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PubMed2004

Social networks and homelessness among women heads of household.

To examine possible bidirectional relationships between homelessness and deficient social networks, we compared the networks of 251 mothers before, and approximately 5 years after, their families entered shelters with networks of 291 consistently housed poor mothers. At Time 1, more women on the verge of homelessness than housed women reported that they had mothers, grandmothers, friends, and relatives but fewer believed these network members were housing resources. At Time 2, after homeless women were rehoused, these network differences between consistently housed and formerly homeless women had largely disappeared. Contrary to prior research findings, formerly homeless mothers did not report smaller networks, more children or fewer partners. However, formerly homeless women did report fewer positive functions. Because of city policies, homeless mothers were frequently rehoused far from network members.

Adult↗

PubMed2000

Social networks and functional status in patients with psychosis.

OBJECTIVE: To investigate whether large social networks are associated with better functional outcome in psychotic disorders. METHOD: A prospective cohort study of epidemiologically representative patients with psychosis was analysed to examine the relationship between social networks and functional outcome. Standardized validated measures were used for diagnosis, social networks and outcome. RESULTS: There was strong evidence of cross-sectional associations between the size of the social network and the number of active interactions, and functional outcome. Structural equation modelling found weak evidence of an additional longitudinal association. CONCLUSION: There was consistent evidence of a positive correlation between the total size of patients' social networks and the number of active interactions, and functional outcome. The direction of causality cannot be determined by this analysis, although it is suggested that the effect of social networks on functioning is stronger than vice versa. Patients' functioning may improve with interventions that improve social networks.

Adolescent↗

PubMed2004

To have but one true friend: implications for practice of research on alcohol use disorders and social network.

Although social networks play an integral role in the recognition and resolution of drinking problems, social network influences may be positive, negative, or mixed. The author reviewed empirical literature on positive and negative aspects of the structure of problem drinkers' social networks, the impact of the social network on problem recognition, social network predictors of treatment outcomes, treatments that involve the social network in treatment, and posttreatment changes in social network structure and functioning. Future directions for research and clinical implications of research findings are discussed.

Alcohol-Related Disorders↗

PubMed2000

A synthetic oscillatory network of transcriptional regulators.

Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems. Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function. We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli. The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells. The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation. This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components. Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks.

Bacterial Proteins↗

PubMed2005

Expression dynamics of a cellular metabolic network.

Toward the goal of understanding system properties of biological networks, we investigate the global and local regulation of gene expression in the Saccharomyces cerevisiae metabolic network. Our results demonstrate predominance of local gene regulation in metabolism. Metabolic genes display significant coexpression on distances smaller than the average network distance, a behavior supported by the distribution of transcription factor binding sites in the metabolic network and genome context associations. Positive gene coexpression decreases monotonically with distance in the network, while negative coexpression is strongest at intermediate network distances. We show that basic topological motifs of the metabolic network exhibit statistically significant differences in coexpression behavior.

Binding Sites↗

PubMed2002

Metabolic network structure determines key aspects of functionality and regulation.

The relationship between structure, function and regulation in complex cellular networks is a still largely open question. Systems biology aims to explain this relationship by combining experimental and theoretical approaches. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness or metabolic phenotype, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.

Biomass↗

PubMed2005

Design principles of a bacterial signalling network.

Cellular biochemical networks have to function in a noisy environment using imperfect components. In particular, networks involved in gene regulation or signal transduction allow only for small output tolerances, and the underlying network structures can be expected to have undergone evolution for inherent robustness against perturbations. Here we combine theoretical and experimental analyses to investigate an optimal design for the signalling network of bacterial chemotaxis, one of the most thoroughly studied signalling networks in biology. We experimentally determine the extent of intercellular variations in the expression levels of chemotaxis proteins and use computer simulations to quantify the robustness of several hypothetical chemotaxis pathway topologies to such gene expression noise. We demonstrate that among these topologies the experimentally established chemotaxis network of Escherichia coli has the smallest sufficiently robust network structure, allowing accurate chemotactic response for almost all individuals within a population. Our results suggest that this pathway has evolved to show an optimal chemotactic performance while minimizing the cost of resources associated with high levels of protein expression. Moreover, the underlying topological design principles compensating for intercellular variations seem to be highly conserved among bacterial chemosensory systems.

Adaptation, Physiological↗

PubMed2005

Using process diagrams for the graphical representation of biological networks.

With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human- and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition-based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner.org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).

Animals↗

PubMed2005

Effect of sampling on topology predictions of protein-protein interaction networks.

Currently available protein-protein interaction (PPI) network or 'interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies--most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing interactome maps cannot be confidently extrapolated to complete interactomes.

Animals↗

PubMed2005

APC is a component of an organizing template for cortical microtubule networks.

A microtubule network on the basal cortex of polarized epithelial cells consists of non-centrosomal microtubules of mixed polarity. Here, we investigate the proteins that are involved in organizing this network, and we show that end-binding protein 1 (EB1), adenomatous polyposis coli protein (APC) and p150Glued - although considered to be microtubule plus-end-binding proteins - are localized along the entire length of microtubules within the network, and at T-junctions between microtubules. The network shows microtubule behaviours that arise from physical interactions between microtubules, including microtubule plus-end stabilization on the sides of other microtubules, and sliding of microtubule ends along other microtubules. APC also localizes to the basal cortex. Microtubules grew over and paused at APC puncta; an in vitro reconstituted microtubule network overlaid APC puncta; and microtubule network reconstitution was inhibited by function-blocking APC antibodies. Thus, APC is a component of a cortical template that guides microtubule network formation.

