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At least 991 records · Page 55Linked to original sources
PubMed1998

Use of an artificial neural network to predict length of stay in acute pancreatitis.

Length of stay (LOS) predictions in acute pancreatitis could be used to stratify patients with severe acute pancreatitis, make treatment and resource allocation decisions, and for quality assurance. Artificial neural networks have been used to predict LOS in other conditions but not acute pancreatitis. The hypothesis of this study was that a neural network could predict LOS in patients with acute pancreatitis. The medical records of 195 patients admitted with acute pancreatitis were reviewed. A backpropagation neural network was developed to predict LOS >7 days. The network was trained on 156 randomly selected cases and tested on the remaining 39 cases. The neural network had the highest sensitivity (75%) for predicting LOS >7 days. Ranson criteria had the highest specificity (94%) for making this prediction. All methods incorrectly predicted LOS in two patients with severe acute pancreatitis who died early in their hospital course. An artificial neural network can predict LOS >7 days. The network and traditional prognostic indices were least accurate for predicting LOS in patients with severe acute pancreatitis who died early in their hospital course. The neural network has the advantage of making this prediction using admission data.

APACHE↗
PubMed1984

The three-dimensional architecture of the elastic-fiber network in canine hepatic portal system.

The architectural arrangement of the elastic-fiber network in the wall of canine hepatic portal veins was observed with the scanning electron microscope (SEM). Selective NaOH sonication digestion and autoclaving were used to expose and isolate the networks of elastic fibers from six selected regions of the hepatic portal vessels from seven healthy dogs. Elastic stains of adjacent segments prepared for light microscopy demonstrated that the elastic fibers were concentrated in two areas within the intact portal wall. The innermost area corresponded to the internal elastic lamina (IEL) of the tunica intima, the internal muscular layer, and the connective tissue layer of the tunica media. The second area was in the tunica adventitia. SEM specimens revealed two sleeves of elastic fiber networks which corresponded to the above regions. Small scattered bundles of radially oriented elastic fibers spanned the gap between the two sleeves. Each tunica had a different architectural arrangement of elastic fibers. The IEL had circumferentially oriented fibers which branched and anastomosed to form a continuous network on the innermost surface. The architecture of the IEL was the most variable between the different regions. The network of the IEL was the most "open" in the caudal region (splenic vein) and became "denser" toward the liver. The large elastic fibers in the tunica media were oriented at approximately right angles to the primary fibers of the IEL. These longitudinally oriented fibers anastomosed with adjacent longitudinal fibers to form a continuous network. In the tunica adventitia, thick, longitudinally oriented fibers of the continuous network fused together to form incomplete layers of fibers. The architecture of the elastic-fiber network in the canine hepatic portal vein was compared to that previously described in the systemic canine saphenous vein.

Animals↗
PubMed2002

Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network.

The vast majority (>95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized "gene expression network"--a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find that gene expression interactions form a robust, error-tolerant "scale-free" network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via "genome-engineering" may be a viable and practical approach to isolating gene function.

Animals↗
PubMed2004

Computational design of reduced metabolic networks.

Cellular functions are based on thousands of chemical reactions and transport processes, most of them being catalysed and regulated by specific proteins. Systematic gene knockouts have provided evidence that this complex reaction network possesses considerable redundancy, that is, alternative routes exist along which signals and metabolic fluxes may be directed to accomplish an identical output behaviour. This property is of particular importance in cases where parts of the reaction network are transiently or permanently impaired, for example, due to an infection or genetic alterations. Here we present a computational concept to determine enzyme-reduced metabolic networks that are still sufficient to accomplish a given set of cellular functions. Our approach consists of defining an objective function that expresses the compromise that has to be made between successive reduction of the network by omission of enzymes and its decreasing thermodynamic and kinetic feasibility. Optimisation of this objective function results in a linear mixed-integer program. With increasing weight given to the reduction of the number of enzymes, the total flux in the network increases and some of the reactions have to proceed in thermodynamically unfavourable directions. The approach was applied to two metabolic schemes: the energy and redox metabolism of red blood cells and the carbon metabolism of Methylobacterium extorquens. For these two example networks, we determined various variants of reduced networks differing in the number and types of disabled enzymes and disconnected reactions. Using a comprehensive kinetic model of the erythrocyte metabolism, we assess the kinetic feasibility of enzyme-reduced subnetworks. The number of enzymes predicted to be indispensable amounts to 14 (out of 28) for the erythrocyte scheme and 13 (out of 77) for the bacterium scheme, the largest group of enzymes predicted to be simultaneously dispensable amounts to 3 and 37 for these two systems. Our approach might contribute to identifying potential target enzymes for rational drug design, to rationalising gene-expression profiles of metabolic enzymes and to designing synthetic networks with highly specialised metabolic functions.

Computational Biology↗
PubMed2006

Arp2/3 complex-mediated actin polymerisation occurs on specific pre-existing networks in cells and requires spatial restriction to sustain functional lamellipod extension.

