Biological impacts and context of network theory

Implications of the solvent vehicles dimethylformamide and dimethylsulfoxide for establishing transcriptomic endpoints in the zebrafish embryo toxicity test

Current aquatic chemical testing guidelines recognize that solvents can potentially interfere with the organism or environmental conditions of aquatic ecotoxicity tests and therefore recommend concentration limits for their use. These recommendations are based on evidence of adverse solvent effects in apical level tests. The growing importance of subapical and chronic endpoints in future test strategies, however, suggests that the limits may need reassessment. To address this concern, microarrays were used to determine the effects of organic solvents, dimethylformamide (DMF) and dimethylsulfoxide (DMSO), on the transcriptome of zebrafish (Danio rerio) embryos. Embryos were exposed for 48 h to a range of concentrations between 0.025 and 32.0 ml/L. Effects on survival and development after 24 and 48 h were assessed microscopically, with no effects on mortality or morphology up to 2.0 and 16.0 ml/L for DMF and DMSO. However, analysis of 48-h embryonic RNA revealed large numbers of differentially expressed genes at concentrations well below the 0.1 ml/L solvent limit level. The enrichment of differentially expressed genes was found for metabolic, developmental, and other key biological processes, some of which could be linked to observed morphological effects at higher solvent concentrations. These findings emphasize the need to remove or lower as far as possible the concentrations of solvent carriers in ecotoxicology tests.

Markov Chain Ontology Analysis (MCOA)

Background

Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.

Results

In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods.

Conclusion

A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.

Complex network analysis in microbial systems: theory and examples

An essential idea in the area of Systems Biology is that a good understanding of interactions between components is crucial for developing deep knowledge of the functioning of the system as a whole. Network analysis is an approach uniquely suited to uncover patterns and organizing principles in a wide variety of complex systems. In this chapter, we will give a detailed description of central network concepts and their algorithmic implementation, and demonstrate how they may be applied on two biological networks: the protein-interaction network of Mus musculus and the reconstructed genome-scale metabolic network of the bacterium Yersinia pestis.

Hive plots--rational approach to visualizing networks

Networks are typically visualized with force-based or spectral layouts. These algorithms lack reproducibility and perceptual uniformity because they do not use a node coordinate system. The layouts can be difficult to interpret and are unsuitable for assessing differences in networks. To address these issues, we introduce hive plots (http://www.hiveplot.com) for generating informative, quantitative and comparable network layouts. Hive plots depict network structure transparently, are simple to understand and can be easily tuned to identify patterns of interest. The method is computationally straightforward, scales well and is amenable to a plugin for existing tools.

The metabolic core and catalytic switches are fundamental elements in the self-regulation of the systemic metabolic structure of cells

BACKGROUND

Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms.

METHODOLOGY/PRINCIPAL FINDINGS

In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows.

CONCLUSIONS/SIGNIFICANCE

We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub--with a high degree of effective connectivity--exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.

Disease embryo development network reveals the relationship between disease genes and embryo development genes

A basic problem for contemporary biology and medicine is exploring the correlation between human disease and underlying cellular mechanisms. For a long time, several efforts were made to reveal the similarity between embryo development and disease process, but few from the system level. In this article, we used the human protein-protein interactions (PPIs), disease genes with their classifications and embryo development genes and reconstructed a human disease-embryo development network to investigate the relationship between disease genes and embryo development genes. We found that disease genes and embryo development genes are prone to connect with each other. Furthermore, diseases can be categorized into three groups according to the closeness with embryo development in gene overlapping, interacting pattern in PPI network and co-regulated by microRNAs or transcription factors. Embryo development high-related disease genes show their closeness with embryo development at least in three biological levels. But it is not for embryo development medium-related disease genes and embryo development low-related disease genes. We also found that embryo development high-related disease genes are more central than other disease genes in the human PPI network. In addition, the results show that embryo development high-related disease genes tend to be essential genes compared with other diseases' genes. This network-based approach could provide evidence for the intricate correlation between disease process and embryo development, and help to uncover potential mechanisms of human complex diseases.