Adenomatous Polyposis Coli Protein↗

PubMed2004

Gene regulatory network growth by duplication.

We are beginning to elucidate transcriptional regulatory networks on a large scale and to understand some of the structural principles of these networks, but the evolutionary mechanisms that form these networks are still mostly unknown. Here we investigate the role of gene duplication in network evolution. Gene duplication is the driving force for creating new genes in genomes: at least 50% of prokaryotic genes and over 90% of eukaryotic genes are products of gene duplication. The transcriptional interactions in regulatory networks consist of multiple components, and duplication processes that generate new interactions would need to be more complex. We define possible duplication scenarios and show that they formed the regulatory networks of the prokaryote Escherichia coli and the eukaryote Saccharomyces cerevisiae. Gene duplication has had a key role in network evolution: more than one-third of known regulatory interactions were inherited from the ancestral transcription factor or target gene after duplication, and roughly one-half of the interactions were gained during divergence after duplication. In addition, we conclude that evolution has been incremental, rather than making entire regulatory circuits or motifs by duplication with inheritance of interactions.

Bacterial Proteins↗

PubMed2025

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

Journal Article↗

PubMed2006

From homogeneous to fractal normal and tumorous microvascular networks in the brain.

We studied normal and tumorous three-dimensional (3D) microvascular networks in primate and rat brain. Tissues were prepared following a new preparation technique intended for high-resolution synchrotron tomography of microvascular networks. The resulting 3D images with a spatial resolution of less than the minimum capillary diameter permit a complete description of the entire vascular network for volumes as large as tens of cubic millimeters. The structural properties of the vascular networks were investigated by several multiscale methods such as fractal and power-spectrum analysis. These investigations gave a new coherent picture of normal and pathological complex vascular structures. They showed that normal cortical vascular networks have scale-invariant fractal properties on a small scale from 1.4 mum up to 40 to 65 mum. Above this threshold, vascular networks can be considered as homogeneous. Tumor vascular networks show similar characteristics, but the validity range of the fractal regime extend to much larger spatial dimensions. These 3D results shed new light on previous two dimensional analyses giving for the first time a direct measurement of vascular modules associated with vessel-tissue surface exchange.

Animals↗

PubMed2005

Highly optimised global organisation of metabolic networks.

High-level, mathematically precise descriptions of the global organisation of complex metabolic networks are necessary for understanding the global structure of metabolic networks, the interpretation and integration of large amounts of biologic data (sequences, various -omics) and ultimately for rational design of therapies for disease processes. Metabolic networks are highly organised to execute their function efficiently while tolerating wide variation in their environment. These networks are constrained by physical requirements (e.g. conservation of energy, redox and small moieties) but are also remarkably robust and evolvable. The authors use well-known features of the stoichiometry of bacterial metabolic networks to demonstrate how network architecture facilitates such capabilities, and to develop a minimal abstract metabolism which incorporates the known features of the stoichiometry and respects the constraints on enzymes and reactions. This model shows that the essential functionality and constraints drive the tradeoffs between robustness and fragility, as well as the large-scale structure and organisation of the whole network, particularly high variability. The authors emphasise how domain-specific constraints and tradeoffs imposed by the environment are important factors in shaping stoichiometry. Importantly, the consequence of these highly organised tradeoffs and tolerances is an architecture that has a highly structured modularity that is self-dissimilar and scale-rich.

Animals↗

PubMed2006

In silico evolution of functional modules in biochemical networks.

Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis-Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.

Algorithms↗

PubMed2004

Modelling periodic oscillation of biological systems with multiple timescale networks.

In this paper, we aim to develop a new methodology to model and design periodic oscillators of biological networks, in particular gene regulatory networks with multiple genes, proteins and time delays, by using multiple timescale networks (MTN). Fast reactions constitute a positive feedback-loop network (PFN), while slow reactions consist of a cyclic feedback-loop network (CFN), in MTN. Multiple timescales are exploited to simplify models according to singular perturbation theory. We show that a MTN has no stable equilibrium but stable periodic orbits when certain conditions are satisfied. Specifically, we first prove the basic properties of MTNs with only one PFN, and then generalise the result to MTNs with multiple PFNs. Finally, we design a biologically plausible gene regulatory network by the cI and Lac genes, to demonstrate the theoretical results. Since there is less restriction on the network structure of a MTN, it can be expected to apply to a wide variety of areas on the modelling, analysing and designing of biological systems.

Biological Clocks↗

PubMed2004

Human attentional networks.

In recent years it has been possible to treat attention as an organ system with its own anatomy, circuitry and set of functions. We view attention in terms of three interrelated neural networks in the human brain. These networks carry out the specific functions of developing and maintaining the alert state, orienting to sensory input, and executive control. Damage to these networks or their chemical neuromodulators can produce specific neurological and psychiatric deficits. We have conducted behavioral, developmental, and patient studies using functional magnetic resonance imaging (fMRI), event related potentials (ERP), genetics, and computational modeling to investigate the anatomy, circuitry, pathology, and development of attentional networks. The Attentional Network Test (ANT) is developed to measure the efficiency of each of the attention networks. The ANT can also serve as an endophenotype for genetic studies on attentional networks. This paper reviews our work with the ANT in studies of normal performance and various forms of psychopathology.

Arousal↗

PubMed2005

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.

Journal Article↗