Degeneracy: A design principle for achieving robustness and evolvability

Robustness, the insensitivity of some of a biological system's functionalities to a set of distinct conditions, is intimately linked to fitness. Recent studies suggest that it may also play a vital role in enabling the evolution of species. Increasing robustness, so is proposed, can lead to the emergence of evolvability if evolution proceeds over a neutral network that extends far throughout the fitness landscape. Here, we show that the design principles used to achieve robustness dramatically influence whether robustness leads to evolvability. In simulation experiments, we find that purely redundant systems have remarkably low evolvability while degenerate, i.e. partially redundant, systems tend to be orders of magnitude more evolvable. Surprisingly, the magnitude of observed variation in evolvability can neither be explained by differences in the size nor the topology of the neutral networks. This suggests that degeneracy, a ubiquitous characteristic in biological systems, may be an important enabler of natural evolution. More generally, our study provides valuable new clues about the origin of innovations in complex adaptive systems.     

Sloppiness, robustness, and evolvability in systems biology

The functioning of many biochemical networks is often robust-remarkably stable under changes in external conditions and internal reaction parameters. Much recent work on robustness and evolvability has focused on the structure of neutral spaces, in which system behavior remains invariant to mutations. Recently we have shown that the collective behavior of multiparameter models is most often sloppy: insensitive to changes except along a few 'stiff' combinations of parameters, with an enormous sloppy neutral subspace. Robustness is often assumed to be an emergent evolved property, but the sloppiness natural to biochemical networks offers an alternative nonadaptive explanation. Conversely, ideas developed to study evolvability in robust systems can be usefully extended to characterize sloppy systems.     

Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics

The emergence of a unified cognitive behaviour relies on the coordination of specialized components that distribute across a ‘brain’, body and environment. Although a general dynamical mechanism involved in agent-environment integration is still largely unknown for behavioural robustness, discussions here are focussed on one of the most plausible candidate: the formation of distributed mechanisms working in transient during agent-environment coupling. This article provides discussions on this sort of coordination based on a mobile object-tracking task with situated, embodied and minimal agents, and tests for robust yet adaptive behaviour. The proposed scenario provides examples of behavioural mechanisms that counterbalance the functional organization of internal control activity and agents’ situatedness to enable the evolution of a two-agent interaction task. Discussions in this article suggest that future studies of distributed cognition should take into account that there are at least two possible modes of interpreting distributed mechanisms and that these have a qualitatively different effect on behavioural robustness.     

Dynamic modeling of complex biological systems: a link between metabolic and macroscopic description

In this study, a class of dynamic models based on metabolic reaction pathways is analyzed, showing that systems with complex intracellular reaction networks can be represented by macroscopic reactions relating extracellular components only. Based on rigorous assumptions, the model reduction procedure is systematic and allows an equivalent 'input-output' representation of the system to be derived. The procedure is illustrated with a few examples.     

On the origins of morphological variation, canalization, robustness, and evolvability

Canalization is a concept, introduced by Waddington that describesthe reduced sensitivity of a phenotype to genetic and environmentalperturbations. Some research in canalization assumes that lackof variation in a trait in one genotype with respect to anothergenotype in a population, is due to the existence of bufferingmechanisms against environmental and/or genetic variation. Thisarticle criticizes this assumption and out points out otherpossible problems with the concepts of canalization, robustness,and evolvability. These involve: the neglect of alternativeexplanations for the lack of variation in a trait, the incompatibilitywith current understanding of development, the way the mutivariatenature of morphological variation is considered. In addition,this article tries to explain that these concepts implicitlyassume, although not generally acknowledged, that without bufferingany genetic or environmental variation should give rise to adistinct phenotypic outcome. This can be avoided by restrictingthe use of canalization to cases in which, as in hsp90, thereis direct evidence of buffering. For the other cases it wouldbe clearer to talk about variational properties or simply typeof variation. The concept of evolvability is also biased towardsunivariate comparisons and is dependent on selective pressures.It is suggested that this can be replaced by "type of phenotypicvariation" from a genotype or variational properties. Overall,this article proposes that the concepts of canalization andevolvability involve some assumptions that, in most situations,unnecessarily complicate the study of evolution and development.     

