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At least 109 records · Page 6Linked to original sources
PubMed2006

Connective tissue: a body-wide signaling network?

Unspecialized "loose" connective tissue forms an anatomical network throughout the body. This paper presents the hypothesis that, in addition, connective tissue functions as a body-wide mechanosensitive signaling network. Three categories of signals are discussed: electrical, cellular and tissue remodeling, each potentially responsive to mechanical forces over different time scales. It is proposed that these types of signals generate dynamic, evolving patterns that interact with one another. Such connective tissue signaling would be affected by changes in movement and posture, and may be altered in pathological conditions (e.g. local decreased mobility due to injury or pain). Connective tissue thus may function as a previously unrecognized whole body communication system. Since connective tissue is intimately associated with all other tissues (e.g. lung, intestine), connective tissue signaling may coherently influence (and be influenced by) the normal or pathological function of a wide variety of organ systems. Demonstrating the existence of a connective signaling network therefore may profoundly influence our understanding of health and disease.

Animals↗
PubMed2004

Global mapping of the yeast genetic interaction network.

A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Amino Acid Sequence↗
PubMed2005

Intrinsic connections of the cingulate cortex in the rat suggest the existence of multiple functionally segregated networks.

The cingulate cortex is a functionally and morphologically heterogeneous cortical area comprising a number of interconnected subregions. To date, the exact anatomy of intracingulate connections has not been studied in detail. In the present study we aimed to determine the topographical and laminar characteristics of intrinsic cingulate connections in the rat, using the anterograde tracers Phaseolus vulgaris-leucoagglutinin and biotinylated dextran amine. For assessment of these data we further refined and compared the existing cytoarchitectonic descriptions of the two major cingulate constituents, the anterior cingulate and retrosplenial cortices. The results of this study demonstrate that rostral areas, i.e. the infralimbic and prelimbic cortices and the rostral one third of the dorsal anterior cingulate cortex are primarily interconnected with each other and not with other cingulate areas. The caudal two thirds of the dorsal anterior cingulate cortex project to the caudal part of the ventral anterior cingulate cortex, whereas the entire ventral anterior cingulate cortex projects to only the mid-rostro-caudal part of the dorsal anterior cingulate cortex. Dense reciprocal connections exist between the remaining, i.e. the supracallosal parts of the anterior cingulate and retrosplenial cortices with a general rostro-caudal topography, in the sense that the rostral part of the anterior cingulate cortex and caudal part of the retrosplenial cortex are interconnected and the same holds true for the caudal part of the anterior cingulate cortex and rostral part of the retrosplenial cortex. This topographical pattern of intracingulate connections relates to the results of several functional studies, suggesting that specific cingulate functions depend on a number of interconnected cingulate subregions. Through their intricate associational connections, these subregions form functionally segregated networks.

Animals↗
PubMed2005

Minireview: the neuroendocrine regulation of puberty: is the time ripe for a systems biology approach?

The initiation of mammalian puberty requires an increase in pulsatile release of GnRH from the hypothalamus. This increase is brought about by coordinated changes in transsynaptic and glial-neuronal communication. As the neuronal and glial excitatory inputs to the GnRH neuronal network increase, the transsynaptic inhibitory tone decreases, leading to the pubertal activation of GnRH secretion. The excitatory neuronal systems most prevalently involved in this process use glutamate and the peptide kisspeptin for neurotransmission/neuromodulation, whereas the most important inhibitory inputs are provided by gamma-aminobutyric acid (GABA)ergic and opiatergic neurons. Glial cells, on the other hand, facilitate GnRH secretion via growth factor-dependent cell-cell signaling. Coordination of this regulatory neuronal-glial network may require a hierarchical arrangement. One level of coordination appears to be provided by a host of unrelated genes encoding proteins required for cell-cell communication. A second, but overlapping, level might be provided by a second tier of genes engaged in specific cell functions required for productive cell-cell interaction. A third and higher level of control involves the transcriptional regulation of these subordinate genes by a handful of upper echelon genes that, operating within the different neuronal and glial subsets required for the initiation of the pubertal process, sustain the functional integration of the network. The existence of functionally connected genes controlling the pubertal process is consistent with the concept that puberty is under genetic control and that the genetic underpinnings of both normal and deranged puberty are polygenic rather than specified by a single gene. The availability of improved high-throughput techniques and computational methods for global analysis of mRNAs and proteins will allow us to not only initiate the systematic identification of the different components of this neuroendocrine network but also to define their functional interactions.

