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Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface. [ABSTRACT FROM AUTHOR]

Abstract The Organic Grid is a biologically inspired and fully decentralized approach to the organization of computation that is based on the autonomous scheduling of strongly mobile agents on a […]

Desktop grids have been used to perform some of the largest computations in the world and have the potential to grow by several more orders of magnitude. However, current approaches to utilizing desktop resources require either centralized servers or extensive knowledge of the underlying system, limiting their scalability. We propose a new design for desktop grids that relies on a self-organizing, fully decentralized approach to the organization of the computation. Our approach, called the organic grid, is a radical departure from current approaches and is modeled after the way complex biological systems organize themselves. Similar to current desktop grids, a large computational task is broken down into sufficiently small subtasks. Each subtask is encapsulated into a mobile agent, which is then released on the grid and discovers computational resources using autonomous behavior. In the process of "colonization" of available resources, the judicious design of the agent behavior produces the emergence of crucial properties of the computation that can be tailored to specific classes of applications. We demonstrate this concept with a reduced-scale proof-of-concept implementation that executes a data-intensive independent-task application on a set of heterogeneous, geographically distributed machines. We present a detailed exploration of the design space of our system and a performance evaluation of our implementation using metrics appropriate for assessing self-organizing desktop grids. Index Terms--Grid scheduling, mobile agents, self-organizing computation.

This paper focuses on the inverse problem of predicting inputs from measured outputs in the context of linear systems in steady-state. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can likely improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. Topics for further research are also suggested in this paper. [ABSTRACT FROM AUTHOR]

Byline: Manoj Pillai, Mario Lauria A known issue in the design of striped file systems is their vulnerability to disk failures. In this paper, we address the challenges of augmenting an existing striped file system with traditional RAID redundancy, and propose a novel redundancy scheme designed to maximise disk throughput seen by applications. We implement our new scheme in CSAR, a proof-of-concept redundant file system based on the parallel virtual file system, along with two other well-known schemes. Our tests using both microbenchmarks and representative scientific applications show that our scheme consistently performs as well as the best of the other two schemes. The application-dependent, potentially larger storage occupation of our scheme is justified by current technological trends that put I/O bandwidth at a premium over disk space.

Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome. [ABSTRACT FROM AUTHOR]

This paper proposes methods for forward and inverse system modeling using Bayesian and least squares regression. These methods are based on both space-filling design criteria for multiple response problems and linear optimality criteria focusing on D-optimality. Modeling with and without the constant term is considered motivated by the case study application of genetic network modeling. We propose extended one-factor-at-a-time experimentation followed by augmentation of next stage design which offers biologists simplicity. Results are illustrated both numerical examples, a test problem from the literature, and a case study motivated by an real world biological research related to genetic network modeling. [ABSTRACT FROM PUBLISHER]

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data. [ABSTRACT FROM AUTHOR]

The next generation of biomarkers and companion diagnostics will require the development of technologies capable of conjugating the advances in high-throughput techniques in biology with computational methods. Systems biology is poised to contribute through an integrated view, capturing the complexity of the system, both in terms of a collection of interacting molecular components and also in terms of multiple intersecting views. Following this system-centered view, novel approaches have been developed for the identification of signatures of both disease processes and drug modes of action with the promising perspectives of better diagnosis of disease and of the discovery of more efficacious and safe drugs. The application of systems biology to the development of companion diagnostics is very recent and to date a few pioneering steps have been made in this direction. In this review, we describe the ongoing studies and the potential developments in this area of research. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-β deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer’s disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes) were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer’s disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer’s disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy. [ABSTRACT FROM AUTHOR]

Stem-cell functions require activation of stem-cell-intrinsic transcriptional programs and extracellular interaction with a niche microenvironment. How the transcriptional machinery controls residency of stem cells in the niche is unknown. Here we show that Id proteins coordinate stem-cell activities with anchorage of neural stem cells (NSCs) to the niche. Conditional inactivation of three Id genes in NSCs triggered detachment of embryonic and postnatal NSCs from the ventricular and vascular niche, respectively. The interrogation of the gene modules directly targeted by Id deletion in NSCs revealed that Id proteins repress bHLH-mediated activation of Rap1GAP, thus serving to maintain the GTPase activity of RAP1, a key mediator of cell adhesion. Preventing the elevation of the Rap1GAP level countered the consequences of Id loss on NSC-niche interaction and stem-cell identity. Thus, by preserving anchorage of NSCs to the extracellular environment, Id activity synchronizes NSC functions to residency in the specialized niche. [ABSTRACT FROM AUTHOR]

