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PubMed2005

Signal transduction networks: topology, response and biochemical processes.

Conventionally, biological signal transduction networks are analysed using experimental and theoretical methods to describe specific protein components, interactions, and biochemical processes and to model network behavior under various conditions. While these studies provide crucial information on specific networks, this information is not easily converted to a broader understanding of signal transduction systems. Here, using a specific model of protein interaction we analyse small network topologies to understand their response and general properties. In particular, we catalogue the response for all possible topologies of a given network size to generate a response distribution, analyse the effects of specific biochemical processes on this distribution, and analyse the robustness and diversity of responses with respect to internal fluctuations or mutations in the network. The results show that even three- and four-protein networks are capable of creating diverse and biologically relevant responses, that the distribution of response types changes drastically as a function of biochemical processes at protein level, and that certain topologies strongly pre-dispose a specific response type while others allow for diverse types of responses. This study sheds light on the response types and properties that could be expected from signal transduction networks, provides possible explanations for the role of certain biochemical processes in signal transduction and suggests novel approaches to interfere with signaling pathways at the molecular level. Furthermore it shows that network topology plays a key role on determining response type and properties and that proper representation of network topology is crucial to discover and understand so-called building blocks of large networks.

Animals↗
PubMed2005

Semi-synthetic mammalian gene regulatory networks.

In recent years gene network engineers have celebrated spectacular success: Genetic devices such as epigenetic toggle switches and oscillating networks have been engineered and pioneered a new ever-increasing scientific community known as synthetic biology. While synthetic biology was until recently restricted to network assembly and testing in prokaryotes, decisive advances have been achieved in eukaryotic systems based on current availability of different human-compatible transgene control technologies. Most prominent examples include the epigenetic gene network enabling metastable fully inheritable transgene expression states in mice, artificial regulatory cascades managing multi-level expression control and Boolean-type BioLogic gates supporting near-digital expression readout. The majority of transgene control networks available to date are fully synthetic and integrate artificial extracellular signals in a desired host metabolism-independent manner. Yet, in order to develop their full anticipated therapeutic potential, synthetic transgene control circuits need to be well interconnected with the host cell's regulatory networks in order to enable physiologic control of prosthetic molecular expression units. We have designed three semi-synthetic transcription control networks able to integrate physiologic oxygen levels and artificial antibiotic signals to produce expression readout with NOT IF or NOR-type Boolean logic or discrete multi-level control of several intracellular and secreted model product proteins. Subtle differences in the regulation performance of the endogenous oxygen-sensing system in CHO-K1 and human HT-1080 switched the semi-synthetic network's readout from a classic four-level (high, medium, low, basal) regulatory cascade to a network enabling six discrete transgene expression levels. These findings are in excellent correspondence with a mathematical model. Prosthetic networks, precisely embedded in host regulatory networks and co-fine-tuned by physiologic as well as pharmacologic input signals, will foster future advances in gene therapy and tissue engineering.

Animals↗
PubMed1998

Social network characteristics as mediators in the relationship between sexual abuse and HIV risk.

Recent research suggests that sexual abuse may be a potent risk factor for engaging in HIV risk behaviors for women. This relationship is likely mediated by the long term sequelae of sexual abuse. One plausible causal pathway posits that specific social network characteristics increase HIV risk exposure opportunities. This is premised on the belief that previous sexual abuse predisposes some women to become members of risk networks characterized by deviant behaviors and that HIV risk occurs in the context of these networks. One hundred and thirty women opiate users were systematically recruited from methadone maintenance and syringe exchange programs in New York City. The women participated in a one hour interview and provided information on drug use and frequency, HIV drug and sex risk behaviors, social network characteristics, and sexual abuse histories. Univariate and logistic regression techniques were used to test the relationship between sexual abuse and increased HIV risk as mediated by social network characteristics. Previous sexual abuse was strongly related to all social network characteristics examined. Moreover, these network characteristics appeared to affect patterns of drug use in identifiable ways. Social isolation was the only network characteristic associated with both HIV drug and sex risk behaviors. However, although a history of sexual abuse was significantly associated with five of the seven specific HIV risk behaviors examined, the relationship between sexual abuse and HIV risk behaviors remained unchanged when social network characteristics were included in the statistical model. Overall, the results suggest that a sexual abuse experience or its aftermath influence behavior far into the future. However, the results of this study did not show social network characteristics mediating the relationship between sexual abuse and HIV risk. Rather, previous sexual abuse and social network characteristics appear to be independent contributors to HIV risk behaviors for women.

