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.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

The Number of Different Binary Functions Generated by NK-Kauffman Networks and the Emergence of Genetic Robustness

We determine the average number $\vartheta (N, K)$, of \textit{NK}-Kauffman networks that give rise to the same binary function. We show that, for $N \gg 1$, there exists a connectivity critical value $K_c$ such that $\vartheta(N,K) \approx e^{\phi N}$ ($\phi > 0$) for $K < K_c$ and $\vartheta(N,K) \approx 1$ for $K > K_c$. We find that $K_c$ is not a constant, but scales very slowly with $N$, as $K_c \approx \log_2 \log_2 (2N / \ln 2)$. The problem of genetic robustness emerges as a statistical property of the ensemble of \textit{NK}-Kauffman networks and impose tight constraints in the average number of epistatic interactions that the genotype-phenotype map can have.Comment: 4 figures 18 page

The Number of Different Binary Functions Generated by NK-Kauffman Networks and the Emergence of Genetic Robustness

We determine the average number $\vartheta (N, K)$, of \textit{NK}-Kauffman networks that give rise to the same binary function. We show that, for $N \gg 1$, there exists a connectivity critical value $K_c$ such that $\vartheta(N,K) \approx e^{\phi N}$ ($\phi > 0$) for $K < K_c$ and $\vartheta(N,K) \approx 1$ for $K > K_c$. We find that $K_c$ is not a constant, but scales very slowly with $N$, as $K_c \approx \log_2 \log_2 (2N / \ln 2)$. The problem of genetic robustness emerges as a statistical property of the ensemble of \textit{NK}-Kauffman networks and impose tight constraints in the average number of epistatic interactions that the genotype-phenotype map can have.Comment: 4 figures 18 page

Degeneracy: a link between evolvability, robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology. This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability

Kulcsfontosságú gének genomikai előrejelzése: In Silico megközelítés = Genomic prediction of essential genes: in silico approach

Kulcsfontosságú gének bioinformatikai elemzése: Csoportunk számos számos olyan sajátságot ismertek fel, melyek segítségével jellemezni lehet az esszenciális vagy a géndózis változására érzékeny géneket. Ezek közül a génduplikációt, az alternatív anyagcsereútvonalak jelenlétét, a génkifejeződés mértékét és a gén genomon belüli pozícióját érdemes megemlíteni. Rendszerbiológiai modellek alapján kulcsfontosságú metabolikus gének előrejelzése: Előzetesen leírt módszerekre alapozva, részletes vizsgálatnak vetettük alá a sörélesztő rekonstruált metabolikus hálózatát, majd megvizsgáltuk, hogyan viselkedik a rendszer ha egy-egy enzim működésképtelen. Módszerünk sikeresen jelzi előre az esszenciális gének 85%-át. Ez a siker lehetővé tette, hogy a biológia olyan kulcskérdéseire keressünk választ, mint a mutációkkal szembeni robusztusság háttere, a biológiai hálózatok evolúciós változása vagy a minimál genomok természete. Genetikai interakciók rendszerbiológiai és kísérleti vizsgálata: Anyagcserehálózat rendszerbiológiai modellünk komoly lehetőséget biztosít a genetikai interakciók mélyebb megértéséhez. A modell sikeresen képes előrejelezni speciális genetikai interakciók jelenlétét. Számos érvünk szól amelett, hogy a mutációkkal szembeni robusztusság a különböző környezeti feltételekhez való alkalmazkodás mellékterméke. | Bioinformatics analyses of essential genes: We identified several cellular and genomic features that enable reliable characterization of essential and dosage sensitive genes: Gene duplication, alternative metabolic pathways, gene expression level and genomic position all have some effect on gene dispensability. In silico prediction of essential metabolic genes using systems biological models: We have employed and further developed a previously elaborated metabolic network model of yeast. Our method predicts gene essentiality with about 85% accuracy. These methods have enabled us to study several key issues in evolutionary biology, such as the nature of mutational robustness and minimal genomes or the driving forces in the evolution of metabolic networks. Computational and experimental analyses of genetic interactions: The computational model described above paves the way for gaining novel insights into the nature of genetic interactions. The current model is able to predict the presence of genetic interactions in the metabolic networks of yeast with nearly 50% accuracy, while only approximately 0.5% would be expected by chance. Along with other arguments, our findings suggest that apparent robustness against harmful mutations is not a directly selected trait, but it's rather a by-product of organismal adaptation to varying environments

Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss.

Many genes can be deleted with little phenotypic consequences. By what mechanism and to what extent the presence of duplicate genes in the genome contributes to this robustness against deletions has been the subject of considerable interest. Here, we exploit the availability of high-density genetic interaction maps to provide direct support for the role of backup compensation, where functionally overlapping duplicates cover for the loss of their paralog. However, we find that the overall contribution of duplicates to robustness against null mutations is low ( approximately 25%). The ability to directly identify buffering paralogs allowed us to further study their properties, and how they differ from non-buffering duplicates. Using environmental sensitivity profiles as well as quantitative genetic interaction spectra as high-resolution phenotypes, we establish that even duplicate pairs with compensation capacity exhibit rich and typically non-overlapping deletion phenotypes, and are thus unable to comprehensively cover against loss of their paralog. Our findings reconcile the fact that duplicates can compensate for each other's loss under a limited number of conditions with the evolutionary instability of genes whose loss is not associated with a phenotypic penalty