CHIP: a link between the chaperone and proteasome systems

CHIP, carboxy terminus of Hsc70 interacting protein, is a cytoplasmic protein whose amino acid sequence is highly conserved across species. It is most highly expressed in cardiac and skeletal muscle and brain. The primary amino acid sequence is characterized by 3 domains, a tetratricopeptide repeat (TPR) domain at its amino terminus, a U-box domain at its carboxy terminus, and an intervening charged domain. CHIP interacts with the molecular chaperones Hsc70-Hsp70 and Hsp90 through its TPR domain, whereas its U-box domain contains its E3 ubiquitin ligase activity. Its interaction with these molecular chaperones results in client substrate ubiquitylation and degradation by the proteasome. Thus, CHIP acts to tilt the folding-refolding machinery toward the degradative pathway, and it serves as a link between the two. Because protein degradation is required for healthy cellular function, CHIP's ability to degrade proteins that are the signature of disease, eg, ErbB2 in breast and ovarian cancers, could prove to be a point of therapeutic intervention.     

Evolution of structure,order and complexity in biological systems: Part III of a general theory

In this, Part III of a general theory, the large-scale features of evolution of structure, order, and complexity are considered as characteristic features of the biological state of matter. This starts with a rigorous formal definition of structure, classes of structural order, complexity, measures of complexity, and how these arise through evolution by a cumulative process of storing information in memory systems. Three such memory systems have evolved: the genetic memory, the immune memory, and the memories of the nervous system. The evolution, characteristic parameters and the limitations of these memory systems are explored. From these considerations emerge the large-scale features of the evolutionary pathways of biological structure, function, and complexity.     

The effect of genetic robustness on evolvability in digital organisms

Background  

Recent work has revealed that many biological systems keep functioning in the face of mutations and therefore can be considered genetically robust. However, several issues related to robustness remain poorly understood, such as its implications for evolvability (the ability to produce adaptive evolutionary innovations).     

Genetic and environment-induced pathways to innovation: on the possibility of a universal relationship between robustness and adaptation in complex biological systems

Recently, there has been considerable interest in the idea that mutational robustness enhances the propensity for future adaptations, i.e. evolvability, if evolution proceeds over a neutral network that extends far throughout a fitness landscape. While the genetic neutral network (NN-G) model may have important implications to our understanding of evolution, little has been done to integrate these theoretical developments with empirical evidence that heritable phenotypes can also originate and become fixated as a result of changes in the environment. In this brief commentary, I reconsider the role of environmental change in the adaptation of species and ask whether positive robustness-evolvability relationships might exist not only for genetic but also environmental buffering. In particular, I ask whether the insensitivity of species fitness towards variability in its environment can have a positive influence on the likelihood of future environment-induced adaptations (i.e. ecological opportunities) in a manner analogous to that proposed by the NN-G model. After outlining scenarios where such a counter-intuitive relationship appears plausible, I comment on the merits of evolutionary theories that can integrate complementary pathways to adaptation under static and time-variant environments. I also speculate on some of the features that such a theory might have.     

Haplotype diversity: the link between statistical and biological association

In the rapidly growing field of association mapping in plants, the use of (marker) haplotypes rather than single markers can be an effective way of improving detection power. Here, we highlight the information that can be obtained from deducing the historical relationships between haplotypes. The ordering of haplotype classes according to deduced historical relationships should further enhance association detection power, but can also be used to predict the genotypic and phenotypic values of unobserved germplasm.     

Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability

A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs). This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs' underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy). In the second scenario segments and domains evolve simultaneously (SS strategy). We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity. We find that while SS networks generate segments and domains in an integrated manner, SF networks use largely independent modules to produce segments and domains. Surprisingly, we find that widely used, purely architectural methods for determining network modularity completely fail to establish this higher modularity of SF networks. Finally, we observe that, as a free side effect of evolving segmentation and differentiation in combination, we obtained in-silico developmental mechanisms resembling mechanisms used in vertebrate development.     

Water-escape in weanling rats: A link between behaviour and biological fitness

Relationships between behaviour, measured in many standard laboratory settings, and biological fitness are often obscure. By making inferences from the genetic architecture revealed in psychogenetic studies, links between behaviour and biological fitness can be forged. To support these inferences, information is needed about the genetic architecture underlying behaviour which has unequivocal links to fitness. In this study, water-escape in rats is related a priori to fitness. Weanling rats from the Roman High Avoidance and Roman Low Avoidance strains and their reciprocal F₁ crosses are tested in a water-escape apparatus. A strong directional dominance found for rapid escape is in keeping with the assumptions made and confirms the test as a useful tool for psychogenetic studies of rats.     

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

This paper describes recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.     

