Networks for systems biology: conceptual connection of data and function

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building

Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

PathExNET: A tool for extracting pathway expression networks from gene expression statistics

A fundamental issue related to the understanding of the molecular mechanisms, is the way in which common pathways act across different biological experiments related to complex diseases. Using network-based approaches, this work aims to provide a numeric characterization of pathways across different biological experiments, in the prospect to create unique footprints that may characterise a specific disease under study at a pathway network level. In this line we propose PathExNET, a web service that allows the creation of pathway-to-pathway expression networks that hold the over- and under expression information obtained from differential gene expression analyses. The unique numeric characterization of pathway expression status related to a specific biological experiment (or disease), as well as the creation of diverse combination of pathway networks generated by PathExNET, is expected to provide a concrete contribution towards the individualization of disease, and further lead to a more precise personalised medicine and management of treatment. PathExNET is available at: https://bioinformatics.cing.ac.cy/PathExNET and at https://pathexnet.cing-big.hpcf.cyi.ac.cy/.

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.

Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database

BACKGROUND

Ischemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM.

METHODS

We studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

From three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results.

CONCLUSIONS

The candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.

ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

Measuring the complexity of directed graphs: A polynomial-based approach

In this paper, we define novel graph measures for directed networks. The measures are based on graph polynomials utilizing the out- and in-degrees of directed graphs. Based on these polynomial, we define another polynomial and use their positive zeros as graph measures. The measures have meaningful properties that we investigate based on analytical and numerical results. As the computational complexity to compute the measures is polynomial, our approach is efficient and can be applied to large networks. We emphasize that our approach clearly complements the literature in this field as, to the best of our knowledge, existing complexity measures for directed graphs have never been applied on a large scale.

DOPSIE: Deep-Order Proximity and Structural Information Embedding

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms

The abundance of available information for each disease from multiple sources (e.g. as genetic, regulatory, metabolic, and protein-protein interaction) constitutes both an advantage and a challenge in identifying disease-specific underlying mechanisms. Integration of multi-source data is a rising topic and a great challenge in precision medicine and is crucial in enhancing disease understanding, identifying meaningful clusters of molecular mechanisms and increasing precision and personalisation towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The overall aim of this work was to develop a novel network-based integration methodology with the following characteristics: (i) maximise the number of data sources, (ii) utilise holistic approaches to integrate these sources (iii) be simple, flexible and extendable, (iv) be conclusive. Here, we present the case of Alzheimer's disease as a paradigm for illustrating our novel approach.

SIGNIFICANCE

In this work we present an integration methodology, which aggregates a large number of the available data sources and types by exploiting the holistic nature of network approaches. It is simple, flexible and extendable generating solid conclusions regarding the molecular mechanisms that underlie the input data. We have illustrated the strength of our proposed methodology using Alzheimer's disease as a paradigm. This method is expected to serve as a stepping-stone for further development of integration methods of multi-source omic-data and to contribute to progress towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The output of this methodology may act as a reference map of implicated pathways in the disease under investigation, where pathways related to additional omics data from any kind of experiment may be projected. This will increase the precision in the understanding of the disease and may contribute to personalised approaches for patients with different disease-related pathway profile, leading to a more precise, personalised and ideally preventive management of the disease.

Identification of potential genes for human ischemic cardiomyopathy based on RNA-Seq data

Ischemic cardiomyopathy (ICM) is an important cause of heart failure, yet no ICM disease genes were stored in any public databases. Mutations of genes provided by RNA-Seq data could set a foundation for a variety of biological processes. This also made it possible to elucidate the mechanism and identify potential genes for ICM. In this paper, an integrated co-expression network was constructed using univariate and bivariate canonical correlation analysis for RNA-Seq data of human ICM samples. Three ICM-related modules were recognized after comparing between Pearson correlation coefficients of ICM samples and normal controls. Furthermore, 32 ICM potential genes were identified from ICM-related modules considering protein-protein interactions. Most of these genes were verified to be involved in ICM and diseases caused it by OMIM and literature. Our study could provide a novel perspective for potential gene identification and the pathogenesis for ICM and other complex diseases.

A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks

The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.

Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model

Post-traumatic stress disorder (PTSD) is a debilitating illness that imposes significant emotional and financial burdens on military families. The understanding of PTSD etiology remains elusive; nonetheless, it is clear that PTSD is manifested by a cluster of symptoms including hyperarousal, reexperiencing of traumatic events, and avoidance of trauma reminders. With these characteristics in mind, several rodent models have been developed eliciting PTSD-like features. Animal models with social dimensions are of particular interest, since the social context plays a major role in the development and manifestation of PTSD.For civilians, a core trauma that elicits PTSD might be characterized by a singular life-threatening event such as a car accident. In contrast, among war veterans, PTSD might be triggered by repeated threats and a cumulative psychological burden that coalesced in the combat zone. In capturing this fundamental difference, the aggressor-exposed social stress (Agg-E SS) model imposes highly threatening conspecific trauma on naïve mice repeatedly and randomly.There is abundant evidence that suggests the potential role of genetic contributions to risk factors for PTSD. Specific observations include putatively heritable attributes of the disorder, the cited cases of atypical brain morphology, and the observed neuroendocrine shifts away from normative. Taken together, these features underscore the importance of multi-omics investigations to develop a comprehensive picture. More daunting will be the task of downstream analysis with integration of these heterogeneous genotypic and phenotypic data types to deliver putative clinical biomarkers. Researchers are advocating for a systems biology approach, which has demonstrated an increasingly robust potential for integrating multidisciplinary data. By applying a systems biology approach here, we have connected the tissue-specific molecular perturbations to the behaviors displayed by mice subjected to Agg-E SS. A molecular pattern that links the atypical fear plasticity to energy deficiency was thereby identified to be causally associated with many behavioral shifts and transformations.PTSD is a multifactorial illness sensitive to environmental influence. Accordingly, it is essential to employ the optimal animal model approximating the environmental condition that elicits PTSD-like symptoms. Integration of an optimal animal model with a systems biology approach can contribute to a more knowledge-driven and efficient next-generation care management system and, potentially, prevention of PTSD.

Pathway Analysis: State of the Art

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.

A Glimpse to Background and Characteristics of Major Molecular Biological Networks

Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.

DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks

Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Diseases such as cancer commonly arise due to changes in the dynamics of signalling and gene regulatory networks caused by genetic alternations. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. More efficient and reliable methods for multi-dimensional systems analysis and visualisation are thus required. To address this issue, we here present an integrated analysis and visualisation framework for high-dimensional network behaviour which exploits the advantages provided by parallel coordinates graphs. We demonstrate the applicability of the framework, named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC), to a variety of signalling networks ranging in topological wirings and dynamic properties. The framework was proved useful in acquiring an integrated understanding of systems behaviour.

Structural Analysis of Treatment Cycles Representing Transitions between Nursing Organizational Units Inferred from Diabetes

In this paper, we investigate treatment cycles inferred from diabetes data by means of graph theory. We define the term treatment cycles graph-theoretically and perform a descriptive as well as quantitative analysis thereof. Also, we interpret our findings in terms of nursing and clinical management.

PheNetic: network-based interpretation of molecular profiling data

Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.

A Boolean gene regulatory model of heterosis and speciation

BACKGROUND

Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. These artificial regulatory networks exhibit topological properties that reflect those observed in biology, and fitness is measured as the ability of a network to respond to external inputs in a pre-defined way.

RESULTS

Our model reproduced common experimental observations on heterosis using only biologically justified parameters, such as mutation rates. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. Thus, the model also describes a process akin to speciation due to genetic incompatibility of the separated populations. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis: dominance, over-dominance, and epistasis.

CONCLUSION

This study describes the most detailed simulation of heterosis using gene regulatory networks to date and reproduces several phenomena associated with heterosis for the first time in a model. The level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios.

Direct estimation of differential networks

It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modeled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Under the assumption that the true differential network is sparse, the direct estimator is shown to be consistent in support recovery and estimation. It is also shown to outperform existing methods in simulations, and its properties are illustrated on gene expression data from late-stage ovarian cancer patients.

Interrelations of graph distance measures based on topological indices

In this paper, we derive interrelations of graph distance measures by means of inequalities. For this investigation we are using graph distance measures based on topological indices that have not been studied in this context. Specifically, we are using the well-known Wiener index, Randić index, eigenvalue-based quantities and graph entropies. In addition to this analysis, we present results from numerical studies exploring various properties of the measures and aspects of their quality. Our results could find application in chemoinformatics and computational biology where the structural investigation of chemical components and gene networks is currently of great interest.

Genome scale modeling in systems biology: algorithms and resources

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.

Cancer systems biology and modeling: microscopic scale and multiscale approaches

Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks

In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

Integrating pathways of Parkinson's disease in a molecular interaction map

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .

B-cell lymphoma gene regulatory networks: biological consistency among inference methods

Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level—that are more important for our biological understanding—the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Quantitative trait loci-dependent analysis of a gene co-expression network associated with Fusarium head blight resistance in bread wheat (Triticum aestivum L.)

