Author summary The phenotype of an organism comprises the set of morphological and functional traits encoded by its genome. In natural evolving systems, phenotypes are organized into mutationally connected networks of genotypes, which increase the likelihood for an evolving population to encounter novel adaptive phenotypes (i.e., its evolvability). We do not know whether artificial systems, such as self-replicating and evolving computer programs—digital organisms—are more or less evolvable than natural systems. By studying how genotypes map onto phenotypes in digital organisms, we characterize many commonalities between natural and artificial evolving systems. In addition, we show that phenotypic complexity can both facilitate and constrain evolution, which harbors lessons not only for designing evolvable artificial systems, but also for synthetic biology.
Author Summary Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.
Arjen ten Have's insight:
Hierarchy evolves as a result of connection cost. How cool!
This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications.
An efficient system to fund science: from proposal review to peer-to-peer distributions
Johan Bollen, David Crandall, Damion Junk, Ying Ding, Katy Börner
A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.
Control principles of complex systems Yang-Yu Liu and Albert-László Barabási Rev. Mod. Phys. 88, 035006
Genomic plasticity enables adaptation to changing environments, which is especially relevant for pathogens that engage in 'arms races' with their hosts. In many pathogens, genes mediating virulence cluster in highly variable, transposon-rich, physically distinct genomic compartments. However, understanding of the evolution of these compartments, and the role of transposons therein, remains limited. Here, we show that transposons are the major driving force for adaptive genome evolution in the fungal plant pathogen Verticillium dahliae. We show that highly variable lineage-specific (LS) regions evolved by genomic rearrangements that are mediated by erroneous double-strand repair, often utilizing transposons. We furthermore show that recent genetic duplications are enhanced in LS regions, against an older episode of duplication events. Finally, LS regions are enriched in active transposons, which contribute to local genome plasticity. Thus, we provide evidence for genome shaping by transposons, both in an active and passive manner, which impacts the evolution of pathogen virulence.
For over 140 years, lichens have been regarded as a symbiosis between a single fungus, usually an ascomycete, and a photosynthesizing partner. Other fungi have long been known to occur as occasional parasites or endophytes, but the one lichen–one fungus paradigm has seldom been questioned. Here we show that many common lichens are composed of the known ascomycete, the photosynthesizing partner, and, unexpectedly, specific basidiomycete yeasts. These yeasts are embedded in the cortex, and their abundance correlates with previously unexplained variations in phenotype. Basidiomycete lineages maintain close associations with specific lichen species over large geographical distances and have been found on six continents. The structurally important lichen cortex, long treated as a zone of differentiated ascomycete cells, appears to consistently contain two unrelated fungi.
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
Arjen ten Have's insight:
where preprints go wrong. The construction of an academic niche? This is meant as an insult and i wonder based on what. Niche construction is like Wrights hypothesis of balanced shift very much to be seen as a metaphor.
The range of hosts that parasites can infect is a key determinant of the emergence and spread of disease. Yet the impact of host range variation on the evolution of parasite genomes remains unknown. Here, we show that codon optimization underlies genome adaptation in broad host range parasite. We found that the longer proteins encoded by broad host range fungi likely increase natural selection on codon optimization in these species. Accordingly, codon optimization correlates with host range across the fungal kingdom. At the species level, biased patterns of synonymous substitutions underpin increased codon optimization in a generalist but not a specialist fungal pathogen. Virulence genes were consistently enriched in highly codon-optimized genes of generalist but not specialist species. We conclude that codon optimization is related to the capacity of parasites to colonize multiple hosts. Our results link genome evolution and translational regulation to the long term persistence of generalist parasitism.
Smut fungi are plant pathogens mostly parasitizing wild species of grasses as well as domesticated cereal crops. Genome analysis of several smut fungi including Ustilago maydis revealed a singular clustered organization of genes encoding secreted effectors. In U. maydis, many of these clusters have a role in virulence. Reconstructing the evolutionary history of clusters of effector genes is difficult because of their intrinsically fast evolution, which erodes the phylogenetic signal and homology relationships. Here, we describe the use of comparative evolutionary analyses of quality draft assemblies of genomes to study the mechanisms of this evolution. We report the genome sequence of a South African isolate of Sporisorium scitamineum, a smut fungus parasitizing sugar cane with a phylogenetic position intermediate to the two previously sequenced species U. maydisand Sporisorium reilianum. We show that the genome of S. scitamineumcontains more and larger gene clusters encoding secreted effectors than any previously described species in this group. We trace back the origin of the clusters and find that their evolution is mainly driven by tandem gene duplication. In addition, transposable elements play a major role in the evolution of the clustered genes. Transposable elements are significantly associated with clusters of genes encoding fast evolving secreted effectors. This suggests that such clusters represent a case of genome compartmentalization that restrains the activity of transposable elements on genes under diversifying selection for which this activity is potentially beneficial, while protecting the rest of the genome from its deleterious effect.
Ah, but isn’t all this wonder simply the product of the blind fumbling of Darwinian evolution, that mindless machine which takes random variation and sieves it by natural selection? Well, not quite. You don’t have to be a benighted creationist, nor even a believer in divine providence, to argue that Darwin’s astonishing theory doesn’t fully explain why nature is so marvelously, endlessly inventive. “Darwin’s theory surely is the most important intellectual achievement of his time, perhaps of all time,” says evolutionary biologist Andreas Wagner of the University of Zurich. “But the biggest mystery about evolution eluded his theory. And he couldn’t even get close to solving it.”
Seagrasses colonized the sea on at least three independent occasions to form the basis of one of the most productive and widespread coastal ecosystems on the planet. Here we report the genome of Zostera marina (L.), the first, to our knowledge, marine angiosperm to be fully sequenced. This reveals unique insights into the genomic losses and gains involved in achieving the structural and physiological adaptations required for its marine lifestyle, arguably the most severe habitat shift ever accomplished by flowering plants. Key angiosperm innovations that were lost include the entire repertoire of stomatal genes, genes involved in the synthesis of terpenoids and ethylene signalling, and genes for ultraviolet protection and phytochromes for far-red sensing. Seagrasses have also regained functions enabling them to adjust to full salinity. Their cell walls contain all of the polysaccharides typical of land plants, but also contain polyanionic, low-methylated pectins and sulfated galactans, a feature shared with the cell walls of all macroalgae and that is important for ion homoeostasis, nutrient uptake and O2/CO2 exchange through leaf epidermal cells. The Z. marina genome resource will markedly advance a wide range of functional ecological studies from adaptation of marine ecosystems under climate warming, to unravelling the mechanisms of osmoregulation under high salinities that may further inform our understanding of the evolution of salt tolerance in crop plants.
Is Computational Biology increasingly—and steadily—progressing toward addressing the mammoth challenge of actually computing biology? That is, have we reached the stage where we do not support biological research but drive it? This question is vitally important for all—young and established computational biologists. Even though forecasting future research can be risky, we still venture to predict that the future will see considerably more research projects drifting toward this ambitious aspiration. Computational Biology is powerful for abstracting signatures of disease, for predicting it, and for proposing medications. It is effective in figuring out disease mechanisms and forceful in bridging experimental disciplines to obtain testable predictions. However, perhaps its biggest challenges lie in putting together the available broad and disparate information, devising tools to efficiently and effectively carry out these tasks while sifting through noise and recognizing cell specificity, and most importantly coming up with sound, coherent, and testable schemes.
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