Family recurrence risk of alopecia areata in the Faroe Islands.

BACKGROUND: Reports of a positive family history of alopecia areata (AA) have led to the assumption of a genetic component. The Faroe Islands population is small, has been isolated until the 20th century, and is served by only one dermatology clinic, making it highly suitable for genealogical studies. AIM: To determine the incidence of AA and to estimate the recurrence risk ratio (RRR) in five generations and in a nationwide dermatologist-based AA cohort using the Faroese genealogy database. METHODS: All registered cases of AA during the period 1973-2011 were identified from the Faroese national dermatology clinics. The AA cases were linked with the genealogy database covering the entire Faroese population, and the probability of AA among first-, second- and third-degree relatives was calculated. RESULTS: In total, 178 cases of AA were identified, giving a crude incidence of 10.1 per 100 000 person-years (10.9 for women and 9.4 for men). The cumulative incidence proportion over life was 0.8%. There was no apparent trend in the probabilities for AA in first-degree family members compared with second- and third-degree relatives. RRR was > 1 in second-degree family members only. CONCLUSION: A lower prevalence rate of AA was found than previously published. The genealogical study failed to identify any apparent trend in the RRR estimates, questioning the role of genetic factors in AA in the Faroe Islands. However, it is possible that the trend is masked by bias and low power; larger studies are therefore warranted to estimate the heritability of AA.

Interampatteness - a generic property of biochemical networks.

Analysis of gene expression data sets reveals that the variation in expression is concentrated to significantly fewer 'characteristic modes' or 'eigengenes' than the number of both recorded assays and measured genes. Previous works have stressed the importance of these characteristic modes, but neglected the equally important weak modes. Herein a generic system property - interampatteness - is defined that explains the previous feature, and assigns equal weight to the characteristic and weak modes. An interampatte network is characterised by strong INTERactions enabling simultaneous AMPlification and ATTEnuation of different signals. It is postulated that biochemical networks are interampatte, based on published experimental data and theoretical considerations. Existence of multiple time-scales and feedback loops is shown to increase the degree of interampatteness. Interampatteness has strong implications for the dynamics and reverse engineering of the network. One consequence is highly correlated changes in gene expression in response to external perturbations, even in the absence of common transcription factors, implying that interampatte gene regulatory networks erroneously may be assumed to have co-expressed/co-regulated genes. Data compression or reduction of the system dimensionality using clustering, singular value decomposition, principal component analysis or some other data mining technique results in a loss of information that will obstruct reconstruction of the underlying network. [Includes supplementary material].

Structural robustness of biochemical network models-with application to the oscillatory metabolism of activated neutrophils.

Sensitivity of biochemical network models to uncertainties in the model structure, with a focus on autonomously oscillating systems, is addressed. Structural robustness, as defined here, concerns the sensitivity of the model predictions with respect to changes in the specific interactions between the network components and encompass, for instance, uncertain kinetic models, neglected intermediate reaction steps and unmodelled transport phenomena. Traditional parametric sensitivity analysis does not address such structural uncertainties and should therefore be combined with analysis of structural robustness. Here a method for quantifying the structural robustness of models for systems displaying sustained oscillations is proposed. The method adopts concepts from robust control theory and is based on adding dynamic perturbations to the network of interacting biochemical components. In addition to providing a measure of the overall robustness, the method is able to identify specific network fragilities. The importance of considering structural robustness is demonstrated through an analysis of a recently proposed model of the oscillatory metabolism in activated neutrophils. The model displays small parametric sensitivities, but is shown to be highly unrobust to small perturbations in some of the network interactions. Identification of specific fragilities reveals that adding a small delay or diffusion term in one of the involved reactions, likely to exist in vivo, completely removes all oscillatory behaviour in the model.

Linear systems approach to analysis of complex dynamic behaviours in biochemical networks.

Central functions in the cell are often linked to complex dynamic behaviours, such as sustained oscillations and multistability, in a biochemical reaction network. Determination of the specific mechanisms underlying such behaviours is important, e.g. to determine sensitivity, robustness, and modelling requirements of given cell functions. In this work we adopt a systems approach to the analysis of complex behaviours in intracellular reaction networks, described by ordinary differential equations with known kinetic parameters. We propose to decompose the overall system into a number of low complexity subsystems, and consider the importance of interactions between these in generating specific behaviours. Rather than analysing the network in a state corresponding to the complex non-linear behaviour, we move the system to the underlying unstable steady state, and focus on the mechanisms causing destabilisation of this steady state. This is motivated by the fact that all complex behaviours in unforced systems can be traced to destabilisation (bifurcation) of some steady state, and hence enables us to use tools from linear system theory to qualitatively analyse the sources of given network behaviours. One important objective of the present study is to see how far one can come with a relatively simple approach to the analysis of highly complex biochemical networks. The proposed method is demonstrated by application to a model of mitotic control in Xenopus frog eggs, and to a model of circadian oscillations in Drosophila. In both examples we are able to identify the subsystems, and the related interactions, which are instrumental in generating the observed complex non-linear behaviours.