The Hsp90 capacitor, developmental remodeling, and evolution: the robustness of gene networks and the curious evolvability of metamorphosis

Genetic capacitors moderate expression of heritable variation and provide a novel mechanism for rapid evolution. The prototypic genetic capacitor, Hsp90, interfaces stress responses, developmental networks, trait thresholds and expression of wide-ranging morphological changes in Drosophila and other organisms. The Hsp90 capacitor hypothesis, that stress-sensitive storage and release of genetic variation through Hsp90 facilitates adaptive evolution in unpredictable environments, has been challenged by the belief that Hsp90-buffered variation is unconditionally deleterious. Here we review recent results supporting the Hsp90 capacitor hypothesis, highlighting the heritability, selectability, and potential evolvability of Hsp90-buffered traits. Despite a surprising bias toward morphological novelty and typically invariable quantitative traits, Hsp90-buffered changes are remarkably modular, and can be selected to high frequency independent of the expected negative side-effects or obvious correlated changes in other, unselected traits. Recent dissection of cryptic signal transduction variation involved in one Hsp90-buffered trait reveals potentially dozens of normally silent polymorphisms embedded in cell cycle, differentiation and growth control networks. Reduced function of Hsp90 substrates during environmental stress would destabilize robust developmental processes, relieve developmental constraints and plausibly enables genetic network remodeling by abundant cryptic alleles. We speculate that morphological transitions controlled by Hsp90 may fuel the incredible evolutionary lability of metazoan life-cycles.     

Exploiting biological complexity for strain improvement through systems biology

Cellular complexity makes it difficult to build a complete understanding of cellular function but also offers innumerable possibilities for modifying the cellular machinery to achieve a specific purpose. The exploitation of cellular complexity for strain improvement has been a challenging goal for applied biological research because it requires the coordinated understanding of multiple cellular processes. It is therefore pursued most efficiently in the framework of systems biology. Progress in strain improvement will depend not only on advances in technologies for high-throughput measurements but, more importantly, on the development of theoretical methods that increase the information content of these measurements and, as such, facilitate the elucidation of mechanisms and the identification of genetic targets for modification.     

The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks

We investigate how scale-free (SF) and Erd?s-Rényi (ER) topologies affect the interplay between evolvability and robustness of model gene regulatory networks with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006) we find that networks with SF_in topologies, that is SF topology for incoming nodes and ER topology for outgoing nodes, are significantly more evolvable towards specific oscillatory targets than networks with ER topology for both incoming and outgoing nodes. Similar results are found for networks with SF_both and SF_out topologies. The functionality of the SF_out topology, which most closely resembles the structure of biological gene networks (Babu et al., 2004), is compared to the ER topology in further detail through an extension to multiple target outputs, with either an oscillatory or a non-oscillatory nature. For multiple oscillatory targets of the same length, the differences between SF_out and ER networks are enhanced, but for non-oscillatory targets both types of networks show fairly similar evolvability. We find that SF networks generate oscillations much more easily than ER networks do, and this may explain why SF networks are more evolvable than ER networks are for oscillatory phenotypes. In spite of their greater evolvability, we find that networks with SF_out topologies are also more robust to mutations (mutational robustness) than ER networks. Furthermore, the SF_out topologies are more robust to changes in initial conditions (environmental robustness). For both topologies, we find that once a population of networks has reached the target state, further neutral evolution can lead to an increase in both the mutational robustness and the environmental robustness to changes in initial conditions.     

Proteomics: a link between genomics,genetics and physiology

Thanks to spectacular advances in the techniques for identifying proteins separated by two-dimensional electrophoresis and in methods for large-scale analysis of proteome variations, proteomics is becoming an essential methodology in various fields of plant biology. In the study of pleiotropic effects of mutants and in the analysis of responses to hormones and to environmental changes, the identification of involved metabolic pathways can be deduced from the function of affected proteins. In molecular quantitative genetics, proteomics can be used to map translated genes and loci controlling their expression, which can be used to identify proteins accounting for the variation of complex phenotypic traits. Linking gene expression to cell metabolism on the one hand and to genetic maps on the other, proteomics is a central tool for functional genomics.     

Variability and robustness in biomolecular systems

The need to perform sophisticated information processing in an environment that is variable and noisy restricts the functional design of biological networks. We discuss several of the strategies that cells and multicellular organisms have evolved to deal with this demand.     

Geometric robustness theory and biological networks

We provide a geometric framework for investigating the robustness of information flows over biological networks. We use information measures to quantify the impact of knockout perturbations on simple networks. Robustness has two components, a measure of the causal contribution of a node or nodes, and a measure of the change or exclusion dependence, of the network following node removal. Causality is measured as statistical contribution of a node to network function, wheras exclusion dependence measures a distance between unperturbed network and reconfigured network function. We explore the role that redundancy plays in increasing robustness, and how redundacy can be exploited through error-correcting codes implemented by networks. We provide examples of the robustness measure when applied to familiar boolean functions such as the AND, OR and XOR functions. We discuss the relationship between robustness measures and related measures of complexity and how robustness always implies a minimal level of complexity.