Understanding ecosystem robustness

Understanding the properties of ecosystems that make them either vulnerable or resistant to species extinctions is vital in the context of rapidly increasing habitat modification and climatic warming. New research by Sinha et al. incorporating 'small-world' topologies and population dynamics into ecosystem network models provides new explanations for species link distributions, extinction events and the maintenance of biodiversity. Their work reveals that previously unconnected ecosystems that come into contact with each other by anthropogenic or natural means are vulnerable to large losses in species.     

Evolution of mutational robustness

We review recent advances in the understanding of the mutation-selection balance of asexual replicators. For over 30 years, population geneticists thought that an expression derived by Kimura and Maruyama in 1966 fully solved this problem. However, Kimura and Maruyama's result is only correct in the absence of neutral mutations. The inclusion of neutral mutations leads to a wealth of interesting new effects, and, in particular, to a selective pressure to evolve robustness against mutations. We cover recent literature on the population dynamics of asexual replicators on networks of neutral genotypes, on the outcompetition of fast replicators by slower ones with better mutational support, and on the probability of fixation at high mutation rates. We discuss empirical evidence for the evolution of mutational robustness, and speculate on its relevance for higher organisms.     

Mechanisms of developmental robustness

We present a review of noise buffering mechanisms responsible for developmental robustness. We focus on functions of chaperone Hsp90, miRNA, and cross-regulation of gap genes in Drosophila. The noise buffering mechanisms associated with these functions represent specific examples of the developmental canalization, reducing the phenotypical variability in presence of either genetic or environmental perturbations. We demonstrate that robustness often appears as a function of a network of interacting elements and that the system level approach is needed to understand the mechanisms of noise filtering.     

Noise and robustness in phyllotaxis

A striking feature of vascular plants is the regular arrangement of lateral organs on the stem, known as phyllotaxis. The most common phyllotactic patterns can be described using spirals, numbers from the Fibonacci sequence and the golden angle. This rich mathematical structure, along with the experimental reproduction of phyllotactic spirals in physical systems, has led to a view of phyllotaxis focusing on regularity. However all organisms are affected by natural stochastic variability, raising questions about the effect of this variability on phyllotaxis and the achievement of such regular patterns. Here we address these questions theoretically using a dynamical system of interacting sources of inhibitory field. Previous work has shown that phyllotaxis can emerge deterministically from the self-organization of such sources and that inhibition is primarily mediated by the depletion of the plant hormone auxin through polarized transport. We incorporated stochasticity in the model and found three main classes of defects in spiral phyllotaxis--the reversal of the handedness of spirals, the concomitant initiation of organs and the occurrence of distichous angles--and we investigated whether a secondary inhibitory field filters out defects. Our results are consistent with available experimental data and yield a prediction of the main source of stochasticity during organogenesis. Our model can be related to cellular parameters and thus provides a framework for the analysis of phyllotactic mutants at both cellular and tissular levels. We propose that secondary fields associated with organogenesis, such as other biochemical signals or mechanical forces, are important for the robustness of phyllotaxis. More generally, our work sheds light on how a target pattern can be achieved within a noisy background.     

Population genetics of translational robustness

Recent work has shown that expression level is the main predictor of a gene's evolutionary rate and that more highly expressed genes evolve slower. A possible explanation for this observation is selection for proteins that fold properly despite mistranslation, in short selection for translational robustness. Translational robustness leads to the somewhat paradoxical prediction that highly expressed genes are extremely tolerant to missense substitutions but nevertheless evolve very slowly. Here, we study a simple theoretical model of translational robustness that allows us to gain analytic insight into how this paradoxical behavior arises.     

Cell communities and robustness in development

The robustness of patterning events in development is a key feature that must be accounted for in proposed models of these events. When considering explicitly cellular systems, robustness can be exhibited at different levels of organization. Consideration of two widespread patterning mechanisms suggests that robustness at the level of cell communities can result from variable development at the level of individual cells; models of these mechanisms show how interactions between participating cells guarantee community-level robustness. Cooperative interactions enhance homogeneity within communities of like cells and the sharpness of boundaries between communities of distinct cells, while competitive interactions amplify small inhomogeneities within communities of initially equivalent cells, resulting in fine-grained patterns of cell specialization.     

