Systems Biology Data Research Articles

Statistical inference of the time-varying structure of gene-regulation networks

BackgroundBiological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.MethodsTo overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).ResultsWe demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.ConclusionsARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.

Integration and visualization of systems biology data in context of the genome

BackgroundHigh-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment.ResultsThe Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data.A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome.ConclusionsGenomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

GTC: A web server for integrating systems biology data with web tools and desktop applications

Gaggle Tool Creator (GTC) is a web application which provides access to public annotation, interaction, orthology, and genomic data for hundreds of organisms, and enables instant analysis of the data using many popular web-based and desktop applications.

A multiway approach to data integration in systems biology based on Tucker3 and N-PLS

This paper discusses the potential of multi-way projection methods for analysing multifactorial data structures to identify underlying components of variability that interconnect different blocks of omics variables. We explore their suitability for explorative and variable selection analysis of systems biology data where different types of biological parameters are studied together. These methodologies were applied to the integrative analysis of a functional genomics dataset where transcriptomics, metabolomics and physiological data are available. Our results show that multiway methods are suited to accommodate multifactorial omics experiments and to analyse relationships between different biochemical layers. Additionally, strategies are presented for variable selection in the context of omics data and for interpreting results at the level of cellular pathways.

SNPRanker: a tool for identification and scoring of SNPs associated to target genes.

The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker . An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator

BackgroundMany current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions.MethodologyWe aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model.Conclusions/SignificanceOur method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. “who regulates who” (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.

Visualization of omics data for systems biology

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.

SBRML: a markup language for associating systems biology data with models

Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

Ultra-Structure database design methodology for managing systems biology data and analyses

BackgroundModern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the system's capabilities without programmer intervention, simply by altering database contents; no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping).ResultsWe transitioned our proteogenomic mapping information system from a traditional entity-relationship design to one based on Ultra-Structure. Our system integrates tandem mass spectrum data, genomic annotation sets, and spectrum/peptide mappings, all within a small, general framework implemented within a standard relational database system. General software procedures driven by user-modifiable rules can perform tasks such as logical deduction and location-based computations. The system is not tied specifically to proteogenomic research, but is rather designed to accommodate virtually any kind of biological research.ConclusionWe find Ultra-Structure offers substantial benefits for biological information systems, the largest being the integration of diverse information sources into a common framework. This facilitates systems biology research by integrating data from disparate high-throughput techniques. It also enables us to readily incorporate new data types, sources, and domain knowledge with no change to the database structure or associated computer code. Ultra-Structure may be a significant step towards solving the hard problem of data management and integration in the systems biology era.

Ultra-Structure database design methodology for managing systems biology data and analyses

Ultra-Structure database design methodology for managing systems biology data and analyses

Personalized cancer diagnostics and therapeutics

Dimitrios H Roukos studied Medicine at the University of Athens, Greece, and specialized in General Surgery at the JW Goethe-University Hospital in Frankfurt (1983–1989), Germany, where he received the title Dr med. Since 1990, Roukos has worked at Ioannina University School of Medicine. His current research activity includes the integration of both standard clinical and treatment data, and genetics/personal genomics/protein interactions/signaling pathways network data to predict a genotypic (QTLs) and phenotypic (cancer diversity) map. He also works to integrate systems biology, in silico, bioinformatics and reverse engineering data to achieve personalized cancer biomedicine. In the past he has published innovative models, algorithms, concepts and invited papers (including editorials and perspectives). He has also participated in keynote plenary presentations and lectures (Quest speaker international meetings). He has acheived over 1500 citations, a Hirsch factor of 28 and a total impact factor of over 600.

Modelling of DARPP-32 Regulation to Understand Intracellular Signaling in Psychiatric Disease

The majority of psychopharmaca acts by binding to G-protein coupled receptors and thereby asserts it's action through the regulation of intracellular signaling networks. The convergence and interactions of pathways within these networks make the detailed signaling hard to study experimentally, and the response to a stimuli can be non-intuitive. To approach these problems with systems biology and merging biochemical data in a computer model to do virtual experiments with high time-resolution can shed new light on the functioning of these networks. The phosphoprotein DARPP-32 is regulated by several modulatory neurotransmitters, including dopamine, serotonin and adenosin, and it's function has been proposed to be altered in schizophrenia. Moreover, the well studied regulation of DARPP-32, and the vast amount of biochemical data makes it a model molecule when it comes to intracellular signaling. To better understand the interactions of the pathways that regulate DARPP-32 activation we constructed a computer model based on experimental data. In this work we discovered unexpected responses of DARPP-32 at fast timescales. An equally important outcome of the work was to identify areas where additional work is needed in order to understand intracellular signaling at the systems level, showing the need for close collaborations between theoretical and experimental biologists.