Socioeconomic status and bone health in community-dwelling older men: the CHAMP Study

SUMMARY

The association between socioeconomic status (SES) and bone health, specifically in men, is unclear. Based upon data from the large prospective Concord Health in Ageing Men Project (CHAMP) Study of community-dwelling men aged 70 years or over, we found that specific sub-characteristics of SES, namely, marital status, living circumstances, and acculturation, reflected bone health in older Australian men.

INTRODUCTION

Previous studies reported conflicting results regarding the relationship between SES and bone health, specifically in men. The main objective of this study was to investigate associations of SES with bone health in community-dwelling men aged 70 years or over who participated in the baseline phase of the CHAMP Study in Sydney, Australia.

METHODS

The Australian Socioeconomic Index 2006 (AUSEI06) based on the Australian and New Zealand Standard Classification of Occupations was used to determine SES in 1,705 men. Bone mineral density and bone mineral content (BMC) were determined by dual-energy X-ray absorptiometry. Bone-related biochemical and hormonal parameters, including markers of bone turnover, parathyroid hormone, and vitamin D, were measured in all men.

RESULTS

General linear models adjusted for age, weight, height, and bone area revealed no significant differences across crude AUSEI06 score quintiles for BMC at any skeletal site or for any of the bone-related biochemical measures. However, multivariate regression models revealed that in Australian-born men, marital status was a predictor of higher lumbar BMC (β = 0.07, p = 0.002), higher total body BMC (β = 0.05, p = 0.03), and lower urinary NTX-I levels (β=-0.08, p = 0.03), while living alone was associated with lower BMC at the lumbar spine (β=-0.05, p = 0.04) and higher urinary NTX-I levels (β=0.07, p = 0.04). Marital status was also a predictor of higher total body BMC (β = 0.14, p = 0.003) in immigrants from Eastern and South Eastern Europe. However, in immigrants from Southern Europe, living alone and acculturation were predictors of higher femoral neck BMC (β = 0.11, p = 0.03) and lumbar spine BMC (β = 0.10, p = 0.008), respectively.

CONCLUSIONS

Although crude occupation-based SES scores were not significantly associated with bone health in older Australian men, specific sub-characteristics of SES, namely, marital status, living circumstances, and acculturation, were predictors of bone health in both Australia-born men and European immigrants.

Networks in plant epidemiology: from genes to landscapes, countries, and continents

There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of super-connected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pathosystem (Phytophthora ramorum) where a theoretical network approach has proven particularly fruitful in analyzing the spread of disease in the UK plant trade. These studies can help in assessing the future threat posed by similar emerging pathogens. Networks have much potential in plant epidemiology and should become part of the standard curriculum.

Epidermal patterning in Arabidopsis: models make a difference

The leaf and root epidermis in Arabidopsis provide ideal systems in which to explore the mechanisms that underlie the patterned assignment of cell fates during development. Extensive experimental studies have uncovered a complex interlocked feedback network that operates within the epidermis to coordinate the choice between hair and nonhair fates. A number of recent studies using mathematical models have begun to study this network, highlighting new mechanisms that have subsequently been confirmed in model-directed experiments. These studies illustrate the potential of integrated modeling and experimentation to shed new light on developmental processes. Moreover, these models enable systems-level comparative analyses that may help understand the origin and role of properties, such as robustness and redundancy in developmental systems and, concomitantly, the evolution of development itself.

Is my network module preserved and reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.

In network applications, one is often interested in studying whether modules are preserved across multiple networks. For example, to determine whether a pathway of genes is perturbed in a certain condition, one can study whether its connectivity pattern is no longer preserved. Non-preserved modules can either be biologically uninteresting (e.g., reflecting data outliers) or interesting (e.g., reflecting sex specific modules). An intuitive approach for studying module preservation is to cross-tabulate module membership. But this approach often cannot address questions about the preservation of connectivity patterns between nodes. Thus, cross-tabulation based approaches often fail to recognize that important aspects of a network module are preserved. Cross-tabulation methods make it difficult to argue that a module is not preserved. The weak statement (“the reference module does not overlap with any of the identified test set modules”) is less relevant in practice than the strong statement (“the module cannot be found in the test network irrespective of the parameter settings of the module detection procedure”). Module preservation statistics have important applications, e.g. we show that the wiring of apoptosis genes in a human cortical network differs from that in chimpanzees.