Animals↗
PubMed1990

A neural network simulation of simultaneous single-unit activity recorded from the dragonfly ganglia.

Techniques are described that allow the use of multiple neuron spike data in a computational neural network architecture. The network architecture was devised to match the number of actual neurons from which data were obtained. The network was successfully trained to accurately predict the multiple neuron spike trains. Simultaneous spike histories of 44 neurons were modeled by a network architecture consisting of 44 input units, 88 hidden units with recurrent connections and 44 output units. The activation function of each unit was determined by data unique to a single neuron. These data were coupled with an analog gradient that preserved both the exact spiking times and the relative spiking tendency of each neuron. The input activation values were compared to network output target values calculated to occur 5 msec forward in the composite spiking records of all neurons. Following 2000 training cycles with the gradient data, the average error of each unit in the network was 0.0016. Discrete output values for each network unit were correlated with those of all other units. These correlations were comparable to those done using the actual neuron data. Both correlations reveal a functional connectivity pattern among the units and neurons. These connectivity patterns indicate that the networks may synthesize patterns of activity needed for biological function; in this case, flight patterns carried out in the mesothoracic ganglion of the dragonfly. This model represents, to the best of our knowledge, the first computer based network simulation using actual experimental neural data obtained from a large number of spontaneously active cells in a small intact ganglion.

Action Potentials↗
PubMed2005

Developmental expression of MNAR mRNA in the mouse brain.

During the development of the central nervous system, estrogen influences cellular differentiation and determines the functional connectivity of distinct neural networks. Estrogens generally act through nuclear estrogen receptors (ERs). Recent research has additionally revealed rapid estrogen effects requiring the binding of estrogen to membrane/cytoplasmic ERs and the activation of intracellular signaling systems such as the Src/MAPK cascade. The scaffold protein MNAR/PELP1 appears to be the designated functional mediator of such non-genomic estrogen effects between non-nuclear ERs and Src/MAPKs. In this study, we demonstrate the expression and differential regulation of MNAR mRNA in the developing male and female mouse brain by quantitative polymerase chain reaction. In the midbrain and hypothalamus, a gradual decline in MNAR mRNA levels has been observed prenatally with the highest values at embryonic day 15 and lowest at postnatal day 15. In the cortex, mRNA levels do not fluctuate until postnatal day 7 but decrease thereafter. No differences in MNAR expression between sexes have been detected. Analysis of neuronal and astroglia-enriched cell cultures has revealed the presence of MNAR in both cell types.

Animals↗
PubMed2005

Functional annotation from predicted protein interaction networks.

MOTIVATION: Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. RESULTS: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. AVAILABILITY: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/. SUPPLEMENTARY INFORMATION: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).

Chromosome Mapping↗
PubMed2005

Subgraphs and network motifs in geometric networks.

Many real-world networks describe systems in which interactions decay with the distance between nodes. Examples include systems constrained in real space such as transportation and communication networks, as well as systems constrained in abstract spaces such as multivariate biological or economic data sets and models of social networks. These networks often display network motifs: subgraphs that recur in the network much more often than in randomized networks. To understand the origin of the network motifs in these networks, it is important to study the subgraphs and network motifs that arise solely from geometric constraints. To address this, we analyze geometric network models, in which nodes are arranged on a lattice and edges are formed with a probability that decays with the distance between nodes. We present analytical solutions for the numbers of all three- and four-node subgraphs, in both directed and nondirected geometric networks. We also analyze geometric networks with arbitrary degree sequences and models with a bias for directed edges in one direction. Scaling rules for scaling of subgraph numbers with system size, lattice dimension, and interaction range are given. Several invariant measures are found, such as the ratio of feedback and feed-forward loops, which do not depend on system size, dimension, or connectivity function. We find that network motifs in many real-world networks, including social networks and neuronal networks, are not captured solely by these geometric models. This is in line with recent evidence that biological network motifs were selected as basic circuit elements with defined information-processing functions.

Journal Article↗
PubMed2003

Localization of function via lesion analysis.