A systems biology analysis of protein–protein interactions in the APOBEC family

Aims The APOBEC (apolipoprotein B mRNA-editing catalytic polypeptide) family of cytidine deaminases inhibits the mobility of diverse retroviruses, retrotransposons and other viruses. This group of apolipoproteins is widely distributed in living organisms and plays a central role in diverse enzymatic pathways. Nevertheless, the interplay between APOBECs and innate immune proteins, as well as the role of APOBECs in protecting the host cell from viral infection are poorly understood. To elucidate the association between human APOBECs and immune system, a systems biology study was performed to identify various proteins involved in the function of APOBEC proteins. Main methods This identification utilized an integrated database and literature network of protein–protein interactions combined with nine microarray experiments. Key findings Considering our systems biology data, we can infer some modes of action of APOBECs through interactions with proteins associated with the immune system. Significance This study presents a comprehensive analysis of the APOBEC network, highlighting those proteins that have a higher probability of playing an important role with APOBECs in the innate immune system.

Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data.

The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

SHARKview: a tool for the visualization of systems biology data

SHARKview: a tool for the visualization of systems biology data

Peak alignment using restricted edit distances

A peak is a pair of real values ( x , y ) , where x is the time when peak of height y is registered. In the peak alignment problem, we are given two sequences of peaks, and our task is to align the sequences allowing some basic edit operations on the peaks. We study an instance of the peak alignment problem that arises in the analysis of Mass Spectrometry data in Systems Biology. There the measurement technique guarantees that two peaks ( x , y ) , ( x ′ , y ′ ) can only be considered the same if x is close enough to x ′ , and y is close enough to y ′ . We review some methods to do alignment under such restrictions on matches.

Visualization of complementary systems biology data with parallel heatmaps

The interpretation of large-scale biological data can be aided by the use of appropriate visualization tools. Heatmaps--pattern-revealing aggregate views of data--have emerged as a preferred technique for the display of genomics data, since they provide an extra dimension of information in a two-dimensional display. However, an increasing focus on the integration of data from multiple sources has created a need for the display of additional dimensions. To improve the identification of relationships between co-expressed genes identified in microarray experiments, a parallel dataset heatmap viewer has been developed for four-dimensional data display. The flexible data entry structure of the parallel heatmap viewer facilitates the display of both continuous and discrete data. Specific examples are presented for the analysis of diverse functional genomics yeast data related to gene regulation, expression, and annotation. The parallel heatmap viewer enables knowledgeable life science researchers to observe patterns and properties within high-throughput genomics data in order to rapidly identify biologically logical relationships.

Graph data management for molecular and cell biology

As high-throughput biology begins to generate large volumes of systems biology data, the need grows for robust, efficient database systems to support investigations of metabolic and signaling pathways, chemical reaction networks, gene regulatory networks, and protein interaction networks. Network data is frequently represented as graphs, and researchers need to navigate, query and manipulate this data in ways that are not well supported by standard relational database management systems (RDBMSs). Current approaches to managing graphs in an RDBMS rely on either external procedural logic to execute the graph algorithms or clumsy and inefficient algorithms implemented in Structured Query Language (SQL). In this paper we describe the Systems Biology Graph Extender, a research prototype that extends the IBM RDBMS—DB2® Universal Database software—with graph objects and operations to support declarative SQL queries over biological networks and other graph structures. Supported operations include neighborhood queries, shortest path queries, spanning trees, graph transposition, and graph matching. In a federated database environment, graph operations may be applied to data stored in any format, whether remote or local, relational or nonrelational. A single federated query may include both graph-based predicates and predicates over related data sources, such as microarray expression levels, clinical prognosis and outcome, or the function of orthologous proteins (i.e., proteins that are evolutionarily related to those in another species) in mouse disease models.

The Gaggle: An open-source software system for integrating bioinformatics software and data sources

BackgroundSystems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.ResultsIn this paper we describe the Gaggle -a simple, open-source Java software environment that helps to solve the problem of software and database integration. Guided by the classic software engineering strategy of separation of concerns and a policy of semantic flexibility, it integrates existing popular programs and web resources into a user-friendly, easily-extended environment.We demonstrate that four simple data types (names, matrices, networks, and associative arrays) are sufficient to bring together diverse databases and software. We highlight some capabilities of the Gaggle with an exploration of Helicobacter pylori pathogenesis genes, in which we identify a putative ricin-like protein -a discovery made possible by simultaneous data exploration using a wide range of publicly available data and a variety of popular bioinformatics software tools.ConclusionWe have integrated diverse databases (for example, KEGG, BioCyc, String) and software (Cytoscape, DataMatrixViewer, R statistical environment, and TIGR Microarray Expression Viewer). Through this loose coupling of diverse software and databases the Gaggle enables simultaneous exploration of experimental data (mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations (operon, chromosomal proximity, phylogenetic pattern), metabolic pathways (KEGG) and Pubmed abstracts (STRING web resource), creating an exploratory environment useful to 'web browser and spreadsheet biologists', to statistically savvy computational biologists, and those in between. The Gaggle uses Java RMI and Java Web Start technologies and can be found at .