A network model for plant-pollinator community assembly

Community assembly models, usually constructed for food webs, are an important component of our understanding of how ecological communities are formed. However, models for mutualistic community assembly are still needed, especially because these communities are experiencing significant anthropogenic disturbances that affect their biodiversity. Here, we present a unique network model that simulates the colonization and extinction process of mutualistic community assembly. We generate regional source pools of species interaction networks on the basis of statistical properties reported in the literature. We develop a dynamic synchronous Boolean framework to simulate, with few free parameters, the dynamics of new mutualistic community formation from the regional source pool. This approach allows us to deterministically map out every possible trajectory of community formation. This level of detail is rarely observed in other analytic approaches and allows for thorough analysis of the dynamical properties of community formation. As for food web assembly, we find that the number of stable communities is quite low, and the composition of the source pool influences the abundance and nature of community outcomes. However, in contrast to food web assembly, stable mutualistic communities form rapidly. Small communities with minor fluctuations in species presence/absence (self-similar limit cycles) are the most common community outcome. The unique application of this Boolean network approach to the study of mutualistic community assembly offers a great opportunity to improve our understanding of these critical communities.

Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data

Complex network analyses of functional connectivity have consistently revealed non-random (modular, small-world, scale-free-like) behavior of hard-thresholded networks constructed from the right-tail of the similarity histogram. In the present study we determined network properties resulting from edges constrained to specific ranges across the full correlation histogram, in particular the left (negative-most) tail, and their dependence on the confound signal removal strategy employed. In the absence of global signal correction, left-tail networks comprised predominantly long range connections associated with weak correlations and were characterized by substantially reduced modularity and clustering, negative assortativity and γ<1 Deconvolution of specific confound signals (white matter, CSF and motion) resulted in the most robust within-subject reproducibility of global network parameters (ICCs~0.5). Global signal removal altered the network topology in the left tail, with the clustering coefficient and assortativity converging to zero. Networks constructed from the absolute value of the correlation coefficient were thus compromised following global signal removal since the different right-tail and left-tail topologies were mixed. These findings informed the construction of soft-thresholded networks, replacing the hard thresholding or binarization operation with a continuous mapping of all correlation values to edge weights, suppressing rather than removing weaker connections and avoiding issues related to network fragmentation. A power law adjacency function with β=12 yielded modular networks whose parameters agreed well with corresponding hard-thresholded values, that were reproducible in repeated sessions across many months and evidenced small-world-like and scale-free-like properties.

Quantitative analysis of cellular metabolic dissipative, self-organized structures

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life.

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

BACKGROUND

Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.

RESULTS

In this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation.

CONCLUSION

This semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications.

A survey of cancer cell lines reveals highly structured and hierarchical relationships within and between DNA and mRNA that may be the result of selection

Copy number variation (CNV) is one of the most profound forms of somatic DNA changes that underlie most human cancers. However, the degree of complexity within and between DNA and mRNA variations in cancer cohorts has yet to be fully characterized. Here we characterized the connectivity of CNV/CNV and its contribution to transcriptome in human cancer cell lines. Strikingly, we found there is a significant nonrandom correlation of many unlinked DNA loci and also a significant association between CNV and mRNA expression in cis and in trans (called eCNV). Both distributions of DNA/DNA and DNA/mRNA associations exhibit a scale-free structure showing that, for DNA/DNA, a few loci correlate to many other loci, whereas most loci correlate to only a few loci; and for DNA/mRNA, certain chromosomal loci associate with many mRNAs and that many mRNAs are controlled by more than one locus. This suggests that a small number of DNA loci act as hubs in a hierarchical structure that is highly nonrandom in nature, and genes linking to these hot spots tend to be involved in similar biological functions. Derivation of highly connected structures suggests a process of undirected copy number changes followed by selection of those advantageous to tumor cells during tumorigenesis. Given that the cohort includes many tissue types, our observations may identify a common and important underlying structure present in human tumors.

