Consistent Robustness Analysis (CRA) Identifies Biologically Relevant Properties of Regulatory Network Models

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values ( Sustainable Analysis ). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration ( Robust Analysis ). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters. In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values (Sustainable Analysis). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration (Robust Analysis). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters.In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness

Uncertainty considered in robust optimization is usually treated as irreducible since it is not reduced during an optimization procedure. In contrast, uncertainty considered in sensitivity analysis is treated as partially or fully reducible in order to quantify the effect of input uncertainty on the outputs of the system. Considering this, and the usual existence of both reducible and irreducible uncertainty, an approach that can perform robust optimization and sensitivity analysis simultaneously is of much interest. This article presents such an integrated optimization model that can be used for both robust optimization and sensitivity analysis for problems that have irreducible and reducible interval uncertainty, multiple objective functions and mixed continuous-discrete design variables. The proposed model is demonstrated by two engineering examples with differing complexity to demonstrate its applicability.

In this work, we develop methodologies for analyzing and cross comparing metabolic models. We investigate three important metabolic networks to discuss the complexity of biological organization of organisms, modeling, and system properties. In particular, we analyze these metabolic networks because of their biotechnological and basic science importance: the photosynthetic carbon metabolism in a general leaf, the Rhodobacter spheroides bacterium, and the Chlamydomonas reinhardtii alga. We adopt single- and multi-objective optimization algorithms to maximize the CO 2 uptake rate and the production of metabolites of industrial interest or for ecological purposes. We focus both on the level of genes (e.g., finding genetic manipulations to increase the production of one or more metabolites) and on finding concentration enzymes for improving the CO 2 consumption. We find that R. spheroides is able to absorb an amount of CO 2 until 57.452 mmol h (-1) gDW (-1) , while C. reinhardtii obtains a maximum of 6.7331. We report that the Pareto front analysis proves extremely useful to compare different organisms, as well as providing the possibility to investigate them with the same framework. By using the sensitivity and robustness analysis, our framework identifies the most sensitive and fragile components of the biological systems we take into account, allowing us to compare their models. We adopt the identifiability analysis to detect functional relations among enzymes; we observe that RuBisCO, GAPDH, and FBPase belong to the same functional group, as suggested also by the sensitivity analysis.

A review of sensitivity analysis methods in building energy analysis

A NOVEL approach to analyze the robustness of a flight control system (FCS) with respect to parametric uncertainties is presented, which specifically applies to gliding vehicles in the terminal phases of reentry flight. Robustness analyses are particularly challenging for these systems. Their reference trajectories are appreciably time-varying and encompass a broad variety of flight regimes. Furthermore, significant uncertainties on some critical design parameters affect the vehicle model, most notably those related to the aerodynamic behavior [1]. Current practice in FCS robustness analysis for this kind of application mainly relies on the theory of linear time-invariant (LTI) systems. In this approach, the original nonlinear system is linearized around a limited number of representative time-varying trajectories, including the nominal one. Then thewell-known frozentime approach [2] is applied, yielding multiple LTI models. In this way, classical stability margins [3] or more sophisticated LTI-based robustness criteria, such as analysis [4] and D-stability analyses [5], can be evaluated. Recently, a Lyapunov-based criterion coupled to interval analysis techniques [6] has been proposed for establishing robustness of a FCS. This approach does not resort to linearization of the system dynamics, but still requires the introduction of fictitious equilibrium points obtained by a frozen-time approach. Even if the flight experience demonstrated that frozen-time approaches are indeed operative, they are widely recognized as inefficient [7]. In fact, because the nominal trajectory may not be an equilibrium trajectory for the system in offnominal conditions, frozen-time analyses can provide only indicative, and often heavily conservative, results. To overcome such problems, further investigations are usually performed to identify a limited set of worst-case combinations of uncertain parameters to be used for FCS design refinement. In this case, nonlinear simulations in specific offnominal conditions, selected using sensitivity analysis and designer’s experience, represent the current practice. Optimization-based worst-case search has also been proposed [8], which may disclose the mutual effects of multiple uncertainties, but to a limited extent. In fact, the complexity of reentry dynamics under multiple uncertainties implies that actual worst cases relevant for FCS design refinement are difficult to identify. In any case, worst-case analysis can select only a limited number of test cases, hiding possible further causes of requirement violations, thus driving wrong refinement strategies that would not solve (or even worsen) FCS robustness problems. Monte Carlo (MC) analysis is, in practice, the only tool that is capable of investigating the combined effect of all uncertainties with a reasonable effort. However, being only a verification tool, when unsatisfactory robustness is discovered at this stage, the identification of its causes can require considerable postprocessing effort [9]. This yields one of the major limitations of this approach: that is, the limited support to the FCS design refinement when a requirement violation occurs due to poor robustness. As a result, in these cases, one is forced to iterate the design with scarce additional information. The present paper contributes toward advancing the current practice used in robustness analysis for FCS design refinement by introducing a method that takes into account nonlinear effects of multiple uncertainties over the whole trajectory, to be used before robustness is finally assessed withMC analysis. Themethod delivers feedback on the causes of requirement violation and adopts robustness criteria directly linked to the original mission or system requirements, such as those employed in MC analyses. The first objective is achieved estimating the region of requirement compliance in the space of the uncertain parameters. In this way, the approach provides an exhaustive coverage of the uncertainty’s effects on the FCS robustness. To translate mission requirements into robustness criteria over the whole trajectory, rather than at isolated points as in frozen-time approaches, we make use of the practical stability concept [10], which, to the authors’ knowledge, has never been applied to robustness analyses of atmospheric reentry vehicles.