Adult↗
PubMed1997

Changes in network characteristics and HIV risk behavior among injection drug users.

Studies indicate that HIV risk behaviors vary greatly among injection drug users (IDUs). The source of such variation is often ascribed to individual differences, but much of it is due to how IDUs are grouped into social networks. Nevertheless, given the turbulent and uncertain lives led by many IDUs, it would not be surprising if their social networks changed substantially over time. We used data from a study of the social networks of IDUs in Chicago and Washington, DC, to examine changes in individual behavior and network characteristics over time. The results indicated few changes in standard network measures, such as density of ties or network size, over time. However, specific network change measures, that is, indicators of movement into and out of networks, showed significant movement of network members over time. Moreover, movement of members into a network significantly predicted a higher likelihood of risky injection drug use over time. We suggest that these movements are indicative of a lack of a stable resource base among IDU networks.

Adolescent↗
PubMed2000

Exploring Alternative Models of Complex Patient Management with Artificial Neural Networks.

This study applied an unsupervised neural network modeling process to test data of the National Board of Medical Examiners (NBME) Computer-based Clinical Scenarios (CCS) to identify new performance categories and validate this process as a scoring technique. The classifications resulting from this neural network modeling were consistent with the NBME model in that highly rated NMBE performances (ratings of 7 or 8) were clustered together on the neural network output grid. Very low performance ratings appeared to share few common features and were accordingly classified at isolated nodes. This clustering was reproducible across three separately trained networks with greater than 80% agreement in two of the three networks trained. However, the neural network also contained performance clusters where disparate NBME-based ratings ranged from 1 (worst) to 8 (best). Here, agreement between networks was less than 60%. Through visualization of the search strategies (search path mapping), this neural network clustering was found to be sensitive to quantitative and qualitative test selections such as excessive usage of irrelevant tests reflecting broader behavioral classification in some instances. A disparity between NBME ratings and an independent human rating system was detected by the neural network model since disagreement among raters was also reflected by a lack of neural network performance clustering. Agreement between rating systems, however, was correlated with neural network clustering for 92% of the highly rated performances.

Journal Article↗
PubMed1998

An ecologically differentiated, multifactor model of adolescent network orientation.

The paper presents a test of an ecologically differentiated model of social network orientation for adolescents that distinguished between different social network reference groups (family, peers, and nonfamily adults). The model was tested in two consecutive studies. Study 1 describes initial model development (N = 120). Study 2 presents a confirmatory factor analysis with a second sample (N = 430) to replicate the factor structure developed in Study 1. Results supported a three-factor model of network orientation that differentiated between network reference groups. Analyses of concurrent and predictive validity indicated that orientation to network reference groups was differentially related to the perceived quality and frequency of support from members of respective social network groups. Group differences (gender, race) regarding network orientation to different network reference groups were consistent with studies of other social network processes. Implications for the study of the network orientation and the study of social networks more generally are discussed.

Adaptation, Psychological↗
PubMed2006

Reconstructing gene regulatory networks: from random to scale-free connectivity.

The manipulation of organisms using combinations of gene knockout, RNAi and drug interaction experiments can be used to reveal regulatory interactions between genes. Several algorithms have been proposed that try to reconstruct the underlying regulatory networks from gene expression data sets arising from such experiments. Often these approaches assume that each gene has approximately the same number of interactions within the network, and the methods rely on prior knowledge, or the investigator's best guess, of the average network connectivity. Recent evidence points to scale-free properties in biological networks, however, where network connectivity follows a power-law distribution. For scale-free networks, the average number of regulatory interactions per gene does not satisfactorily characterise the network. With this in mind, a new reverse engineering approach is introduced that does not require prior knowledge of network connectivity and its performance is compared with other published algorithms using simulated gene expression data with biologically relevant network structures. Because this new approach does not make any assumptions about the distribution of network connections, it is suitable for application to scale-free networks.