Identification of modules in Aspergillus niger by gene co-expression network analysis

The fungus Aspergillus niger has been studied in considerable detail with respect to various industrial applications. Although its central metabolic pathways are established relatively well, the mechanisms that control the adaptation of its metabolism are understood rather poorly. In this study, clustering of co-expressed genes has been performed on the basis of DNA microarray data sets from two experimental approaches. In one approach, low amounts of inducer caused a relatively mild perturbation, while in the other approach the imposed environmental conditions including carbon source starvation caused severe perturbed stress. A set of conserved genes was used to construct gene co-expression networks for both the individual and combined data sets. Comparative analysis revealed the existence of modules, some of which are present in all three networks. In addition, experimental condition-specific modules were identified. Module-derived consensus expression profiles enabled the integration of all protein-coding A. niger genes to the co-expression analysis, including hypothetical and poorly conserved genes. Conserved sequence motifs were detected in the upstream region of genes that cluster in some modules, e.g., the binding site for the amino acid metabolism-related transcription factor CpcA as well as for the fatty acid metabolism-related transcription factors, FarA and FarB. Moreover, not previously described putative transcription factor binding sites were discovered for two modules: the motif 5'-CGACAA is overrepresented in the module containing genes encoding cytosolic ribosomal proteins, while the motif 5'-GGCCGCG is overrepresented in genes related to 'gene expression', such as RNA helicases and translation initiation factors.

Global self-regulation of the cellular metabolic structure

BACKGROUND

Different studies have shown that cellular enzymatic activities are able to self-organize spontaneously, forming a metabolic core of reactive processes that remain active under different growth conditions while the rest of the molecular catalytic reactions exhibit structural plasticity. This global cellular metabolic structure appears to be an intrinsic characteristic common to all cellular organisms. Recent work performed with dissipative metabolic networks has shown that the fundamental element for the spontaneous emergence of this global self-organized enzymatic structure could be the number of catalytic elements in the metabolic networks.

METHODOLOGY/PRINCIPAL FINDINGS

In order to investigate the factors that may affect the catalytic dynamics under a global metabolic structure characterized by the presence of metabolic cores we have studied different transitions in catalytic patterns belonging to a dissipative metabolic network. The data were analyzed using non-linear dynamics tools: power spectra, reconstructed attractors, long-term correlations, maximum Lyapunov exponent and Approximate Entropy; and we have found the emergence of self-regulation phenomena during the transitions in the metabolic activities.

CONCLUSIONS/SIGNIFICANCE

The analysis has also shown that the chaotic numerical series analyzed correspond to the fractional Brownian motion and they exhibit long-term correlations and low Approximate Entropy indicating a high level of predictability and information during the self-regulation of the metabolic transitions. The results illustrate some aspects of the mechanisms behind the emergence of the metabolic self-regulation processes, which may constitute an important property of the global structure of the cellular metabolism.

Desmosomal interactome in keratinocytes: a systems biology approach leading to an understanding of the pathogenesis of skin disease

We provide the first description of the desmosome network in keratinocytes using a systems level approach. The desmo-adhesome consists of 59 proteins connected by 128 direct interactions and forms different functional subnets. Whilst the structure appears to be extremely robust against random perturbations, network fragmentation analysis suggests that the desmo-adhesome is susceptible to targeted attacks. To confirm this prediction, we applied this model to the autoimmune disease Pemphigus Vulgaris (PV), a paradigm of external perturbation of the desmosome. Our analysis showed that the adaptor protein plakophilin (Pkp) 3 was in the highest percentile group for both connectivity rate and gene expression changes in experimental PV. This observation led us to speculate that Pkp3 was crucial in desmosomal remodelling, and therefore we designed the experiments to verify this hypothesis. Our data demonstrate that, whilst Pkp3 is important in conferring adhesive strength to keratinocytes, it also acts as a central molecule mediating cell-cell detachment induced by PV IgG.

