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Acquired Immunodeficiency Syndrome↗
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Organization of network properties of cells in local and distributed neuronal networks of the brain of cats.
The network properties of neurons of the visual and motor cortex and of the lateral nucleus of the hypothalamus were investigated on the basis of identified interneuronal interactions, using the cross-correlation method of analysis, in cats with developed alimentary conditioned instrumental reflexes to light. The varied organization of the network properties of cortical neurons in the organization of local and distributed neuronal networks was demonstrated, namely: the predominance of divergent properties over convergent properties for large cells in local networks and the leveling out of these relationships in distributed networks. The neurons of the lateral nucleus of the hypothalamus had an equal representation of convergent and divergent properties in the organization of local and distributed networks. The network properties of neurons of the cortical and subcortical structures were manifested in the background, following the development of conditioned reflexes, and during extinction. Only the small cells of the visual cortex were functionally dependent and changed the relationship of network properties in local networks during the extinction of conditioned reflexes.
Action Potentials↗
Application of network theory to potential mycorrhizal networks.
The concept of a common mycorrhizal network implies that the arrangement of plants and mycorrhizal fungi in a community shares properties with other networks. A network is a system of nodes connected by links. Here we apply network theory to mycorrhizas to determine whether the architecture of a potential common mycorrhizal network is random or scale-free. We analyzed mycorrhizal data from an oak woodland from two perspectives: the phytocentric view using trees as nodes and fungi as links and the mycocentric view using fungi as nodes and trees as links. From the phytocentric perspective, the distribution of potential mycorrhizal links, as measured by the number of ectomycorrhizal morphotypes on trees of Quercus garryana, was random with a short tail, implying that all the individuals of this species are more or less equal in linking to fungi in a potential network. From the mycocentric perspective, however, the distribution of plant links to fungi was scale-free, suggesting that certain fungus species may act as hubs with frequent connections to the network. Parallels exist between social networks and mycorrhizas that suggest future lines of study on mycorrhizal networks.
Biodiversity↗
Subnets of scale-free networks are not scale-free: sampling properties of networks.
Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.
Journal Article↗
The role of certain Post classes in Boolean network models of genetic networks.
A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.
Computational Biology↗
A network of investigator networks in human genome epidemiology.
The task of identifying genetic determinants for complex, multigenetic diseases is hampered by small studies, publication and reporting biases, and lack of common standards worldwide. The authors propose the creation of a network of networks that include groups of investigators collecting data for human genome epidemiology research. Twenty-three networks of investigators addressing specific diseases or research topics and representing several hundreds of teams have already joined this initiative. For each field, the authors are currently creating a core registry of teams already participating in the respective network. A wider international registry will include all other teams also working in the same field. Independent investigators are invited to join the registries and existing networks and to join forces in creating additional ones as needed. The network of networks aims to register these networks, teams, and investigators; be a resource for information about or connections to the many networks; offer methodological support; promote sound design and standardization of analytical practices; generate inclusive overviews of fields at large; facilitate rapid confirmation of findings; and avoid duplication of effort.
Databases, Genetic↗
Network motifs: simple building blocks of complex networks.
Complex networks are studied across many fields of science. To uncover their structural design principles, we defined "network motifs," patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.
Algorithms↗
Possible continuity of subplasmalemmal cytoplasmic network with basement membrane cord network: ultrastructural study.
The ultrastructure of the subplasmalemmal cytoplasm of the cell and the associated basement membrane as well as the area of the cell-basement membrane border were observed with high resolution electron microscopy after preparation of the tissues with cryofixation or glutaraldehyde fixation followed by freeze substitution. The subplasmalemmal cytoplasm of the smooth muscle cells of rat epididymal tubules and the podocyte processes of the mouse glomerular visceral epithelium were found to be composed of a fine network of irregular anastomosing strands. This network closely resembled the previously characterized cord network of the basement membrane. The cords are known to be composed of a 1.5 to 3 nm thick core filament made up of type IV collagen which is surrounded by an irregular 'sheath' of other components. The strands in the subplasmalemmal network showed ultrastructural features similar to those of the cord network. Ribbon-like, 4.5 nm wide heparan sulfate proteoglycan 'double tracks' were previously reported to be associated with the cord network. Structures similar in size and appearance to the double tracks were also found in the subplasmalemmal network. At the cell-basement membrane border, the lamina densa of the basement membrane was in contact with the cell without the intervening space of a lamina lucida which was recently found to be an artefact caused by conventional tissue processing. Furthermore, the subplasmalemmal network appeared to be continuous through the plasma membrane, with the cord network of the basement membrane.(ABSTRACT TRUNCATED AT 250 WORDS)
Animals↗
Network participation indices: characterizing component roles for information processing in neural networks.