Cell Physiological Phenomena↗
PubMed2000

[Physician's anxiety and physician's elegance. Problems in dealing with cost reduction, education of general practitioners and optimal size of practice networks in a cross-national comparison].

The key reason for physicians networking in managed care is to get a better coping with uncertainty on action (treatment) decisions. The second reason for networking in managed care are financial benefits grounds. But this reason is very ambivalent. Three different action problems (role conflicts) in managed care network are to solved, which was also in single practices. In the lecture the decision strategies and decision resources has been compared. Observations are done using expert interviews, patient interviews and analysis of documents in USA, Germany and Switzerland. The first problem is the choosing of a cost reduction strategy which is not reducing the effectiveness. Such "ugly" solution strategies like exclusion of "expensive" patients and a rationing of necessary medical services in a kind of McDonalds network of physicians will fail the target. The optimost way is a saving of all unnecessary medical even injourious performances. The chosen cost reduction strategy is not real visible from outside but in fact limited cognizable and controllable. Evidence based health care can be a resource of treatment decisions and could train such decisions but it will not substitute these decisions. The second problem is the making of real family practitioners as gatekeepers. Knowledge about the care system is still not making a real family practitioner, even if this is the minimum condition of their work. Also contractual relationships between insurance and doctor as a gatekeeper or financial incentives for patients are still making not a real family practitioner as a gatekpeeper. Only throughout the trust of patients supported by second opinions is making the real family practitioner as a gatekeeper. "Doctor hopping" could be the reaction by scarcity of trustworthy family practitioners as gatekeepers. The third problem is the choosing of the optimal scale of a network due to the very different optimal size of networks regarding the requirement of risk spreeds, of the motivated engagement, of competition, incentives of inclusion of insurantes, they always need other net sizes. But it is possible, for each requirement there could function different networks. A practice (doctor's office) can be a member in different networks in several levels. The social transition from a small office to a network of offices is in all business lines a cultural shock involving not only benefits also psychical and social distress. In this there is no difference between health or agriculture or each other business of trade and industry. The destiny of the joint doctor's offices in Germany suggest due to a very serious power to scatter this networks. The comparative analysis of conflicts, strains, resources and strategies of associations and networks could yield from a developed methodical repository in sociology and social psychology what exists since 40 years (see also Meyer--in this journal). But therefore must be included also the action problems, which are only mentioned in passing of the according profession horizon.

Cost Control↗
PubMed2001

Designer gene networks: Towards fundamental cellular control.

The engineered control of cellular function through the design of synthetic genetic networks is becoming plausible. Here we show how a naturally occurring network can be used as a parts list for artificial network design, and how model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics. We first review the relevant work on synthetic gene networks, highlighting the important experimental findings with regard to genetic switches and oscillators. We then present the derivation of a deterministic model describing the temporal evolution of the concentration of protein in a single-gene network. Bistability in the steady-state protein concentration arises naturally as a consequence of autoregulatory feedback, and we focus on the hysteretic properties of the protein concentration as a function of the degradation rate. We then formulate the effect of an external noise source which interacts with the protein degradation rate. We demonstrate the utility of such a formulation by constructing a protein switch, whereby external noise pulses are used to switch the protein concentration between two values. Following the lead of earlier work, we show how the addition of a second network component can be used to construct a relaxation oscillator, whereby the system is driven around the hysteresis loop. We highlight the frequency dependence on the tunable parameter values, and discuss design plausibility. We emphasize how the model equations can be used to develop design criteria for robust oscillations, and illustrate this point with parameter plots illuminating the oscillatory regions for given parameter values. We then turn to the utilization of an intrinsic cellular process as a means of controlling the oscillations. We consider a network design which exhibits self-sustained oscillations, and discuss the driving of the oscillator in the context of synchronization. Then, as a second design, we consider a synthetic network with parameter values near, but outside, the oscillatory boundary. In this case, we show how resonance can lead to the induction of oscillations and amplification of a cellular signal. Finally, we construct a toggle switch from positive regulatory elements, and compare the switching properties for this network with those of a network constructed using negative regulation. Our results demonstrate the utility of model analysis in the construction of synthetic gene regulatory networks. (c) 2001 American Institute of Physics.

Journal Article↗
PubMed2001

From topology to dynamics in biochemical networks.