The nature of robustness in development

A trait is robust to a genetic or environmental variable if its variation is weakly correlated with variation in that variable. The source of robustness lies in the fact that the developmental processes that give rise to complex traits are nonlinear. A consequence of this nonlinearity is that not all genes are equally correlated with the trait whose ontogeny they control. Here we explore how developmental mechanisms determine and alter the correlation structure between genes and the traits that they control. A formula is developed by which the correlation of a gene or environmental variable with a trait can be calculated if the mechanism that gives rise to the trait is known. The nature of robustness and the ways in which robustness can evolve are discussed in the context of the problems that arise in the analysis of inherently nonlinear systems.     

Cyclin aggregation and robustness of bio-switching

During the cell cycle, Cdc2-cyclin B kinase abruptly becomes active and triggers the entry into mitosis/meiosis. Recently, it was found that inactive Cdc2-cyclin B is present in aggregates in immature starfish oocytes and becomes disaggregated at the time of its activation during maturation. We discuss a possible scenario in which aggregation of Cdc2-cyclin B dramatically enhances robustness of this activation. In this scenario, only inactive Cdc2-cyclin B can form aggregates, and the aggregates are in equilibrium with inactive Cdc2-cyclin B in solution. During maturation, the hormone-triggered inactivation of Myt1 depletes the soluble inactive Cdc2-cyclin B and the turnover leads to dissolution of the aggregates. This phase change, when coupled with the instability of the signaling network, provides a robust bio-switch.     

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.     

Pramlintide injection drug product robustness studies

The article examines the effects of temperature excursions and actual dose withdrawal on the quality of pramlintide injection, a multidose liquid parenteral formulation. Studies were designed to demonstrate product robustness under conditions that may occur during patient use. Pramlintide %Purity was determined by two high-performance liquid chromatography (HPLC) methods, a reversed-phase (RP-HPLC) and a strong-cation exchange (SCX-HPLC) method. A second RP-HPLC method was used to determine pramlintide potency and the concentration of them-cresol preservative. Antimicrobial preservative effectiveness testing was per USP and European Pharmacopeia (Ph. Eur.). Short-term stability studies were undertaken to probe the effects of the following conditions: 5°C to 40°C and 5°C to −20°C temperature cycling over 10 days; once daily or four-times daily dose withdrawal over 12 or 42 days; and combined 30°C storage and four-times daily dose withdrawal over 42 days. In all cases, pramlintide %Purity and potency values remained essentially unchanged or unchanged relative to controls. Similarly, product appearance, andm-cresol concentration and preservative effectiveness were not significantly affected by the stress conditions used in the 5 studies. Pramlintide injection drug product is extremely robust to challenging stress conditions that may occur during patient use of this multidose product for chronic administration.     

Quantifying robustness of biochemical network models

Background  

Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed.     

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.     

Relative ascendency predicts food web robustness

Threats to ecosystems globally from anthropogenic disturbance and climate change requires us to urgently identify the most sensitive biological communities to ensure they are effectively preserved. It is for this reason that understanding and predicting food web stability has been topical within ecology. Food web stability is a multi-faceted concept that represents the ability of a food web to maintain its integrity following disturbance, it includes resistance, resilience and fragility. In this study, we examine the ability of four food web metrics to predict the fragility to random species extinctions in 120 qualitative food webs. We show that three information-based indices out performed food web connectance in predicting fragility, with relative ascendency having the strongest relationship. Relative ascendency was a much stronger predictor of fragility than MacArthur’s stability metric, Average Mutual Information and connectance as it accounted for both the distribution and number of links between species. We also find that most qualitative food webs persist around a central tendency of relative ascendency.     

Selection for mutational robustness in finite populations

We investigate the evolutionary dynamics of a finite population of RNA sequences replicating on a neutral network. Despite the lack of differential fitness between viable sequences, we observe typical properties of adaptive evolution, such as increase of mean fitness over time and punctuated-equilibrium transitions, after initial mutation-selection balance has been reached. We find that a product of population size and mutation rate of approximately 30 or larger is sufficient to generate selection pressure for mutational robustness, even if the population size is orders of magnitude smaller than the neutral network on which the population resides. Our results show that quasispecies effects and neutral drift can occur concurrently, and that the relative importance of each is determined by the product of population size and mutation rate.