The number of catalytic elements is crucial for the emergence of metabolic cores

Background

Different studies show evidence that several unicellular organisms display a cellular metabolic structure characterized by a set of enzymes which are always in an active state (metabolic core), while the rest of the molecular catalytic reactions exhibit on-off changing states. This self-organized enzymatic configuration seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. In a recent analysis performed with dissipative metabolic networks (DMNs) we have shown that this global functional structure emerges in metabolic networks with a relatively high number of catalytic elements, under particular conditions of enzymatic covalent regulatory activity.

Methodology/Principal Findings

Here, to investigate the mechanism behind the emergence of this supramolecular organization of enzymes, we have performed extensive DMNs simulations (around 15,210,000 networks) taking into account the proportion of the allosterically regulated enzymes and covalent enzymes present in the networks, the variation in the number of substrate fluxes and regulatory signals per catalytic element, as well as the random selection of the catalytic elements that receive substrate fluxes from the exterior. The numerical approximations obtained show that the percentages of DMNs with metabolic cores grow with the number of catalytic elements, converging to 100% for all cases.

Conclusions/Significance

The results show evidence that the fundamental factor for the spontaneous emergence of this global self-organized enzymatic structure is the number of catalytic elements in the metabolic networks. Our analysis corroborates and expands on our previous studies illustrating a crucial property of the global structure of the cellular metabolism. These results also offer important insights into the mechanisms which ensure the robustness and stability of living cells.

The use of network analyses for elucidating mechanisms in cardiovascular disease

Systems biology offers the potential to provide new insights into our understanding of the pathogenesis of complex diseases such as atherosclerosis. It seeks to comprehend the system properties of the non-linear interactions of the multiple biomolecular components that characterize a living organism. An important component of this research approach is identifying the biological networks that connect the differing elements of a system and in the process describe the characteristics that define a shift in equilibrium from a healthy to a diseased state. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies such as inflammatory-related diseases, herein exemplified by cardiovascular disease. In this study, the application of network theory to systems biology is described in detail and an example is provided using data from a clinical biobank database of carotid endarterectomies from the Karolinska University Hospital (Biobank of Karolinska Endarterectomies, BiKE). Data from 47 microarrays were examined using a combination of Bioconductor modules and the Cytoscape resource with several associated plugins to analyze the transcriptomics data and create a combined gene association and correlation network of atherosclerosis. The methodology and workflow are described in detail, with a total of 43 genes found to be differentially expressed on a gender-specific basis, of which 15 were not directly linked to the sex chromosomes. In particular, the APOC1 gene was 2.1-fold down-regulated in plaques in women relative to men and was selected for further analysis based upon a purported role in cardiovascular disease. The resulting network was identified as a scale-free network that contained specific sub-networks related to immune function and lipid biosynthesis. These sub-networks link atherosclerotic-related genes to other genes that may not have previously known roles in disease etiology and only evidence small alterations, which are challenging to find by statistical and comparison-based methods. A number of Gene Ontology (GO), BioCarta and KEGG pathways involved in the atherosclerotic process were identified in the constructed sub-network, with 19 GO pathways related to APOC1 of which 'phospholipid efflux' evidenced the strongest association. The utility and functionality of network analysis and the different Cytoscape plugins employed are discussed. Lastly, the applications of these methods to cardiovascular disease are discussed with focus on the current limitations and future visions of this emerging field.

Integrins as "functional hubs" in the regulation of pathological angiogenesis

It is well accepted that complex biological processes such as angiogenesis are not controlled by a single family of molecules or individually isolated signaling pathways. In this regard, new insight into the interconnected mechanisms that regulate angiogenesis might be gained by examining this process from a more global network perspective. The coordination of signaling cues from both outside and inside many different cell types is required for the successful completion of angiogenesis. Evidence is accumulating that the multifunctional integrin family of cell adhesion receptors represent an important group of molecules that play active roles in sensing, integrating, and distributing a diverse set of signals that regulate many cellular events required for angiogenesis. Given the ability of integrins to bind numerous extracellular ligands and transmit signals in a bi-directional fashion, we will discuss the multiple ways by which integrins may serve as a functional hub during pathological angiogenesis. In addition, we will highlight potential imaging and therapeutic strategies based on the expanding new insight into integrin function.