We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the interesting property of linking local features of individual network components to distributed properties that arise within the network as a whole. We use connectivity data on large-scale cortical networks to demonstrate the virtues of this approach and highlight some interesting features that had not been brought up in previously published material. Some implications of our approach for defining network characteristics relevant to functional segregation and functional integration, for example, from functional imaging studies are discussed.
Mental Processes↗
Neural network studies. 4. Introduction to associative neural networks.
Associative neural network (ASNN) represents a combination of an ensemble of feed-forward neural networks and the k-nearest neighbor technique. This method uses the correlation between ensemble responses as a measure of distance amid the analyzed cases for the nearest neighbor technique. This provides an improved prediction by the bias correction of the neural network ensemble. An associative neural network has a memory that can coincide with the training set. If new data becomes available, the network further improves its predictive ability and provides a reasonable approximation of the unknown function without a need to retrain the neural network ensemble. This feature of the method dramatically improves its predictive ability over traditional neural networks and k-nearest neighbor techniques, as demonstrated using several artificial data sets and a program to predict lipophilicity of chemical compounds. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models. It is shown that analysis of such correlations makes it possible to provide "property-targeted" clustering of data. The possible applications and importance of ASNN in drug design and medicinal and combinatorial chemistry are discussed. The method is available on-line at http://www.vcclab.org/lab/asnn.
Neural Networks, Computer↗
Using neural networks as an aid in the determination of disease status: comparison of clinical diagnosis to neural-network predictions in a pedigree with autosomal dominant limb-girdle muscular dystrophy.
Studies of the genetics of certain inherited diseases require expertise in the determination of disease status even for single-locus traits. For example, in the diagnosis of autosomal dominant limb-girdle muscular dystrophy (LGMD1A), it is not always possible to make a clear-cut determination of disease, because of variability in the diagnostic criteria, age at onset, and differential presentation of disease. Mapping such diseases is greatly simplified if the data present a homogeneous genetic trait and if disease status can be reliably determined. Here, we present an approach to determination of disease status, using methods of artificial neural-network analysis. The method entails "training" an artificial neural network, with input facts (based on diagnostic criteria) and related results (based on disease diagnosis). The network contains weight factors connecting input "neurons" to output "neurons," and these connections are adjusted until the network can reliably produce the appropriate outputs for the given input facts. The trained network can be "tested" with a second set of facts, in which the outcomes are known but not provided to the network, to see how well the training has worked. The method was applied to members of a pedigree with LGMD1A, now mapped to chromosome 5q. We used diagnostic criteria and disease status to train a neural network to classify individuals as "affected" or "not affected." The trained network reproduced the disease diagnosis of all individuals of known phenotype, with 98% reliability. This approach defined an appropriate choice of clinical factors for determination of disease status. Additionally, it provided insight into disease classification of those considered to have an "unknown" phenotype on the basis of standard clinical diagnostic methods.
Adolescent↗
Establishing a practice based primary care research network. The University Family Practice Network in South Australia.
BACKGROUND: Practice based research networks enable the study of primary care problems in a primary care setting. To be successful, our experience indicates that a network will need to incorporate a number of key components. In South Australia, a University Family Practice Network has been established to undertake primary health care research and teaching and to contribute to the general practice workforce. The University Family Practice Network is composed of five practices and is managed jointly by the Department of General Practice, Adelaide University and the South Australian Centre for Rural and Remote Health, Adelaide University and the University of South Australia. OBJECTIVE: This article details the successful establishment of the University Family Practice Network in the hope of providing a model for other universities who may be interested in setting up a practice based research network. DISCUSSION: The characteristics of this network include fractional academic positions for general practitioners, computerised medical records, location in rural and urban sites, regular meetings and the appointment of a research fellow. A number of benefits have arisen since the network's establishment that will assist its future development and sustainability. These include collocation with allied health services, development of local expertise and workforce support.
Australia↗
The Mlx network: evidence for a parallel Max-like transcriptional network that regulates energy metabolism.
Recent experiments suggest the existence of a transcriptional network that functions in parallel to the canonical Myc/Max/Mad transcriptional network. Unlike the Myc/Max/Mad network, our understanding of this network is still in its infancy. At the center of this network is a Max-like protein called Mlx; hence we have called this network the Mlx network. Like Max, Mix interacts with transcriptional repressors and transcriptional activators, namely the Mad family and the Mondo family, respectively. Similar to Max-containing heterodimers, Mlx-containing heterodimers recognize CACGTG E-box elements, suggesting that the transcriptional targets of these two networks may overlap. Supporting this hypothesis, we have observed genetic interactions between the Drosophila melanogaster orthologs of Myc and Mondo. In higher eukaryotes, two proteins, MondoA and MondoB/CHREBP/WBSCR14, constitute the Mondo family. At present little is known about the transcriptional targets of MondoA; however, pyruvate kinase is a putative target of MondoB/CHREBP/WBSCR14, suggesting a function for the Mondo family in glucose and/or lipid metabolism. Finally, unlike the predominant nuclear localization of Myc family proteins, both Mondo family members localize to the cytoplasm. Therefore, while the Myc and Mondo families may share some biological functions, it is likely each family is under distinct regulatory control.