Abstract formulations of the regulation of gene expression as random Boolean switching networks have been studied extensively over the past three decades. These models have been developed to make statistical predictions of the types of dynamics observed in biological networks based on network topology and interaction bias, p. For values of mean connectivity chosen to correspond to real biological networks, these models predict disordered dynamics. However, chaotic dynamics seems to be absent from the functioning of a normal cell. While these models use a fixed number of inputs for each element in the network, recent experimental evidence suggests that several biological networks have distributions in connectivity. We therefore study randomly constructed Boolean networks with distributions in the number of inputs, K, to each element. We study three distributions: delta function, Poisson, and power law (scale free). We analytically show that the critical value of the interaction bias parameter, p, above which steady state behavior is observed, is independent of the distribution in the limit of the number of elements N--> infinity. We also study these networks numerically. Using three different measures (types of attractors, fraction of elements that are active, and length of period), we show that finite, scale-free networks are more ordered than either the Poisson or delta function networks below the critical point. Thus the topology of scale-free biochemical networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells. (c) 2001 American Institute of Physics.

Journal Article↗
PubMed2002

Food-web structure and network theory: The role of connectance and size.

Networks from a wide range of physical, biological, and social systems have been recently described as "small-world" and "scale-free." However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25-172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power-law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks.

Ecology↗
PubMedDate not supplied

Size and complexity of social networks among substance abusers: childhood and current correlates.

The objective of this study was to identify parental, childhood, demographic, and social function factors associated with social network size and complexity among substance abusers using retrospective data regarding family and childhood history and current data regarding demographic characteristics and psychosocial function. The authors interviewed 505 voluntary patients with substance abuse at two university medical centers in Minnesota and Oklahoma with alcohol-drug programs located within departments of psychiatry. Data collection instruments included a childhood questionnaire, a demographic checklist, and two psychiatric rating scales of psychosocial function. The authors found that years of education, current residence with others, being actively occupied at work or school, and higher psychosocial function on two psychiatrist-rated scales were associated with increased social network size and complexity. Loss of mother, out-of-home placement, and runaway before age 18 were associated with smaller social networks in adulthood. Age, gender, and current marital status were not associated with social network. Regression analysis indicated that network size (i.e., the number of individuals in the network) was associated with higher psychosocial function over the last year but not over the last two weeks, whereas network complexity (ie, the number of subgroups in the network) was related to psychosocial function over both the last year and the last two weeks. These data indicate that in addicted persons, both childhood factors and current social factors affect network size and complexity. Network complexity may be amenable to short-term change, whereas network size may be more related to longer-term coping.

Adaptation, Psychological↗
PubMed1976

Functional significance of the variations in the geometrical organization of tight junction networks.

Using freeze-fracture techniques, we have examined the morpholog of tight junction networks found along the length of the alimentary tract of Xenopus laevis before and after metamorphosis. We have developed the hypothesis, based on these observations, that the geometrical organization of the network determined by the stress-induced shape changes normally experienced by the cells linked by the network. Consistent with this theory, tight junctions can be classified into two distinct types of network organization which differ in their response normal and experimentally induced stress conditions: (a) loosely interconnected networks which can stretch or compress extensively under tension, thereby adapting to stress changes in the tissue; and (b) evenly cross-linked networks which retain their basic morphology under normal stress conditions. The absorptive cells of the large intestine as well as the mucous cells of the gastrointestine or stomach are sealed by the first, flexible type of tight junction. The second type of junctional organization, the evenly cross-connected network, is found between absorptive cells of the small intestine and ciliated cells of the esophagus, and reflects in its constant morphology the relative stability of the apical region of both of these cell types. Networks intermediate between these two types arise when a cell which would normally form a lossely interconnected network borders a cell which tends to form a more evenly cross-linked network, as is found in the esophagus where ciliated and goblet cells adjoin. Despite the change in the animal's diet during metamorphosis from herbivorous to carnivorous, the basic gemetrical organization of the networks associated with each tissue of the alimentary tract remains the same.

Animals↗
PubMed2002

Building and analysing genome-wide gene disruption networks.

