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PubMed2004

Semi-sparse deconvolution robust to uncertainties in the impulse responses.

The received signal in ultrasonic pulse-echo inspection can be modeled as a convolution between an impulse response, or prototype, and the reflection sequence that is the impulse characteristic of the inspected object. Deconvolution aims at approximately inverting this process to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small. For the relatively high contrast reflection sequences often found in non-destructive testing applications, semi-sparse deconvolution algorithms will typically yield better results than the classical Wiener filter solution. However, this requires a prototype that is a good representative for all echo responses found in the signals. Since, in practice, the prototype is often chosen by the operator directly from the inspection data, the prototype may incidentally be a bad representative for modeling the echoes for the remaining part of the object. Because of the sensitivity to deviations in the prototype this can yield deconvolution results with poor reproducibility. This paper presents a new semi-sparse deconvolution algorithm that is robust to deviations in the prototype. The new robust algorithm is based on a modification of an earlier presented non-robust semi-sparse algorithm. The robustness is obtained by including a stochastic model of the variations in the prototypes to the signal model when deriving the algorithm. Experiments performed using simulated data verify that the robust algorithm is less sensitive to deviations in the prototypes compared to the non-robust version of the algorithm and show that the proposed algorithm yields better estimates than its non-robust version and the Wiener filter in scenarios for which the algorithm was derived. Results using real ultrasonic data further show that the algorithm can be useful in practical scenarios where similar deconvolution results are required from slightly different echoes.

Journal Article↗
PubMed2006

Robustness analysis of biochemical network models.

Biological systems that have been experimentally verified to be robust to significant changes in their environments require mathematical models that are themselves robust. In this context, a necessary condition for model robustness is that the model dynamics should not be sensitive to small variations in the model's parameters. Robustness analysis problems of this type have been extensively studied in the field of robust control theory and have been found to be very difficult to solve in general. The authors describe how some tools from robust control theory and nonlinear optimisation can be used to analyse the robustness of a recently proposed model of the molecular network underlying adenosine 3',5'-cyclic monophosphate (cAMP) oscillations observed in fields of chemotactic Dictyostelium cells. The network model, which consists of a system of seven coupled nonlinear differential equations, accurately reproduces the spontaneous oscillations in cAMP observed during the early development of D. discoideum. The analysis by the authors reveals, however, that very small variations in the model parameters can effectively destroy the required oscillatory dynamics. A biological interpretation of the analysis results is that correct functioning of a particular positive feedback loop in the proposed model is crucial to maintaining the required oscillatory dynamics.

Animals↗
PubMed2004

Robustness properties of circadian clock architectures.

Robustness, a relative insensitivity to perturbations, is a key characteristic of living cells. However, the specific structural characteristics that are responsible for robust performance are not clear, even in genetic circuits of moderate complexity. Formal sensitivity analysis allows the investigation of robustness and fragility properties of mathematical models representing regulatory networks, but it yields only local properties with respect to a particular choice of parameter values. Here, we show that by systematically investigating the parameter space, more global properties linked to network structure can be derived. Our analysis focuses on the genetic oscillator responsible for generating circadian rhythms in Drosophila as a prototypic dynamical cellular system. Analysis of two mathematical models of moderate complexity shows that the tradeoff between robustness and fragility is largely determined by the regulatory structure. Rank-ordered sensitivities, for instance, allow the correct identification of protein phosphorylation as an influential process determining the oscillator's period. Furthermore, sensitivity analysis confirms the theoretical insight that hierarchical control might be important for achieving robustness. The complex feedback structures encountered in vivo, however, do not seem to enhance robustness per se but confer robust precision and adjustability of the clock while avoiding catastrophic failure.

Animals↗
PubMed2005

A new measure of the robustness of biochemical networks.

MOTIVATION: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, we consider only the robustness measurement of a biochemical network at the steady state. RESULTS: We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance (robustness) of a biochemical network without testing many parametric perturbations. We find that a biochemical network with a large tolerance can also better attenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to be important mechanisms for the robustness of biochemical networks. Finally, four biochemical networks, such as a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid cycle in Dictyostelium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.

Animals↗
PubMed2003

Robust control of linear ceramic motor drive with LLCC resonant technique.

This study presents a robust control system for a linear ceramic motor (LCM) that is driven by a high-frequency voltage source inverter using two-inductance two-capacitance (LLCC) resonant technique. The structure and driving principle of the LCM are introduced. Because the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a robust control system is designed based on the hypothetical dynamic model to achieve high-precision position control. The presentation of robust control for the LCM drive system is divided into three parts, which comprise state feedback controller, feed-forward controller, and uncertainty controller. The adaptation laws of control gains in the robust control system are derived in the sense of Lyapunov stability theorem such that the stability of the control system can be guaranteed. It not only has the learning ability similar to intelligent control, but also its control framework is more simple than intelligent control. With the proposed robust control system, the controlled LCM drive possesses the advantages of good tracking control performance and robustness to uncertainties. The effectiveness of the proposed robust control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.

