably defined) tail indices. We show that the location, scale and shape of the fitness distribution can be predicted far into the future from the measurement of this tail index at some initial time. Unlike the “fitness waves” studied in the literature, this pattern encompasses both positive and negative selection and is not restricted to rapidly adapting populations. Our results are well supported by numerical simulations, both from the Wright–Fisher model and from a less structured genetic algorithm.
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Multimodal emotion recognition with evolutionary computation for human-robot interaction

Publication date: 30 December 2016
Source:Expert Systems with Applications, Volume 66
Author(s): Luis-Alberto Perez-Gaspar, Santiago-Omar Caballero-Morales, Felipe Trujillo-Romero
Service robotics is an important field of research for the development of assistive technologies. Particularly, humanoid robots will play an increasing and important role in our society. More natural assistive interaction with humanoid robots can be achieved if the emotional aspect is considered. However emotion recognition is one of the most challenging topics in pattern recognition and improved intelligent techniques have to be developed to accomplish this goal. Recent research has addressed the emotion recognition problem with techniques such as Artificial Neural Networks (ANNs)/Hidden Markov Models (HMMs) and reliability of proposed approaches has been assessed (in most cases) with standard databases. In this work we (1) explored on the implications of using standard databases for assessment of emotion recognition techniques, (2) extended on the evolutionary optimization of ANNs and HMMs for the development of a multimodal emotion recognition system, (3) set the guidelines for the development of emotional databases of speech and facial expressions, (4) rules were set for phonetic transcription of Mexican speech, and (5) evaluated the suitability of the multimodal system within the context of spoken dialogue between a humanoid robot and human users. The development of intelligent systems for emotion recognition can be improved by the findings of the present work: (a) emotion recognition depends on the structure of the database sub-sets used for training and testing, and it also depends on the type of technique used for recognition where a specific emotion can be highly recognized by a specific technique, (b) optimization of HMMs led to a Bakis structure which is more suitable for acoustic modeling of emotion-specific vowels while optimization of ANNs led to a more suitable ANN structure for recognition of facial expressions, (c) some emotions can be better recognized based on speech patterns instead of visual patterns, and (d) the weighted integration of the multimodal emotion recognition system optimized with these observations can achieve a recognition rate up to 97.00 % in live dialogue tests with a humanoid robot.
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[1702.06189] A Graphical Evolutionary Game Approach to Social Learning

In this work, we study the social learning problem, in which agents of a
networked system collaborate to detect the state of the nature based on their
private signals. A novel distributed graphical evolutionary game theoretic
learning method is proposed. In the proposed game-theoretic method, agents only
need to communicate their binary decisions rather than the real-valued beliefs
with their neighbors, which endows the method with low communication
complexity. Under mean field approximations, we theoretically analyze the
steady state equilibria of the game and show that the evolutionarily stable
states (ESSs) coincide with the decisions of the benchmark centralized
detector. Numerical experiments are implemented to confirm the effectiveness of
the proposed game-theoretic learning method.
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