Robustness and Evolvability

Masel, Joanna; Trotter, Meredith V.

doi:10.1016/j.tig.2010.06.002

Cited by 233 publications

(221 citation statements)

References 90 publications

(145 reference statements)

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“…We next consider evolvability, which is most readily detected when the organism is under stress (29) or when acquiring new capacities such as during external training in our experiment. We found that the community organization of brain connectivity reconfigured adaptively over time.…”

Section: Resultsmentioning

confidence: 99%

“…The accompanying system decomposability provides necessary structure for complex reconfigurations. Modularity can be a property of morphology, as has been widely described in the context of evolution and development (11,12,29), as well as of the interconnection patterns of social, biological, and technological systems (30,31). More pertinent to this paper, recent evidence suggests that modular organization over several spatial scales, or hierarchical modularity, also characterizes the large-scale anatomical connectivity of the human brain (27,28), as well as the spontaneous fluctuations (35,36) thought to stem from anatomical patterns (37).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic reconfiguration of human brain networks during learning

Bassett

Wymbs

Porter

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,459

1,702

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Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes-flexibility and selection-must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.complex network | time-dependent network | fMRI | motor learning | community structure T he brain is a complex system, composed of many interacting parts, which dynamically adapts to a continually changing environment over multiple temporal scales. Over relatively short temporal scales, rapid adaptation and continuous evolution of those interactions or connections form the neurophysiological basis for behavioral adaptation or learning. At small spatial scales, stable neurophysiological signatures of learning have been best demonstrated in animal systems at the level of individual synapses between neurons (1-3). At a larger spatial scale, it is also well-known that specific regional changes in brain activity and effective connectivity accompany many forms of learning in humans-including the acquisition of motor skills (4, 5).Learning-associated adaptability is thought to stem from the principle of cortical modularity (6). Modular, or nearly decomposable (7), structures are aggregates of small subsystems (modules) that can perform specific functions without perturbing the remainder of the system. Such structure provides a combination of compartmentalization and redundancy, which reduces the interdependence of components, enhances robustness, and facilitates behavioral adaptation (8, 9). Modular organization also confers evolvability on a system by reducing constraints on change (8,(10)(11)(12). Indeed, a putative relationship between modularity and adaptability in the context of human neuroscience has recently been posited (13,14). To date, however, the existence of modularity in large-scale cortical connectivity during learning has not been tested directly.Based on the aforementioned theoretical and empirical grounds, we hypothesized that the principle of modularity would characterize the fundamental organiz...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dynamic reconfiguration of human brain networks during learning

Bassett

Wymbs

Porter

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,459

1,702

View full text Add to dashboard Cite

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“…Other recent reviews have focused on phenotypic variability and its relationship to recombination [17], enzyme promiscuity [18], commonalities among different system classes [19] and phenotypic constraints [20]. In contrast, this review's focus is the role of robustness in phenotypic variability.…”

Section: Introductionmentioning

confidence: 99%

The role of robustness in phenotypic adaptation and innovation

2012

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Phenotypes that vary in response to DNA mutations are essential for evolutionary adaptation and innovation. Therefore, it seems that robustness, a lack of phenotypic variability, must hinder adaptation. The main purpose of this review is to show why this is not necessarily correct. There are two reasons. The first is that robustness causes the existence of genotype networks-large connected sets of genotypes with the same phenotype. I discuss why genotype networks facilitate phenotypic variability. The second reason emerges from the evolutionary dynamics of evolving populations on genotype networks. I discuss how these dynamics can render highly robust phenotypes more variable, using examples from protein and RNA macromolecules. In addition, robustness can help avoid an important evolutionary conflict between the interests of individuals and populations-a conflict that can impede evolutionary adaptation.

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“…Such higher order effects have always been questioned in biology, since they might be expected, in real populations, to be swamped by direct selective effects (Pigliucci, 2008). However, discussion has intensified recently over the concepts of 'robustness' and 'evolvability' (Masel and Trotter, 2010). These are higher order effects of somewhat unclear definition; the latter potentially relates directly to the control of mutation rate considered here.…”

Section: Discussionmentioning

confidence: 95%

Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

Belavkin¹,

Channon²,

Aston³

et al. 2011

Ecal 2011

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We investigate the problem of optimal control of mutation by asexual self-replicating organisms represented by points in a metric space. We introduce the notion of a relatively monotonic fitness landscape and consider a generalisation of Fisher's geometric model of adaptation for such spaces. Using a Hamming space as a prime example, we derive the probability of adaptation as a function of reproduction parameters (e.g. mutation size or rate). Optimal control rules for the parameters are derived explicitly for some relatively monotonic landscapes, and then a general information-based heuristic is introduced. We then evaluate our theoretical control functions against optimal mutation functions evolved from a random population of functions using a meta genetic algorithm. Our experimental results show a close match between theory and experiment. We demonstrate this result both in artificial fitness landscapes, defined by a Hamming distance, and a natural landscape, where fitness is defined by a DNA-protein affinity. We discuss how a control of mutation rate could occur and evolve in natural organisms. We also outline future directions of this work.

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Robustness and Evolvability

Cited by 233 publications

References 90 publications

Dynamic reconfiguration of human brain networks during learning

Dynamic reconfiguration of human brain networks during learning

The role of robustness in phenotypic adaptation and innovation

Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

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