Amino Acid Sequence↗
Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.
MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.
Cell Cycle↗
An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs.
Recent studies of genome-wide transcriptional regulatory network (TRN) revealed several intriguing structural and dynamic features of gene expression at a system level. Unfortunately, the network under study is often far from complete. A critical question is thus how much the network is incomplete and to what extent this would affect the results of analysis. Here we compare the Escherichia coli TRN built by Shen-Orr et al. (Nature Genet., 31, 64-68) with two TRNs reconstructed from RegulonDB and Ecocyc respectively and present an extended E.coli TRN by integrating information from these databases and literature. The scale of the extended TRN is about twice as large as the previous ones. The new network preserves the multi-layer hierarchical structure which we recently reported but has more layers. More global regulators are inferred. While the feed forward loop (FFL) is confirmed to be highly representative in the network, the distribution of the different types of FFLs is different from that based on the incomplete network. In contrast to the notion of motif aggregation and formation of homologous motif clusters, we found that most FFLs interact and form a giant motif cluster. Furthermore, we show that only a small portion of the genes is solely regulated by only one FFL. Many genes are regulated by two or more interacting FFLs or other more complicated network motifs together with transcriptional factors not belonging to any network motifs, thereby forming complex regulatory circuits. Overall, the extended TRN represents a more solid basis for structural and functional analysis of genome-wide gene regulation in E.coli.
Databases, Genetic↗
The network of sheep movements within Great Britain: Network properties and their implications for infectious disease spread.
During the 2001 foot and mouth disease epidemic in the UK, initial dissemination of the disease to widespread geographical regions was attributed to livestock movement, especially of sheep. In response, recording schemes to provide accurate data describing the movement of large livestock in Great Britain (GB) were introduced. Using these data, we reconstruct directed contact networks within the sheep industry and identify key epidemiological properties of these networks. There is clear seasonality in sheep movements, with a peak of intense activity in August and September and an associated high risk of a large epidemic. The high correlation between the in and out degree of nodes favours disease transmission. However, the contact networks were largely dissasortative: highly connected nodes mostly connect to nodes with few contacts, effectively slowing the spread of disease. This is a result of bipartite-like network properties, with most links occurring between highly active markets and less active farms. When comparing sheep movement networks (SMNs) to randomly generated networks with the same number of nodes and node degrees, despite structural differences (such as disassortativity and higher frequency of even path lengths in the SMNs), the characteristic path lengths within the SMNs are close to values computed from the corresponding random networks, showing that SMNs have 'small-world'-like properties. Using the network properties, we show that targeted biosecurity or surveillance at highly connected nodes would be highly effective in preventing a large and widespread epidemic.
Animal Husbandry↗
Why social networks are different from other types of networks.
We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.
Journal Article↗
Affine versus non-affine fibril kinematics in collagen networks: theoretical studies of network behavior.
The microstructure of tissues and tissue equivalents (TEs) plays a critical role in determining the mechanical properties thereof. One of the key challenges in constitutive modeling of TEs is incorporating the kinematics at both the macroscopic and the microscopic scale. Models of fibrous microstructure commonly assume fibrils to move homogeneously, that is affine with the macroscopic deformation. While intuitive for situations of fibril-matrix load transfer, the relevance of the affine assumption is less clear when primary load transfer is from fibril to fibril. The microstructure of TEs is a hydrated network of collagen fibrils, making its microstructural kinematics an open question. Numerical simulation of uniaxial extensile behavior in planar TE networks was performed with fibril kinematics dictated by the network model and by the affine model. The average fibril orientation evolved similarly with strain for both models. The individual fibril kinematics, however, were markedly different. There was no correlation between fibril strain and orientation in the network model, and fibril strains were contained by extensive reorientation. As a result, the macroscopic stress given by the network model was roughly threefold lower than the affine model. Also, the network model showed a toe region, where fibril reorientation precluded the development of significant fibril strain. We conclude that network fibril kinematics are not governed by affine principles, an important consideration in the understanding of tissue and TE mechanics, especially when load bearing is primarily by an interconnected fibril network.
Anisotropy↗