Journal Article↗
PubMed2002

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. RESULTS: Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. CONCLUSION: The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.

Biochemical Phenomena↗
PubMed2005

Robust detection of periodic time series measured from biological systems.

BACKGROUND: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. RESULTS: We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fisher's test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably. CONCLUSION: As the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method.

Algorithms↗
PubMed2006

Robust computational reconstitution - a new method for the comparative analysis of gene expression in tissues and isolated cell fractions.

BACKGROUND: Biological tissues consist of various cell types that differentially contribute to physiological and pathophysiological processes. Determining and analyzing cell type-specific gene expression under diverse conditions is therefore a central aim of biomedical research. The present study compares gene expression profiles in whole tissues and isolated cell fractions purified from these tissues in patients with rheumatoid arthritis and osteoarthritis. RESULTS: The expression profiles of the whole tissues were compared to computationally reconstituted expression profiles that combine the expression profiles of the isolated cell fractions (macrophages, fibroblasts, and non-adherent cells) according to their relative mRNA proportions in the tissue. The mRNA proportions were determined by trimmed robust regression using only the most robustly-expressed genes (1/3 to 1/2 of all measured genes), i.e. those showing the most similar expression in tissue and isolated cell fractions. The relative mRNA proportions were determined using several different chip evaluation methods, among which the MAS 5.0 signal algorithm appeared to be most robust. The computed mRNA proportions agreed well with the cell proportions determined by immunohistochemistry except for a minor number of outliers. Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics. CONCLUSION: Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions. This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.

Arthritis, Rheumatoid↗
PubMedDate not supplied

Activity, climate, and postcranial robusticity: implications for modern human origins and scenarios of adaptive change.

Postcranial robusticity--the massiveness of the skeleton--figures prominently in the debate over the origin of modern humans. Anthropologists use postcranial robusticity to infer the activity levels of prehistoric populations, and changes in robusticity are often used to support scenarios of adaptive change. These scenarios explain differences in morphology as the result of a change in lifestyle (habitual activity). One common scenario posits that early modern humans were more gracile than Neandertals because the modern humans' complex culture required less physical exertion. However, lifestyle is only one of many influences on morphology. Climate has clear correlations with physique and skeletal proportions. Analysis of recent humans that differ in terms of lifestyle and climatic adaptations reveals that limb bone robusticity varies with climate as much as or more than with lifestyle. Many of the differences in robusticity between Neandertals and early modern humans appear to be related to climatic adaptations. The results support the single-recent origin model of modern human origins. The differences in robusticity between Neandertals and early modern humans suggest that population replacement rather than local evolution best explains the emergence of modern humans in Europe. Both climatic adaptations (primarily body proportions) and lifestyle should be considered in analyses of robusticity.

Activities of Daily Living↗
PubMed2006

A robust design procedure for improvement of quality of lower-limb prosthesis.

Lower-limb prostheses are used to restore amputee's walking. Monolimb is one of the designs referring to socket and the shank being molded into one piece of thermoplastic material. Appropriate shank flexibility of a monolimb can improve gait of an amputee. However, during the fabrication, the variations of design variables are inevitably produced which may lead the unexpected shank deflection and directly influence on gait efficiency of an amputee. This paper presents a robust design procedure for improvement of quality of the monolimb by simultaneously minimizing performance variations caused by variations in design variables and bringing the mean value of performance on target. The robust design procedure embodies the integration of response surface methodology with genetic algorithms. Response surface models are developed for the responses of monolimb as functions of design variables over the region of interest and genetic algorithms are employed to find the robust solution. A robust design of monolimb is performed for an amputee subject and the results show that the robust design can design a "robust" monolimb which provides specified performance targets that are minimally sensitive to the variations of design variables. This indicates that robust design may have the potential application in improving the quality of the prescribed prosthesis.

Algorithms↗
PubMed2004

Locus of fragility in robust breast cancer system.

Functional heterogeneous redundancy of breast cancer makes this tumor to be robust. Signaling mechanisms which control cancer responses are crucial for controlling robustness. Identification of locus of fragility in cancer represents basic mechanism to target robustness. The goal of this prospect is to present locus of fragility in breast cancer robust system, and how disruption of this locus induces failure of robustness. My recent research show, that locus of fragility in breast cancer cells is suppression of nitric oxide (NO). When it was targeted, dynamics of cancer to generate robustness failed that it blocked cancer cell proliferation dependent on the NO/Rb pathway, blocked cell migration and angiogenesis dependent on the VEGF/PI3K/AKT/NO/ICAM-1 pathway, and induced breast cancer cell apoptosis through the NO/ROCK/FOXO3a signaling pathway. This tiny and trivial perturbation in breast cancer cells such as suppression of NO represents locus of fragility (weakness) and new approach for breast cancer chemotherapy.

Breast Neoplasms↗
PubMed2006

Robust Bayesian clustering.

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Algorithms↗
PubMed2006

Robustness and modular design of the Drosophila segment polarity network.

Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that whereas the vast majority of topologies can either not perform the required function or only do so very fragilely, a small fraction of topologies emerges as particularly robust for the function. The topology adopted by Drosophila, that of the segment polarity network, is a top ranking one among all topologies with no direct autoregulation. Furthermore, we found that all robust topologies are modular-each being a combination of three kinds of modules. These modules can be traced back to three subfunctions of the patterning function, and their combinations provide a combinatorial variability for the robust topologies. Our results suggest that the requirement of functional robustness drastically reduces the choices of viable topology to a limited set of modular combinations among which nature optimizes its choice under evolutionary and other biological constraints.

Animals↗
PubMed2004

Biological robustness.

Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

Adaptation, Biological↗
PubMed2006

Mechanisms of genetic robustness in RNA viruses.

Two key features of RNA viruses are their compacted genomes and their high mutation rate. Accordingly, deleterious mutations are common and have an enormous impact on viral fitness. In their multicellular hosts, robustness can be achieved by genomic redundancy, including gene duplication, diploidy, alternative metabolic pathways and biochemical buffering mechanisms. However, here we review evidence suggesting that during RNA virus evolution, alternative robustness mechanisms may have been selected. After briefly describing how genetic robustness can be quantified, we discuss mechanisms of intrinsic robustness arising as consequences of RNA-genome architecture, replication peculiarities and quasi-species population dynamics. These intrinsic robustness mechanisms operate efficiently at the population level, despite the mutational sensitivity shown by individual genomes. Finally, we discuss the possibility that viruses might exploit cellular buffering mechanisms for their own benefit, producing a sort of extrinsic robustness.

Evolution, Molecular↗
PubMed1998

Statistically robust approaches for sib-pair linkage analysis.

Many traits that distinguish one individual from another, such as height or weight, are clearly heritable and yet vary continuously in populations. Continuous, heritable variation in trait levels presumably reflects the segregation of multiple genes, but elucidation of the genetic architecture of quantitative traits has been limited. Haseman & Elston (1972) developed a genetically robust method (HE) for detecting linkage to quantitative trait loci using sib-pairs. The method is based on a simple linear regression of the squared sib-pairs trait difference on the proportion of alleles shared identical by descent at a marker locus. Linkage is detected by a negative slope which has been traditionally assessed by a standard t-test. Wan, Cohen & Guerra (1997) have shown that the standard t-test is robust to the violations of the stochastic assumptions underlying the test. In practice, however, the standard t-test, based on least-squares regression, is sensitive to outliers. The presence of outliers in the data can lead to false positive and false negative linkage results. Accordingly we have developed and evaluated a statistically robust procedure for the HE approach to linkage. The procedure is based on robust regression. Simulation studies show that this robust procedure has greater power than the standard t-test in the presence of outliers, and has similar power to the standard t-test in the absence of outliers. This robust procedure also shows greater power than rank-based approaches either in the absence or presence of outliers. To illustrate the methods using real data we reanalyse data from two lipoprotein systems that motivated this work.

Adult↗
PubMed2005

Analysing the robustness of cellular rhythms.

Autonomous oscillations at the cellular level are important for various timing and signalling functions. The rhythms depend on environmental influences in a specific manner. In particular, the period of some rhythms has been shown to be very robust to certain environmental factors whereas other rhythms show a high sensitivity towards these factors. It is discussed that the robustness of the systems towards environmental changes results from underlying design principles. However, a comparison of robustness properties of different rhythms is lacking. Here we analyse the sensitivity of the oscillatory period with respect to parameter variations in models describing oscillations in calcium signalling, glycolysis and the circadian system. By comparing models for the same and different rhythms it is shown that the sensitivity depends on the oscillatory mechanism rather than the details of the model description. In particular, we find models of calcium oscillations to be very sensitive, those for glycolytic oscillations intermediately sensitive and models for circadian rhythms very robust. The results are discussed with respect to the temperature dependency of the rhythms. The question of what impact design principles have on the robustness of an oscillator, is addressed more explicitly by a direct comparison of systems with positive and negative feedback regulation for various reaction chain lengths. We find that the systems with negative feedback are more robust than corresponding systems with positive feedback. An increase in the length of the reaction chain under regulation leads to a decrease in sensitivity.

Algorithms↗
PubMedDate not supplied

The importance of robustness in drinking-water systems.

It is important that drinking-water systems be as robust as possible. That is, they should deliver excellent quality water under adverse conditions. Robustness is important for each of the five elements that can be considered necessary for providing safe drinking water (a good source, adequate treatment, secure distribution, appropriate monitoring, and appropriate response to adverse monitoring results). However, a given degree of overall system robustness can be achieved in varying ways. The quantification of robustness facilitates its improvement in a rational way. This paper introduces the concept of robustness, and illustrates one way in which it could be quantified by means of an example involving filtration in relation to Cryptosporidium removal. With regard to a serious water contamination incident that occurred in Canada during May 2000, the robustness of each of the five elements (source, treatment, distribution, monitoring, and response) is assessed qualitatively to explain the overall vulnerability of the water-supply system in that town.

Canada↗