MOTIVATION: Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure. RESULTS: We have built genome-wide disruption networks for yeast, using a representation of gene expression data as directed labelled graphs. Nodes represent genes and arcs connect nodes if the disruption of the source gene significantly alters the expression of the target gene. We are interested in features of the resulting disruption networks that are robust over a range of significance cutoffs. The networks show a significant overlap with analogous networks constructed from scientific literature. In disruption networks the number of arcs adjacent to different nodes are distributed roughly according to a power-law, like in many complex systems where the robustness against perturbations is important. The networks are dominated by a single large component and do not have an obvious modular structure. Genes with the highest outdegrees often encode proteins with regulatory functions, whereas genes with the highest indegrees are predominantly involved in metabolism. The local structure of the networks is meaningful, genes involved in the same cellular processes are close together in the network. AVAILABILITY: http://www.ebi.ac.uk/microarray/networks

Chromosome Mapping↗
PubMed2004

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

MOTIVATION: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. RESULTS: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/

Algorithms↗
PubMed2005

Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment.

MOTIVATION: The topology and function of gene regulation networks are commonly inferred from time series of gene expression levels in cell populations. This strategy is usually invalid if the gene expression in different cells of the population is not synchronous. A promising, though technically more demanding alternative is therefore to measure the gene expression levels in single cells individually. The inference of a gene regulation network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, owing to experimental limitations a complete determination of the precursor state is not possible. RESULTS: We investigate a strategy for the inference of gene regulatory networks from incomplete expression data based on dynamic Bayesian networks. This permits prediction of the number of experiments necessary for network inference depending on parameters including noise in the data, prior knowledge and limited attainability of initial states. Our strategy combines a gradual 'Partial Learning' approach based solely on true experimental observations for the network topology with expectation maximization for the network parameters. We illustrate our strategy by extensive computer simulations in a high-dimensional parameter space in a simulated single-cell-based example of hematopoietic stem cell commitment and in random networks of different sizes. We find that the feasibility of network inferences increases significantly with the experimental ability to force the system into different initial network states, with prior knowledge and with noise reduction. AVAILABILITY: Source code is available under: www.izbi.uni-leipzig.de/services/NetwPartLearn.html SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

Algorithms↗
PubMed2005

Social networks among men and women: the effects of age and socioeconomic status.

OBJECTIVES: This study examines the main and interactive effects of age and socioeconomic status (SES) on social networks. METHODS: Respondents are drawn from a regional stratified probability sample aged 40 to 93 years. Hierarchical regression analysis estimates the influence of age and SES on dimensions of social networks, controlling for marital status and health among men and women. RESULTS: Among men, older age was associated with older networks. Professional men report networks that are less geographically proximal, however, occupational effects are most obvious in late life. Among women, age is associated with smaller networks that are older, less geographically proximal, and less frequently contacted. Whereas less education is associated with younger network members in midlife, among women in later life, lower levels of education are not associated with a younger network. Professional women report older networks composed of a higher proportion of friends than do homemakers. Higher levels of education are linked to larger personal networks among men and women, but not to the number of individuals considered closest. Among women, higher levels of education are also associated with less proximal networks. DISCUSSION: An examination of within-group variability reveals influences of age and SES on personal networks among men and women.

Adult↗
PubMedDate not supplied

Benefiting from networks by occupying central positions: an empirical study of the Taiwan health care industry.

At issue is whether network resources imply some resources available to all members in networks or available only to those occupying structurally central positions in networks. In this article, two conceptual models, the additive and interaction models of the firm, are empirically tested regarding the impact of hospital resources, network resources, and centrality on hospital performance in the Taiwan health care industry. The results demonstrate that: (1) in the additive model, hospital resources and centrality independently affect performance, whereas network resources do not; and (2) no evidence supports the interaction effect of centrality and resources on performance. Based on our findings in Taiwanese practices, the extent to which the resources are acquired externally from networks, we suggest that while adopting interorganizational strategies, hospitals should clearly identify those important resources that reside in-house and those transferred from network partners. How hospitals access resources from central positions is more important than what network resources can hospitals acquire from networks. Hospitals should improve performance by exploiting its in-house resources rather than obtaining network resources externally. In addition, hospitals should not only invest in hospital resources for better performance but should also move to central positions in networks to benefit from collaborations.

Efficiency, Organizational↗