Information flow during gene activation by signaling molecules: ethylene transduction in Arabidopsis cells as a study system

BACKGROUND

We study root cells from the model plant Arabidopsis thaliana and the communication channel conformed by the ethylene signal transduction pathway. A basic equation taken from our previous work relates the probability of expression of the gene ERF1 to the concentration of ethylene.

RESULTS

The above equation is used to compute the Shannon entropy (H) or degree of uncertainty that the genetic machinery has during the decoding of the message encoded by the ethylene specific receptors embedded in the endoplasmic reticulum membrane and transmitted into the nucleus by the ethylene signaling pathway. We show that the amount of information associated with the expression of the master gene ERF1 (Ethylene Response Factor 1) can be computed. Then we examine the system response to sinusoidal input signals with varying frequencies to determine if the cell can distinguish between different regimes of information flow from the environment. Our results demonstrate that the amount of information managed by the root cell can be correlated with the frequency of the input signal.

CONCLUSION

The ethylene signaling pathway cuts off very low and very high frequencies, allowing a window of frequency response in which the nucleus reads the incoming message as a sinusoidal input. Out of this window the nucleus reads the input message as an approximately non-varying one. From this frequency response analysis we estimate: a) the gain of the system during the synthesis of the protein ERF1 (approximately -5.6 dB); b) the rate of information transfer (0.003 bits) during the transport of each new ERF1 molecule into the nucleus and c) the time of synthesis of each new ERF1 molecule (approximately 21.3 s). Finally, we demonstrate that in the case of the system of a single master gene (ERF1) and a single slave gene (HLS1), the total Shannon entropy is completely determined by the uncertainty associated with the expression of the master gene. A second proposition shows that the Shannon entropy associated with the expression of the HLS1 gene determines the information content of the system that is related to the interaction of the antagonistic genes ARF1, 2 and HLS1.

An evolving model of undirected networks based on microscopic biological interaction systems

With protein or gene interaction systems as the background, this paper proposes an evolving model of biological undirected networks, which are consistent with some plausible mechanisms in biology. Through introducing a rule of preferential duplication of a node inversely proportional to the degree of existing nodes and an attribute of the age of the node (the older, the more influence), by which the probability of a node receiving re-wiring links is chosen, the model networks generated in certain parameter conditions could reproduce series of statistic topological characteristics of real biological graphs, including the scale-free feature, small world effect, hierarchical modularity, limited structural robustness, and disassortativity of degree-degree correlation.

The impact of cellular networks on disease comorbidity

The impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects. By combining information on cellular interactions, disease--gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population. Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

BACKGROUND

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.

RESULTS

Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined.

CONCLUSION

Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene-encoded proteins are attached to the core at more peripheral positions of the networks.

The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

BACKGROUND

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

RESULTS

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

CONCLUSION

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.

Interlinked nonlinear subnetworks underlie the formation of robust cellular patterns in Arabidopsis epidermis: a dynamic spatial model

Background

Dynamical models are instrumental for exploring the way information required to generate robust developmental patterns arises from complex interactions among genetic and non-genetic factors. We address this fundamental issue of developmental biology studying the leaf and root epidermis of Arabidopsis. We propose an experimentally-grounded model of gene regulatory networks (GRNs) that are coupled by protein diffusion and comprise a meta-GRN implemented on cellularised domains.

Results

Steady states of the meta-GRN model correspond to gene expression profiles typical of hair and non-hair epidermal cells. The simulations also render spatial patterns that match the cellular arrangements observed in root and leaf epidermis. As in actual plants, such patterns are robust in the face of diverse perturbations. We validated the model by checking that it also reproduced the patterns of reported mutants. The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.

Conclusion

The spatial meta-GRN model integrates available experimental data and contributes to further understanding of the Arabidopsis epidermal system. It also provides a systems biology framework to explore the interplay among sub-networks of a GRN, cell-to-cell communication, cell shape and domain traits, which could help understanding of general aspects of patterning processes. For instance, our model suggests that the information needed for cell fate determination emerges from dynamic processes that depend upon molecular components inside and outside differentiating cells, suggesting that the classical distinction of lineage versus positional cell differentiation may be instrumental but rather artificial. It also suggests that interlinkage of nonlinear and redundant sub-networks in larger networks is important for pattern robustness. Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible. A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

Understanding the web of life: the birds, the bees, and sex with aliens

Interactions in food webs indicate the structure and stability of ecosystems. Now, new research uses these interactions to illustrate the vulnerability of pollination webs to invasive plants and pollinators.

Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis

BACKGROUND

Biological systems are interacting, molecular networks in which genetic variation contributes to phenotypic heterogeneity. This heterogeneity is traditionally modelled as a dichotomous trait (e.g. affected vs. non-affected). This is far too simplistic considering the complexity and genetic variations of such networks.

METHODS

In this study on type 2 diabetes mellitus, heterogeneity was resolved in a latent class framework combined with structural equation modelling using phenotypic indicators of distinct physiological processes. We modelled the clinical condition "the metabolic syndrome", which is known to be a heterogeneous and polygenic condition with a clinical endpoint (type 2 diabetes mellitus). In the model presented here, genetic factors were not included and no genetic model is assumed except that genes operate in networks. The impact of stratification of the study population on genetic interaction was demonstrated by analysis of several genes previously associated with the metabolic syndrome and type 2 diabetes mellitus.

RESULTS

The analysis revealed the existence of 19 distinct subpopulations with a different propensity to develop diabetes mellitus within a large healthy study population. The allocation of subjects into subpopulations was highly accurate with an entropy measure of nearly 0.9. Although very few gene variants were directly associated with metabolic syndrome in the total study sample, almost one third of all possible epistatic interactions were highly significant. In particular, the number of interactions increased after stratifying the study population, suggesting that interactions are masked in heterogenous populations. In addition, the genetic variance increased by an average of 35-fold when analysed in the subpopulations.

CONCLUSION

The major conclusions from this study are that the likelihood of detecting true association between genetic variants and complex traits increases tremendously when studied in physiological homogenous subpopulations and on inclusion of epistasis in the analysis, whereas epistasis (i.e. genetic networks) is ubiquitous and should be the basis in modelling any biological process.

The implications of human metabolic network topology for disease comorbidity

Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.

The landscape of human proteins interacting with viruses and other pathogens

Infectious diseases result in millions of deaths each year. Mechanisms of infection have been studied in detail for many pathogens. However, many questions are relatively unexplored. What are the properties of human proteins that interact with pathogens? Do pathogens interact with certain functional classes of human proteins? Which infection mechanisms and pathways are commonly triggered by multiple pathogens? In this paper, to our knowledge, we provide the first study of the landscape of human proteins interacting with pathogens. We integrate human-pathogen protein-protein interactions (PPIs) for 190 pathogen strains from seven public databases. Nearly all of the 10,477 human-pathogen PPIs are for viral systems (98.3%), with the majority belonging to the human-HIV system (77.9%). We find that both viral and bacterial pathogens tend to interact with hubs (proteins with many interacting partners) and bottlenecks (proteins that are central to many paths in the network) in the human PPI network. We construct separate sets of human proteins interacting with bacterial pathogens, viral pathogens, and those interacting with multiple bacteria and with multiple viruses. Gene Ontology functions enriched in these sets reveal a number of processes, such as cell cycle regulation, nuclear transport, and immune response that participate in interactions with different pathogens. Our results provide the first global view of strategies used by pathogens to subvert human cellular processes and infect human cells. Supplementary data accompanying this paper is available at http://staff.vbi.vt.edu/dyermd/publications/dyer2008a.html.

Molecular network of obesity: what does it promise for pharmacotherapy?

There is growing evidence that receptors that respond to orexigenic and anorexigenic signals of respective neuropeptides are also implicated in cognitive, emotional, sensory and motor functions. How do these signals trigger a particular appetitive function while also acting in so different contexts in controlling non-appetitive behaviours? This perspective seeks an answer in their peculiar modular organization when each module planted in complex networks controlling appetite is also engaged in different domains. Network analysis may be essential in considering pharmacotherapeutic interventions and, in particular, when anticipating untoward central effects of agents explored from a therapeutic point of view.

Computing topological parameters of biological networks

Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user.

AVAILABILITY

NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org