This article presents a general approach for employing lesion analysis to address the fundamental challenge of localizing functions in a neural system. We describe functional contribution analysis (FCA), which assigns contribution values to the elements of the network such that the ability to predict the network's performance in response to multilesions is maximized. The approach is thoroughly examined on neurocontroller networks of evolved autonomous agents. The FCA portrays a stable set of neuronal contributions and accurate multilesion predictions that are significantly better than those obtained based on the classical single lesion approach. It is also used for a detailed synaptic analysis of the neurocontroller connectivity network, delineating its main functional backbone. The FCA provides a quantitative way of measuring how the network functions are localized and distributed among its elements. Our results question the adequacy of the classical single lesion analysis traditionally used in neuroscience and show that using lesioning experiments to decipher even simple neuronal systems requires a more rigorous multilesion analysis.

Algorithms↗
PubMed2006

A novel functional module detection algorithm for protein-protein interaction networks.

BACKGROUND: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. RESULTS: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. CONCLUSION: STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.

Journal Article↗
PubMed2000

Brain regulation of feeding behavior and food intake in fish.

In mammals, the orexigenic and anorexigenic neuronal systems are morphologically and functionally connected, forming an interconnected network in the hypothalamus to govern food intake and body weight. However, there are relatively few studies on the brain control of feeding behavior in fish. Recent studies using mammalian neuropeptides or fish homologs of mammalian neuropeptides indicate that brain orexigenic signal molecules include neuropeptide Y, orexins, galanin and beta-endorphin, whereas brain anorexigenic signal molecules include cholecystokinin, bombesin, corticotropin-releasing factor, cocaine- and amphetamine-regulated transcript, and serotonin. Tachykinins may also have an anorectic action in fish. The brain hypothalamic area is associated with regulation of food intake, while sites outside the hypothalamus are also involved in this function. There is correlation between short-term changes in serum growth hormone levels and feeding behavior, although possible mechanisms integrating these functions remain to be defined.

Animals↗
PubMed1999

Fibromodulin-null mice have abnormal collagen fibrils, tissue organization, and altered lumican deposition in tendon.

Fibromodulin is a member of a family of connective tissue glycoproteins/proteoglycans containing leucine-rich repeat motifs. Several members of this gene family bind to fibrillar collagens and are believed to function in the assembly of the collagen network in connective tissues. Here we show that mice lacking a functional fibromodulin gene exhibit an altered morphological phenotype in tail tendon with fewer and abnormal collagen fiber bundles. In fibromodulin-null animals virtually all collagen fiber bundles are disorganized and have an abnormal morphology. Also 10-20% of the bundles in heterozygous mice are similar to the abnormal bundles in fibromodulin-null tail tendon. Ultrastructural analysis of Achilles tendon from fibromodulin-null mice show collagen fibrils with irregular and rough outlines in cross-section. Morphometric analysis show that fibromodulin-null mice have on the average thinner fibrils than wild type animals as a result of a larger preponderance of very thin fibrils in an overall similar range of fibril diameters. Protein and RNA analyses show an approximately 4-fold increase in the content of lumican in fibromodulin-null as compared with wild type tail tendon, despite a decrease in lumican mRNA. These results demonstrate a role for fibromodulin in collagen fibrillogenesis and suggest that the orchestrated action of several leucine-rich repeat glycoproteins/proteoglycans influence the architecture of collagen matrices.

Animals↗
PubMed2007

Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: a principal component analysis of the human visual system.

This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second-level analysis subsequent to model-based General Linear Model (GLM)-type functional activation mapping. Functional homogeneity of the functional magnetic resonance imaging (fMRI) time series within and between clusters was examined on several levels of the visual network, starting from the level of individual clusters up to the network level comprising two or more distinct visual regions. On each level, the number of significant components was identified and compared with the number of clusters in the data set. Eigenimages were used to examine the regional distribution of the extracted components. It was shown that voxels within individual clusters and voxels located in bilateral homologue visual regions can be represented by a single component, constituting the characteristic functional specialization of the cluster(s). If, however, PCA was applied to time series of voxels located in functionally distinct visual regions, more than one component was observed with each component being dominated by voxels in one of the investigated regions. The model of functional connections derived by PCA was in accordance with the well-known functional anatomy and anatomical connectivity of the visual system. PCA in combination with conventional activation mapping might therefore be used to identify the number of functionally distinct nodes in an fMRI data set in order to generate a model of functional connectivity within a neuroanatomical network.

