Validation and invalidation of systems biology models using robustness analysis

Robustness, the ability of a system to function correctly in the presence of both internal and external uncertainty, has emerged as a key organising principle in many biological systems. Biological robustness has thus become a major focus of research in Systems Biology, particularly on the engineering-biology interface, since the concept of robustness was first rigorously defined in the context of engineering control systems. This review focuses on one particularly important aspect of robustness in Systems Biology, that is, the use of robustness analysis methods for the validation or invalidation of models of biological systems. With the explosive growth in quantitative modelling brought about by Systems Biology, the problem of validating, invalidating and discriminating between competing models of a biological system has become an increasingly important one. In this review, the authors provide a comprehensive overview of the tools and methods that are available for this task, and illustrate the wide range of biological systems to which this approach has been successfully applied.     

Validation of reliability computational models using Bayes networks

This paper proposes a methodology based on Bayesian statistics to assess the validity of reliability computational models when full-scale testing is not possible. Sub-module validation results are used to derive a validation measure for the overall reliability estimate. Bayes networks are used for the propagation and updating of validation information from the sub-modules to the overall model prediction. The methodology includes uncertainty in the experimental measurement, and the posterior and prior distributions of the model output are used to compute a validation metric based on Bayesian hypothesis testing. Validation of a reliability prediction model for an engine blade under high-cycle fatigue is illustrated using the proposed methodology.     

实测数据对大气颗粒物浓度计算模式的验证

针对常用的大气颗粒物(PM10、PM2.5)浓度计算模式计算结果偏差较大的问题,由太原市城区例行监测的PM10浓度估算出燃煤电厂导致的大气颗粒物(PM10、PM2.5)浓度推算值,使用AERMOD和CALPUFF模式计算燃煤电厂排放源导致的大气颗粒物浓度,对大气颗粒物浓度的计算值与实测值进行了比较分析与评估。结果表明:在城市区域,性能较佳的大气颗粒物浓度计算模式是AERMOD模式。     

Mechanical system reliability analysis using a combination of graph theory and Boolean function

A new method based on graph theory and Boolean function for assessing reliability of mechanical systems is proposed. The procedure for this approach consists of two parts. By using the graph theory, the formula for the reliability of a mechanical system that considers the interrelations of subsystems or components is generated. Use of the Boolean function to examine the failure interactions of two particular elements of the system, followed with demonstrations of how to incorporate such failure dependencies into the analysis of larger systems, a constructive algorithm for quantifying the genuine interconnections between the subsystems or components is provided. The combination of graph theory and Boolean function provides an effective way to evaluate the reliability of a large, complex mechanical system. A numerical example demonstrates that this method an effective approaches in system reliability analysis.     

Validation of models with multivariate output

This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading.     

The mutually beneficial relationship of patents and scientific literature: topic evolution in nanoscience

Patent and scientific literature are the fundamental sources of innovation in knowledge creation and transfer activities. Establishing and understanding the complex relationships between them can help scientists and stakeholders to predict and promote the innovation process. In this paper, we consider the domain of nanoscience, using a large scale collection of patents and scientific literature to find evolution patterns and distinctive keywords of each topic in a particular period. By extracting the semantic-level topics from a dataset of nearly 810,000 scientific literature from Web of Science and 160,000 patents from Derwent, the results reveal that the degree of topic popularity for both innovative platforms shows a totally different situation during the last 20 years from 1995 to 2015. In addition, the top keywords of patents and scientific literature, representing the topic content of concern, have changed respectively as time went on. Not only our analysis can be used for confirming existing topics in nanoscience, but it also gives new views on the relationship between scientific literature and patents.     

Stable and accurate method for modal analysis of multilayer waveguides using a graph approach

A method for calculating the propagation constants of allowed guided and leaky modes in multilayer planar waveguides is presented. We develop a two-way graph model to describe the tangential fields propagating in the waveguides. According to the special structure of the graph model, it is convenient to employ a topology scheme to derive analytical and closed-form dispersion equations for TE and TM modes. By comparing the dispersion equations formulated by series-expansion methods, approximation methods, and transfer-matrix methods, we find that the use of these equations for finding the eigenmodes has some benefits. First, this method can be easily employed to solve eigenmodes accurately in numerical computation without using series truncation. Second, the dispersion equations are exact. Moreover, all the eigenmodes can be determined according to the formulas without losing roots or causing numerical instability even for a waveguide with thick layers.     

Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec

The combination of the topic model and the semantic method can help to discover the semantic distributions of topics and the changing characteristics of the semantic distributions, further providing a new perspective for the research of topic evolution. This study proposes a solution for quantifying the semantic distributions and the changing characteristics based on words in topic evolution through the Dynamic topic model (DTM) and the word2vec model. A dataset in the field of Library and information science (LIS) is utilized in the empirical study, and the topic-semantic probability distribution is derived. The evolving dynamics of the topics are constructed. The characteristics of evolving dynamics are used to explain the semantic distributions of topics in topic evolution. Then, the regularities of evolving dynamics are summarized to explain the changing characteristics of semantic distributions in topic evolution. Results show that no topic is distributed in a single semantic concept, and most topics correspond to various semantic concepts in LIS. The three kinds of topics in LIS are the convergent, diffusive, and stable topics. The discovery of different modes of topic evolution can further prove the development of the field. In addition, findings indicate that the popularity of topics and the characteristics of evolving dynamics of topics are irrelevant.

     

Performance analysis of flexible workcentres using multiqueue models

We construct a multiqueue model of high fidelity for detailed performance evaluation of flexible machine centres. These models permit one to represent part type specific demand, processing, and setup time values, and to obtain part type specific performance figures. We show through numerical examples that variabilities in system parameters can induce paradoxical system behaviour. We also present an enhanced model which concerns a workpiece undergoing several operations at the work centre, and its application to evaluate alternative schedul ing strategies.     

A multi-view method of scientific paper classification via heterogeneous graph embeddings

The classification task of scientific papers can be implemented based on contents or citations. In order to improve the performance on this task, we express papers as nodes and integrate scientific papers’ contents and citations into a heterogeneous graph. It has two types of edges. One type represents the semantic similarity between papers, derived from papers’ titles and abstracts. The other type represents the citation relationship between papers and the journals or proceedings of conferences of their references. We utilize a contrastive learning method to embed the nodes in the heterogeneous graph into a vector space. Then, we feed the paper node vectors into classifiers, such as the decision tree, multilayer perceptron, and so on. We conduct experiments on three datasets of scientific papers: the Microsoft Academic Graph with 63,211 scientific papers in 20 classes, the Proceedings of the National Academy of Sciences with 38,243 scientific papers in 18 classes, and the American Physical Society with 443,845 scientific papers in 5 classes. The experimental results on the multi-class task show that our multi-view method scores the classification accuracy up to 98%, outperforming state-of-the-arts.

     

Validation of two models for discharge rate

A substantial body of discharge rate data has been developed over the past half century applicable for validation of single and two-phase discharge models. This paper applies a wide range of test cases and compares predictions with test data for two types of discharge model: (a) the energy balance model, and (b) the non-equilibrium model of Diener and Schmidt. The latter enhances the original homogeneous equilibrium model of Leung. This exercise reveals possible inconsistency between experimental datasets as much as it provides confirmation of the accuracy of the models, but both models are shown to provide adequate predictions within a factor of two and generally better.     

A structural topic model approach to scientific reorientation of economics and chemistry after German reunification

The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter.

     

Citation impact prediction for scientific papers using stepwise regression analysis

Researchers typically pay greater attention to scientific papers published within the last 2 years, and especially papers that may have great citation impact in the future. However, the accuracy of current citation impact prediction methods is still not satisfactory. This paper argues that objective features of scientific papers can make citation impact prediction relatively accurate. The external features of a paper, features of authors, features of the journal of publication, and features of citations are all considered in constructing a paper’s feature space. The stepwise multiple regression analysis is used to select appropriate features from the space and to build a regression model for explaining the relationship between citation impact and the chosen features. The validity of this model is also experimentally verified in the subject area of Information Science & Library Science. The results show that the regression model is effective within this subject.     

