Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

The evolution of phenotypic correlations and “developmental memory”

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time

Genomic correlates of relationship QTL involved in fore-versus hind limb divergence in mice

Divergence of serially homologous elements of organisms is a common evolutionary pattern contributing to increased phenotypic complexity. Here, we study the genomic intervals affecting the variational independence of fore- and hind limb traits within an experimental mouse population. We use an advanced intercross of inbred mouse strains to map the loci associated with the degree of autonomy between fore- and hind limb long bone lengths (loci affecting the relationship between traits, relationship quantitative trait loci [rQTL]). These loci have been proposed to interact locally with the products of pleiotropic genes, thereby freeing the local trait from the variational constraint due to pleiotropic mutations. Using the known polymorphisms (single nucleotide polymorphisms [SNPs]) between the parental strains, we characterized and compared the genomic regions in which the rQTL, as well as their interaction partners (intQTL), reside. We find that these two classes of QTL intervals harbor different kinds of molecular variation. SNPs in rQTL intervals more frequently reside in limb-specific cis-regulatory regions than SNPs in intQTL intervals. The intQTL loci modified by the rQTL, in contrast, show the signature of protein-coding variation. This result is consistent with the widely accepted view that protein-coding mutations have broader pleiotropic effects than cis-regulatory polymorphisms. For both types of QTL intervals, the underlying candidate genes are enriched for genes involved in protein binding. This finding suggests that rQTL effects are caused by local interactions among the products of the causal genes harbored in rQTL and intQTL intervals. This is the first study to systematically document the population-level molecular variation underlying the evolution of character individuation

There is an obstetrical dilemma: Misconceptions about the evolution of human childbirth and pelvic form

Compared to other primates, modern humans face high rates of maternal and neonatal morbidity and mortality during childbirth. Since the early 20th century, this "difficulty" of human parturition has prompted numerous evolutionary explanations, typically assuming antagonistic selective forces acting on maternal and fetal traits, which has been termed the "obstetrical dilemma." Recently, there has been a growing tendency among some anthropologists to question the difficulty of human childbirth and its evolutionary origin in an antagonistic selective regime. Partly, this stems from the motivation to combat increasing pathologization and overmedicalization of childbirth in industrialized countries. Some authors have argued that there is no obstetrical dilemma at all, and that the difficulty of childbirth mainly results from modern lifestyles and inappropriate and patriarchal obstetric practices. The failure of some studies to identify biomechanical and metabolic constraints on pelvic dimensions is sometimes interpreted as empirical support for discarding an obstetrical dilemma. Here we explain why these points are important but do not invalidate evolutionary explanations of human childbirth. We present robust empirical evidence and solid evolutionary theory supporting an obstetrical dilemma, yet one that is much more complex than originally conceived in the 20th century. We argue that evolutionary research does not hinder appropriate midwifery and obstetric care, nor does it promote negative views of female bodies. Understanding the evolutionary entanglement of biological and sociocultural factors underlying human childbirth can help us to understand individual variation in the risk factors of obstructed labor, and thus can contribute to more individualized maternal care

Molecular evolution of HoxA13 and the multiple origins of limbless morphologies in amphibians and reptiles

Developmental processes and their results, morphological characters, are inherited through transmission of genes regulating development. While there is ample evidence that cis-regulatory elements tend to be modular, with sequence segments dedicated to different roles, the situation for proteins is less clear, being particularly complex for transcription factors with multiple functions. Some motifs mediating protein-protein interactions may be exclusive to particular developmental roles, but it is also possible that motifs are mostly shared among different processes. Here we focus on HoxA13, a protein essential for limb development. We asked whether the HoxA13 amino acid sequence evolved similarly in three limbless clades: Gymnophiona, Amphisbaenia and Serpentes. We explored variation in ω (dN/dS) using a maximum-likelihood framework and HoxA13sequences from 47 species. Comparisons of evolutionary models provided low ω global values and no evidence that HoxA13 experienced relaxed selection in limbless clades. Branch-site models failed to detect evidence for positive selection acting on any site along branches of Amphisbaena and Gymnophiona, while three sites were identified in Serpentes. Examination of alignments did not reveal consistent sequence differences between limbed and limbless species. We conclude that HoxA13 has no modules exclusive to limb development, which may be explained by its involvement in multiple developmental processes

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Eco-evolutionary dynamics on deformable fitness landscapes

Conventional approaches to modelling ecological dynamics often do not include evolutionary changes in the genetic makeup of component species and, conversely, conventional approaches to modelling evolutionary changes in the genetic makeup of a population often do not include ecological dynamics. But recently there has been considerable interest in understanding the interaction of evolutionary and ecological dynamics as coupled processes. However, in the context of complex multi-species ecosytems, especially where ecological and evolutionary timescales are similar, it is difficult to identify general organising principles that help us understand the structure and behaviour of complex ecosystems. Here we introduce a simple abstraction of coevolutionary interactions in a multi-species ecosystem. We model non-trophic ecological interactions based on a continuous but low-dimensional trait/niche space, where the location of each species in trait space affects the overlap of its resource utilisation with that of other species. The local depletion of available resources creates, in effect, a deformable fitness landscape that governs how the evolution of one species affects the selective pressures on other species. This enables us to study the coevolution of ecological interactions in an intuitive and easily visualisable manner. We observe that this model can exhibit either of the two behavioural modes discussed in the literature; namely, evolutionary stasis or Red Queen dynamics, i.e., continued evolutionary change. We find that which of these modes is observed depends on the lag or latency between the movement of a species in trait space and its effect on available resources. Specifically, if ecological change is nearly instantaneous compared to evolutionary change, stasis results; but conversely, if evolutionary timescales are closer to ecological timescales, such that resource depletion is not instantaneous on evolutionary timescales, then Red Queen dynamics result. We also observe that in the stasis mode, the overall utilisation of resources by the ecosystem is relatively efficient, with diverse species utilising different niches, whereas in the Red Queen mode the organisation of the ecosystem is such that species tend to clump together competing for overlapping resources. These models thereby suggest some basic conditions that influence the organisation of inter-species interactions and the balance of individual and collective adaptation in ecosystems, and likewise they also suggest factors that might be useful in engineering artificial coevolution