Adult↗
PubMed1995

The protective effects of education on simulated brain injury.

Disturbed intellectual function is an important determinant of long-term recovery after head injury. Residual behavioral disability depends largely upon which hemisphere is damaged and the site of injury within the hemisphere. Resulting neurobehavioral syndromes vary with the pattern of brain insult. While this correlation is important, it is still not well understood how damage to single neurons translates into abnormal behavior. In addition, no currently available technology permits in vivo evaluation of such cells in normal or injured states. While research into brain trauma traditionally emphasize single cell pathology, this level of study is insufficient to clarify how individual cell activity contributes to higher cognition. In contrast, neural network simulations may partially fill this void by predicting biological function. Here, we present data generated by a three-layer neural network that employs the ALOPEX algorithm, capable of learning 10 words. The addition of Gaussian distributed noise into connection weights strengths damaged the network's function; interestingly, the rate and extent of network impairment depended upon the level of learning that occurred during the training period, in agreement with human studies that demonstrate a protective effect of education on subsequent brain injury. Damage to input signals resulted in similar effects on network function. Accordingly, such simulations offer powerful evidence that human behavior may be considered in terms of neuronal cell populations and efforts to preserve brain function at the time of injury should emphasize maintaining neural connections.

Algorithms↗
PubMed2001

Neural network models of schizophrenia.

There is considerable neurobiological evidence suggesting that schizophrenia is associated with reduced corticocortical connectivity. The authors describe two neural network computer simulations that explore functional consequences of these abnormalities. The first utilized an "attractor" neural network capable of content-addressable memory. Application of a pruning rule that eliminated weaker connections over longer distances produced functional fragmentation and the emergence of localized, "parasitic" attractors that intruded into network dynamics. These pathologies generally were expressed only when input information was ambiguous and provide models for delusions and cognitive disorganization. A second neural network simulation examined effects of corticocortical pruning in a speech perception network. Excessive pruning caused the network to produce percepts spontaneously, that is, in the absence of inputs, thereby simulating hallucinations. The "hallucinating" network also demonstrated subtle impairments in narrative speech perception. A parallel study of human patients found similar impairments when comparing hallucinating patients with nonhallucinating patients. In addition, the authors have used transcranial magnetic stimulation (TMS) to directly probe speech perception neurocircuitry in patients with these hallucinations. As predicted by the neural network model, the authors confirmed that "suppressive" low-frequency TMS reduces auditory hallucinations. Neural network simulations provide empirically testable concepts linking phenomenological, cognitive, and neurobiological findings in schizophrenia.

Brain↗
PubMed2002

Combinatorial synthesis of genetic networks.

A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Here, with the use of a combinatorial method, a library of networks with varying connectivity was generated in Escherichia coli. These networks were composed of genes encoding the transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters. They displayed phenotypic behaviors resembling binary logical circuits, with two chemical "inputs" and a fluorescent protein "output." Within this simple system, diverse computational functions arose through changes in network connectivity. Combinatorial synthesis provides an alternative approach for studying biological networks, as well as an efficient method for producing diverse phenotypes in vivo.

Bacterial Proteins↗
PubMed2005

[C-terminal fragment of tetanus toxin: its use in neuronal network analysis and its potential as non-viral vector].

The atoxic C-terminal fragment of tetanus neurotoxin or TTC fragment presents similar retrograde and transsynaptic properties to that of holotoxin. Detection of this fragment is easier when it is associated with a fluorescent marker or with beta-galactosidase activity by genetic fusion or chemical conjugation. Thus, these tracers have been used to study and analyse the synaptic connections of a neural network. In this article, we shortly review the various methods used with this aim including: injection of the fusion protein, adenovirus in vivo expression and transgenesis. Since neural activity is essential for neuronal TTC binding and internalization, the functionality of connections can be also evaluated. Moreover, modifications of the retrograde transport can be detected by using this fragment. Thus, TTC fragment is an excellent tracer to analyse the connectivity and functionality of a neural network. The TTC fragment was also soon proposed as potential therapeutic vector to transport and to deliver a biological activity or gene in a neural network. With this aim, the efficiency of a translocation domain to induce the cytosolic release of the associated activity has been evaluated. The use of the TTC fragment to target specifically a neurotrophic factor to neurons and thus avoid secondary effects has been tested with interesting results.

Animals↗