Improved group theoretic method using graph products for the analysis of symmetric-regular structures

In this paper, a new combined graph-group method is proposed for eigensolution of special graphs. Symmetric regular graphs are the subject of this study. Many structural models can be viewed as the product of two or three simple graphs. Such models are called regular, and usually have symmetric configurations. The proposed method of this paper performs the symmetry analysis of the entire structure via symmetric properties of its simple generators. Here, a graph is considered as the general model of an arbitrary structure. The Laplacian matrix, as one of the most important matrices associated with a graph, is studied in this paper. The characteristic problem of this matrix is investigated using symmetry analysis via group theory enriched by graph theory. The method is developed and decomposition of the Laplacian matrix of such graphs is studied in a step-by-step manner, based on the proposed method. This method focuses on simple paths which generate large networks, and finds the eigenvalues of the network via analysis of the simple generators. Group theory is the main tool, which is improved using the concept of graph products. As a mechanical application of the method, a benchmark problem of group theory in structural mechanics is studied in this paper. Vibration of cable nets is analyzed and the frequencies of the networks are calculated using the combined graph-group method.     

The analysis of dynamic fracture using lumped mass-spring models

It is shown that many dynamic effects in fracture can be usefully modelled using lumped mass-spring models of the fracturing speciemens, since many effects are not a consequence of stress waves but of the dynamic behaviour of the specimen and loading system. When the energy release rate, G, is calculated for specimens with harmonic motions present, it is found that multiple solutions occur which, if analysed using quasi-static calibration factors, can give apparent scatter and limiting speeds in G _c . This simple approach can be used to interpret several types of experimental data.

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Résumé On montre que de nombreux effets dynamiques en rupture peuvent être utilement modélisés en ayant recours à des masses et des ressorts pour simuler des éprouvettes en cours de rupture, dans la mesure où plusieurs de ces effets ne résultent pas des ondes de contraintes, mais plutôt du comportement dynamique de l'éprouvette et de son système de charge.Lorsque l'on calcule la vitesse G de relaxation de l'énergie pour des éprouvettes où sont présents des mouvements parmoniques, on trouve que se présente une multitude de solutions qui, lorsqu'on les analyse en utilisant des facteurs d'étalonnage quasi-statiques, peuvent donner une apparente dispersion et des vitesses limites pour Gc.Cette approche simple est utilisable pour l'interprétation de divers types de données expérimentales.

     

Validation of biokinetic models of Cs and I using their daily dietary intakes and organ contents in an adult Indian population

The daily dietary intakes and total contents of Cs and I in skeletal muscle and thyroid, respectively, for an average Indian adult were estimated by determining their concentrations in the total cooked diet and tissue samples using instrumental neutron activation analysis. These data were then used to predict the biological half-lives of Cs and I and their radioactive counterparts for the Reference Indian Man. The predicted biological half-lives of Cs and I for Reference Indian Man were found to be comparable with the reported values for the ICRP Reference Man.     

Numerical analysis of composite systems by using interphase/interface models

The paper considers two classes of approaches for the numerical analysis of composite systems: the first one discretizes the assumed interphase (between matrix and fibre) as volumic elements and uses material models that degenerate from Continuum Damage Mechanics. The second one introduces interface elements that relate non linearly the normal and tangential tractions to the corresponding displacement discontinuities, incorporating a progressive decohesion, following the lines of Needleman (1987) and Tvergaard (1990). The respective capabilities of these two approaches are discussed on the basis of some numerical results obtained for a unidirectional metal matrix composite system. When the models are consistently adjusted they are able to reproduce the same kind of results. The advantages of the second class of method is underlined and two new versions of interface models are proposed that guarantee the continuity and the monotonicity of the shear stiffness between the progressive decohesion phase and the subsequent contact/friction law that plays role under compressive shear after complete separation.     

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

Flexible non-linear regression techniques have been widely used for data-based modeling of chemical processes, and they form the basis of process design under the framework of response surface methodology (RSM). These non-linear models typically achieve more accurate approximation to the factor-response relationship than traditional polynomial regressions. However, non-linear models usually lack a clear interpretation as to how the factors contribute to the prediction of process response.This paper applies the technique of sensitivity analysis (SA) to facilitate the interpretation of non-linear process models. By recognizing that derivative-based local SA is only valid within the neighborhood of certain “nominal” values, global SA is adopted to study the entire range of the factors. Global SA is based on the decomposition of the model and the variance of response into contributing terms of main effects and interactions. Therefore, the effect of individual factors and their interactions can be both visualized by graphs and quantified by sensitivity indices. The proposed methodology is demonstrated on two catalysis processes where non-linear data-based models have been developed to aid process design. The results indicate that global SA is a powerful tool to reveal the impact of process factors on the response variables.