systems models
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Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper we provide a critical review of dynamical systems models of BD. Owing to heterogeneity of methodologies and experimental designs in computational modeling, we designed a structured approach to guide our review in a fashion that parallels the appraisal of animal models by their Face, Predictive, and Construct Validity. This tool, the Validity Appraisal Guide for Computational Models (VAG-CM) is not an absolute estimate of validity, but rather a guide for more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to included studies, obtaining a mean Cohen's kappa of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future models should be developed using a hybrid approach that first operationalizes BD features of interest using empirical data (a data-driven approach), followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD (a theory-driven approach).

Systems epidemiology offers a more comprehensive and holistic approach to studies of cancer in populations by considering high dimensionality measures from multiple domains, assessing the inter-relationships among risk factors, and considering changes over time. These approaches offer a framework to account for the complexity of cancer and contribute to a broader understanding of the disease. Therefore, NCI sponsored a workshop in February 2019 to facilitate discussion about the opportunities and challenges of the application of systems epidemiology approaches for cancer research. Eight key themes emerged from the discussion: transdisciplinary collaboration and a problem-based approach; methods and modeling considerations; interpretation, validation, and evaluation of models; data needs and opportunities; sharing of data and models; enhanced training practices; dissemination of systems models; and building a systems epidemiology community. This manuscript summarizes these themes, highlights opportunities for cancer systems epidemiology research, outlines ways to foster this research area, and introduces a collection of papers, “Cancer System Epidemiology Insights and Future Opportunities” that highlight findings based on systems epidemiology approaches.

Despite the importance of microbial dysbiosis in human disease, the phenomenon remains poorly understood. We provide the first comprehensive and predictive model of dysbiosis at ecosystem-scale, leveraging our new machine learning method for efficiently inferring compact and interpretable dynamical systems models. Coupling this approach with the most densely temporally sampled interventional study of the microbiome to date, using microbiota from healthy and dysbiotic human donors that we transplanted into mice subjected to antibiotic and dietary interventions, we demonstrate superior predictive performance of our method over state-of-the-art techniques. Moreover, we demonstrate that our approach uncovers intrinsic dynamical properties of dysbiosis driven by destabilizing competitive cycles, in contrast to stabilizing interaction chains in the healthy microbiome, which have implications for restoration of the microbiome to treat disease.

AbstractTo cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.

ABSTRACTAn improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a gateway portal. This portal connects a broad-spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease and drug treatment. These atlases are provided as independent and integrated queriable datasets, with an emphasis on dynamic visualization, figure generation and reference-based classification of user-provided datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, data portals and datasets from LungMAP and external research efforts.

This article assesses the management tools of electric systems in developing countries. To promote the nuclear energy as an alternative resource in developing countries, the International Atomic Energy Agency (I.A.E.A.) has encouraged the development of a decision-making tool which optimizes operational activities in electric systems. Since 1982, most developing countries use that tool or a derivative one; recurrent management difficulties are encountered despite the numerous studies and the efforts to enhance the management performances. Activities are the main levers in the management of electric systems. They are set to improve the performances which translate the efficiencies. In dealing with the difficulties, none of the previous studies has considered the tool assessment. Monte Carlo or probabilistic or derating simulation methods are used in the tool Implementation; simulations are only relevant if models are realistic and the systems properly analysed. We show in this paper indirectly from the WASP notebook guide A (I.A.E.A.) 1984 and directly from the evolution of electrical system’s characteristics that the electric systems models are not suitable in developing countries. An unrealistic model stands for a system significantly different from the actual one; the differences lead to reference quantity value management difficulties and to the impossibility to set up the infinitesimal evolution responsible for the overall behaviour of the system and underlies the implementation of the tool. The article puts the constraints and associated activities at the centre of electrical system management, it opens prospects for efficiency management with a significant impact on upcoming electric systems.

This article assesses the management tools of electric systems in developing countries. To promote the nuclear energy as an alternative resource in developing countries, the International Atomic Energy Agency (I.A.E.A.) has encouraged the development of a decision-making tool which optimizes operational activities in electric systems. Since 1982, most developing countries use that tool or a derivative one; recurrent management difficulties are encountered despite the numerous studies and the efforts to enhance the management performances. Activities are the main levers in the management of electric systems. They are set to improve the performances which translate the efficiencies. In dealing with the difficulties, none of the previous studies has considered the tool assessment. Monte Carlo or probabilistic or derating simulation methods are used in the tool Implementation; simulations are only relevant if models are realistic and the systems properly analysed. We show in this paper indirectly from the WASP notebook guide A (I.A.E.A.) 1984 and directly from the evolution of electrical system’s characteristics that the electric systems models are not suitable in developing countries. An unrealistic model stands for a system significantly different from the actual one; the differences lead to reference quantity value management difficulties and to the impossibility to set up the infinitesimal evolution responsible for the overall behaviour of the system and underlies the implementation of the tool. The article puts the constraints and associated activities at the centre of electrical system management, it opens prospects for efficiency management with a significant impact on upcoming electric systems.