Motivation: Sequence alignment is one of the most popular tools of modern biology. NCBI's PSI-BLAST utilizes iterative model building in order to better detect distant homologs with greater sensitivity than non-iterative BLAST. However, PSI-BLAST's performance is limited by the fact that it relies on deterministic alignments. Using a semi-probabilistic alignment scheme such as Hybrid alignment should allow for better informed model building and improved identification of homologous sequences, particularly remote homologs.Results: We have built a new version of the tool in which the Smith-Waterman alignment algorithm core is replaced by the hybrid alignment algorithm. The favorable statistical properties of the hybrid algorithm allow the introduction of position-specific gap penalties in Hybrid PSI-BLAST. This improves the position-specific modeling of protein families and results in an overall improvement of performance.Availability: Source code is freely available for download at http://bioserv.mps.ohio-state.edu/HybridPSI, implemented in C and supported on linux.Contact: bundschuh@mps.ohio-state.eduSupplementary information:Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based solely on high-throughput gene expression data. The algorithm can infer (i) gene–gene interactions from steady state expression profiles and (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify both information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one. [ABSTRACT FROM AUTHOR]

Summary The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. Availability and implementation The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo. [ABSTRACT FROM AUTHOR]

Progressive sequence model refinement by means of iterative searches is an effective technique for high sensitivity database searches and is currently employed in popular tools such as PSI-BLAST and SAM. Recently, a novel alignment algorithm has been proposed that offers features expected to improve the sensitivity of such iterative approaches, specifically a well-characterized theory of its statistics even in the presence of position-specific gap costs. Here, we demonstrate that the new hybrid alignment algorithm is ready to be used as the alignment core of PSI-BLAST. In addition, we evaluate the accuracy of two proposed approaches to edge effect correction in short sequence alignment statistics that turns out to be one of the crucial issues in developing a hybrid-alignment based version of PSI-BLAST. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Simple Summary: Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature. High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. [ABSTRACT FROM AUTHOR]

The Negr1 gene has been significantly associated with major depression in genetic studies. Negr1 encodes for a cell adhesion molecule cleaved by the protease Adam10, thus activating Fgfr2 and promoting neuronal spine plasticity. We investigated whether antidepressants modulate the expression of genes belonging to Negr1-Fgfr2 pathway in Flinders sensitive line (FSL) rats, in a corticosterone-treated mouse model of depression, and in mouse primary neurons. Negr1 and Adam10 were the genes mostly affected by antidepressant treatment, and in opposite directions. Negr1 was down-regulated by escitalopram in the hypothalamus of FSL rats, by fluoxetine in the hippocampal dentate gyrus of corticosterone-treated mice, and by nortriptyline in hippocampal primary neurons. Adam10 mRNA was increased by nortriptyline administration in the hypothalamus, by escitalopram in the hippocampus of FSL rats, and by fluoxetine in mouse dorsal dentate gyrus. Similarly, nortriptyline increased Adam10 expression in hippocampal cultures. Fgfr2 expression was increased by nortriptyline in the hypothalamus of FSL rats and in hippocampal neurons. Lsamp, another IgLON family protein, increased in mouse dentate gyrus after fluoxetine treatment. These findings suggest that Negr1-Fgfr2 pathway plays a role in the modulation of synaptic plasticity induced by antidepressant treatment to promote therapeutic efficacy by rearranging connectivity in corticolimbic circuits impaired in depression. [ABSTRACT FROM AUTHOR]

Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different. [ABSTRACT FROM AUTHOR]

We describe a new signature definition and analysis method to be used as biomarker for early cancer detection. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and cancer affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published datasets of circulating miRNA, and we quantify its performance compared to current state-of-the-art methods. A number of additional features make this method an ideal candidate for large-scale use, for example, as a mass screening tool for early cancer detection or for at-home diagnostics. Specifically, our method is minimally invasive (because it works well with circulating miRNA), it is robust with respect to lab-to-lab protocol variability and batch effects (it requires that only the relative ranking of expression value of miRNA in a profile be accurate not their absolute values), and it is scalable to a large number of subjects. Finally we discuss the need for HPC capability in a widespread application of our or similar methods. [ABSTRACT FROM AUTHOR]