Background

Fusarium head blight (FHB) caused by Fusarium graminearum Schwabe is one of the most prevalent diseases of wheat (Triticum aestivum L.) and other small grain cereals. Resistance against the fungus is quantitative and more than 100 quantitative trait loci (QTL) have been described. Two well-validated and highly reproducible QTL, Fhb1 and Qfhs.ifa-5A have been widely investigated, but to date the underlying genes have not been identified.

Results

We have investigated a gene co-expression network activated in response to F. graminearum using RNA-seq data from near-isogenic lines, harboring either the resistant or the susceptible allele for Fhb1 and Qfhs.ifa-5A. The network identified pathogen-responsive modules, which were enriched for differentially expressed genes between genotypes or different time points after inoculation with the pathogen. Central gene analysis identified transcripts associated with either QTL within the network. Moreover, we present a detailed gene expression analysis of four gene families (glucanases, NBS-LRR, WRKY transcription factors and UDP-glycosyltransferases), which take prominent roles in the pathogen response.

Conclusions

A combination of a network-driven approach and differential gene expression analysis identified genes and pathways associated with Fhb1 and Qfhs.ifa-5A. We find G-protein coupled receptor kinases and biosynthesis genes for jasmonate and ethylene earlier induced for Fhb1. Similarly, we find genes involved in the biosynthesis and metabolism of riboflavin more abundant for Qfhs.ifa-5A.

Systems biology - the broader perspective

Systems biology has two general aims: a narrow one, which is to discover how complex networks of proteins work, and a broader one, which is to integrate the molecular and network data with the generation and function of organism phenotypes. Doing all this involves complex methodologies, but underpinning the subject are more general conceptual problems about upwards and downwards causality, complexity and information storage, and their solutions provide the constraints within which these methodologies can be used. This essay considers these general aspects and the particular role of protein networks; their functional outputs are often the processes driving phenotypic change and physiological function—networks are, in a sense, the units of systems biology much as proteins are for molecular biology. It goes on to argue that the natural language for systems-biological descriptions of biological phenomena is the mathematical graph (a set of connected facts of the general form <state 1> [process] <state 2> (e.g., <membrane-bound delta> [activates] <notch pathway>). Such graphs not only integrate events at different levels but emphasize the distributed nature of control as well as displaying a great deal of data. The implications and successes of these ideas for physiology, pharmacology, development and evolution are briefly considered. The paper concludes with some challenges for the future.

Network nursing: connections between nursing and complex network science

PURPOSE

Nursing processes are complex and include quite a few nonlinear interrelationships that are difficult to discover. Moreover, the formal description, visualization, and analysis of these processes are nontrivial challenges. The purpose of this paper is to establish the term network nursing as synonym for using quantitative graph theory in nursing science and to discuss how network nursing can be used for tackling such complex challenges in nursing science.

METHODS

In particular, methods from quantitative graph theory, divided into two major classes, comparative network analysis and network characterization, are employed to solve challenging problems in nursing.

FINDINGS

We demonstrate by way of example that this mathematical apparatus is feasible to tackle research questions when modeling and analyzing nursing processes. Here we use a "NANDA-I" showcase to illustrate how nursing networks can be derived from nursing data and how these networks can be used to compare different patients regarding their nursing diagnoses.

CONCLUSIONS

Nursing networks can be used to characterize patients from a nursing perspective. Especially, they allow a process-based view and are able to map relationships or dependencies. One major advantage of a networking approach is that it can be applied independently from the underlying nursing classifications or terminologies.

IMPLICATIONS FOR NURSING PRACTICE

Network nursing makes it possible to formally investigate the nursing process and thus opens up a so far little-known cosmos of possibilities and methods to expand nursing knowledge.

Interfacing cellular networks of S. cerevisiae and E. coli: connecting dynamic and genetic information

Background

In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored.

Results

We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes.

Conclusions

Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.

Structural properties and complexity of a new network class: Collatz step graphs

In this paper, we introduce a biologically inspired model to generate complex networks. In contrast to many other construction procedures for growing networks introduced so far, our method generates networks from one-dimensional symbol sequences that are related to the so called Collatz problem from number theory. The major purpose of the present paper is, first, to derive a symbol sequence from the Collatz problem, we call the step sequence, and investigate its structural properties. Second, we introduce a construction procedure for growing networks that is based on these step sequences. Third, we investigate the structural properties of this new network class including their finite scaling and asymptotic behavior of their complexity, average shortest path lengths and clustering coefficients. Interestingly, in contrast to many other network models including the small-world network from Watts & Strogatz, we find that CS graphs become ‘smaller’ with an increasing size.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.