Genetic Associations with Gestational Duration and Spontaneous Preterm Birth

BACKGROUND Despite evidence that genetic factors contribute to the duration of gestation and the risk of preterm birth, robust associations with genetic variants have not been identified. We used large data sets that included the gestational duration to determine possible genetic associations. METHODS We performed a genomewide association study in a discovery set of samples obtained from 43,568 women of European ancestry using gestational duration as a continuous trait and term or preterm (<37 weeks) birth as a dichotomous outcome. We used samples from three Nordic data sets (involving a total of 8643 women) to test for replication of genomic loci that had significant genomewide association (P<5.0x10(-8)) or an association with suggestive significance (P<1.0x10(-6)) in the discovery set. RESULTS In the discovery and replication data sets, four loci (EBF1, EEFSEC, AGTR2, and WNT4) were significantly associated with gestational duration. Functional analysis showed that an implicated variant in WNT4 alters the binding of the estrogen receptor. The association between variants in ADCY5 and RAP2C and gestational duration had suggestive significance in the discovery set and significant evidence of association in the replication sets; these variants also showed genomewide significance in a joint analysis. Common variants in EBF1, EEFSEC, and AGTR2 showed association with preterm birth with genomewide significance. An analysis of mother-infant dyads suggested that these variants act at the level of the maternal genome. CONCLUSIONS In this genomewide association study, we found that variants at the EBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C loci were associated with gestational duration and variants at the EBF1, EEFSEC, and AGTR2 loci with preterm birth. Previously established roles of these genes in uterine development, maternal nutrition, and vascular control support their mechanistic involvement.Peer reviewe

Genetic variation in the pleiotropic association between physical activity and body weight in mice

<p>Abstract</p> <p>Background</p> <p>A sedentary lifestyle is often assumed to lead to increases in body weight and potentially obesity and related diseases but in fact little is known about the genetic association between physical activity and body weight. We tested for such an association between body weight and the distance, duration, and speed voluntarily run by 310 mice from the F₂generation produced from an intercross of two inbred lines that differed dramatically in their physical activity levels.</p> <p>Methods</p> <p>We used a conventional interval mapping approach with SNP markers to search for QTLs that affected both body weight and activity traits. We also conducted a genome scan to search for relationship QTLs (<it>rel</it>QTLs), or chromosomal regions that affected an activity trait variably depending on the phenotypic value of body weight.</p> <p>Results</p> <p>We uncovered seven quantitative trait loci (QTLs) affecting body weight, but only one co-localized with another QTL previously found for activity traits. We discovered 19 <it>rel</it>QTLs that provided evidence for a genetic (pleiotropic) association of physical activity and body weight. The three genotypes at each of these loci typically exhibited a combination of negative, zero, and positive regressions of the activity traits on body weight, the net effect of which was to produce overall independence of body weight from physical activity. We also demonstrated that the <it>rel</it>QTLs produced these varying associations through differential epistatic interactions with a number of other epistatic QTLs throughout the genome.</p> <p>Conclusion</p> <p>It was concluded that individuals with specific combinations of genotypes at the <it>rel</it>QTLs and <it>epi</it>QTLs might account for some of the variation typically seen in plots of the association of physical activity with body weight.</p

Developmental pathways inferred from modularity, morphological integration and fluctuating asymmetry patterns in the human face

Facial asymmetries are usually measured and interpreted as proxies to developmental noise. However, analyses focused on its developmental and genetic architecture are scarce. To advance on this topic, studies based on a comprehensive and simultaneous analysis of modularity, morphological integration and facial asymmetries including both phenotypic and genomic information are needed. Here we explore several modularity hypotheses on a sample of Latin American mestizos, in order to test if modularity and integration patterns difer across several genomic ancestry backgrounds. To do so, 4104 individuals were analyzed using 3D photogrammetry reconstructions and a set of 34 facial landmarks placed on each individual. We found a pattern of modularity and integration that is conserved across sub-samples difering in their genomic ancestry background. Specifcally, a signal of modularity based on functional demands and organization of the face is regularly observed across the whole sample. Our results shed more light on previous evidence obtained from Genome Wide Association Studies performed on the same samples, indicating the action of diferent genomic regions contributing to the expression of the nose and mouth facial phenotypes. Our results also indicate that large samples including phenotypic and genomic metadata enable a better understanding of the developmental and genetic architecture of craniofacial phenotypes