In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout, which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organisms to maximise the production of key intermediate metabolites such as succinate and acetate. We obtain a vast set of Pareto optimal solutions; each of them represents an organism strain. For each solution, we evaluate the fragility by calculating three robustness indexes and by exploring reactions and metabolite interactions. Finally, we perform the Sensitivity Analysis of the metabolic model, which finds the inputs with the highest influence on the outputs of the model. We show that our methodology provides effective vision of the achievable synthetic strain landscape and a powerful design pipeline.KeywordsCell MetabolismBiological CADSensitive and Fragile PathwaysGenetic DesignMulti-Objective optimisationFlux balance analysisSensitivity and Robustness Analysis

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper addresses robust performance analysis problems of linear time-invariant (LTI) systems affected by real parametric uncertainties. These problems, known also as a special class of structured singular value computation problems, are inherently intractable (NP-hard problems). As such intensive research effort has been made to obtain computationally tractable and less conservative analysis conditions, where linear matrix inequality (LMI) plays an important. Nevertheless, since LMI-based conditions are expected to be conservative in general, it is often the case that we cannot conclude anything if the LMI at hand turns out to be infeasible. This motivates us to consider the dual of the LMI and examine the structure of the dual solution. By pursuing this direction, in this paper, we provide rank conditions on the dual solution matrix under which we can conclude that the underlying robust performance is never attained. In particular, a set of uncertain parameters that violates the specified performance can be computed. These results come from block-moment matrix structure of the dual variable, which is consistent with the recent results on polynomial optimization. This particular structure enables us to make good use of simultaneous diagonalizability property of commuting diagonalizable matrices so that the sound rank conditions for the exactness verification can be obtained. </para>

Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.

The robust Bayesian analysis was suggested to estimate the sector-averaged concentrations expected through the atmospheric dispersion of the airborne nuclides. The sector-averaged concentrations due to variation of effective height under F-stability were calculated by applying the plausible and contaminated priors according to the CSA model for Ulchin nuclear power units 3 and 4 of Korea. The median of the sector-averaged concentrations considerably agreed with the deterministic concentrations and was then suitable as the representative quantile of their distributions. Additionally, the medians of the concentrations reflecting the symmetric unimodal priors nearly agreed with those of the concentrations reflecting the plausible ones within 10% by downwind distance and the Bayesian inferences were, therefore, found to be sufficiently robust to reasonable variations in priors. From sensitivity analysis, horizontal dispersion coefficient was the highest effect in the atmospheric dispersion model due to the fanning condition under F-stability. In addition, the atmospheric dispersion model was less sensitive to all inputs, as the downwind distance increases to 5 km.

To satisfy the high reliability requirement of the battery charge regulator (BCR) of satellite electrical power systems (EPSs), a robust design and analysis method is proposed. First, a controller with outer voltage loop and inner current loop is designed based on the BCR mathematic model. Then, a BCR simulation model is established in the software, and the system performance with nominal design is obtained. Furthermore, considering temperature drifts and component aging, the sensitivity analysis, parametric analysis, statistical analysis, stress analysis, and failure modes analysis are presented. The results show that the BCR performance meets the design requirement. The reliability of system under space environments is ensured.

Emerging application of robust data envelopment analysis for optimization of graft copolymerization of poly(2-hydroxyethyl methacrylate) to Tamarindus indica seed polysaccharide

BackgroundClostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship.ResultsWe present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium’s metabolism, such as changes in the bacterium’s growth in response to different environmental conditions.ConclusionsAfter an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.

The purpose of this study was to perform a government-perspective economic analysis of the most widely used topical creams [namely, ciclopirox (CIC), clotrimazole (CLO), ketoconazole (KET), miconazole (MIC), and terbinafine (TER)], for the treatment of 2 types of dermatophyte skin infections: dermatophytosis major (excluding onychomycosis) and dermatophytosis minor. A 3-phase approach was used. In phase I, experts were assembled to identify the standard approach for management of fungal infections and a decision tree was constructed to model the process; in phase II, meta-analysis was used to determine success, failure, and relapse rates; and in phase III, economic analyses were performed including cost of regimen, total expected cost and cost-effectiveness analysis. Sensitivity analyses (robustness analyses) were also performed in phase III. It was found that while TER was successful following 1 week of administration for minor infections and after 2 weeks for major infections, duration of drug treatment was usually twice that time. Other comparators (CIC, CLO, KET and MIC) took 4 weeks to achieve a successful outcome. In addition, an extra 2 weeks were often needed to clear both types of infections because the comparators are fungistatic, whereas TER is fungicidal. In the total expected cost analysis, TER had the lowest overall cost of treating patients for both infection categories. It was also responsible for the highest number of disease-free days and, consequently, the lowest cost per disease-free day. Sensitivity analyses confirmed that TER was the most cost-effective topical agent for treating dermatophytosis major (excluding onychomycosis) and dermatophytosis minor.