The classical Arp2/3-mediated dendritic network defines the cytoskeleton at the leading edge of crawling cells, and it is generally assumed that Arp2/3-mediated actin polymerization generates the force necessary to extend lamellipods. Our previous work suggested that successful lamellipod extension required not only free barbed ends for actin polymerization but also a proper ultrastructural organization of the cytoskeleton. To further explore the structural role of the Arp2/3 complex-mediated networks in lamellipod morphology and function, we performed a detailed analysis of the ultrastructure of the Arp2/3-mediated networks, using the WA domains of Scar and WASp to generate mislocalised Arp2/3 networks in vivo, and to reconstruct de novo Arp2/3-mediated actin nucleation and polymerization on extracted cytoskeletons. We present here evidence that spatially unrestricted Arp2/3-mediated networks are intrinsically three-dimensional and multilayered by nature and, as such, cannot sustain significant polarized extension. Furthermore, such networks polymerize only at preferred locations in extracted cells, corresponding to pre-existing Arp2/3 networks, suggesting that the specific molecular organization of the actin cytoskeleton, in terms of structure and/or biochemical composition, dictates the location of Arp2/3 complex-mediated actin polymerization. We propose that successful lamellipod extension depends not only on localized actin polymerization mediated through local signalling, but also on spatial restriction of the Arp2/3 complex-mediated nucleation of actin polymerization, both in terms of location within the cell and ultrastructural organization of the resulting network.

Actin-Related Protein 2-3 Complex↗
PubMed2005

Differential connections of the perirhinal and parahippocampal cortex with the orbital and medial prefrontal networks in macaque monkeys.

Previous anatomical studies indicate that the orbital and medial prefrontal cortex (OMPFC) of monkeys is organized into an "orbital" network, which appears to be related to feeding and reward, and a "medial" network, related to visceral control and emotion. In this study, we examined the connections of the orbital and medial prefrontal networks with the perirhinal (areas 35 and 36) and parahippocampal (areas TF and TH) cortex with anterograde and retrograde axonal tracers. The perirhinal cortex is reciprocally connected with orbital network areas Iapm, Iam, Ial, 13m, 13l, 12r, and 11l. In contrast, the parahippocampal cortex is reciprocally connected with the medial network, especially areas around the corpus callosum (areas 24a/b, caudal 32, and 25), and with area 11m. Projections from the parahippocampal cortex also extend to areas 10m, 10o, Iai, and rostral area 32, as well as to dorsolateral areas 9 and 46. In addition, both the perirhinal and parahippocampal cortex are reciprocally connected with areas that are intermediate between the orbital and medial networks (areas 13a, 13b, and 14c) and with the supracallosal area 24a'/b'. Outside the frontal cortex, the perirhinal cortex and the orbital prefrontal network are both interconnected with the ventral part of the temporal pole (TG), area TE and the ventral bank and fundus of the superior temporal sulcus (STS), and the dysgranular insula. In contrast, the parahippocampal cortex and the medial prefrontal network are connected with the dorsal TG, the rostral superior temporal gyrus (STG) and dorsal bank of STS, and the retrosplenial cortex.

Animals↗
PubMedDate not supplied

Cultured neuronal networks as environmental biosensors.

Contamination of water by toxins, either intentionally or unintentionally, is a growing concern for both military and civilian agencies and thus there is a need for systems capable of monitoring a wide range of natural and industrial toxicants. The EILATox-Oregon Workshop held in September 2002 provided an opportunity to test the capabilities of a prototype neuronal network-based biosensor with unknown contaminants in water samples. The biosensor is a portable device capable of recording the action potential activity from a network of mammalian neurons grown on glass microelectrode arrays. Changes in the action potential fi ring rate across the network are monitored to determine exposure to toxicants. A series of three neuronal networks derived from mice was used to test seven unknown samples. Two of these unknowns later were revealed to be blanks, to which the neuronal networks did not respond. Of the five remaining unknowns, a significant change in network activity was detected for four of the compounds at concentrations below a lethal level for humans: mercuric chloride, sodium arsenite, phosdrin and chlordimeform. These compounds--two heavy metals, an organophosphate and an insecticide--demonstrate the breadth of detection possible with neuronal networks. The results generated at the workshop show the promise of the neuronal network biosensor as an environmental detector but there is still considerable effort needed to produce a device suitable for routine environmental threat monitoring.

Action Potentials↗
PubMed2004

Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds.

The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.

Journal Article↗
PubMed2007

Erosion of interaction networks in reduced and degraded genomes.

Unlike eukaryotes, which often recruit duplicated genes into existing protein-protein interaction (PPI) networks, the low levels of gene duplication coupled with the high probability of lateral transfer of novel genes alters the manner in which PPI networks can evolve in bacteria. By inferring the PPIs present in the ancestor to contemporary Gammaproteobacteria, we were able to trace the changes in gene repertoires, and their consequences on PPI network evolution, in several bacterial lineages that have independently undergone reductions in genome size and genome contents. As genomes degrade, virtually all multi-partner proteins have lost interactors; however, the overall average number of connections increases due to the preferential elimination of proteins that interact with only one other protein partner. We also studied the effect of lateral gene transfer on PPI network evolution by analyzing the connectivity of genes that have been gained along the Escherichia coli lineage, as well as those acquired genes subsequently silenced in Shigella flexneri, since diverging from the gammaproteobacterial ancestor. The situation in PPI networks, in which newly acquired genes preferentially attach to the hubs of the network, contrasts that observed in metabolic networks, which evolve by the peripheral gain and loss of genes, and in regulatory networks, in which high connectivity increases the propensity of loss.

Gammaproteobacteria↗
PubMed2006

Protein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster: large-scale organization and robustness.

High-throughput screens have begun to reveal protein interaction networks in several organisms. To understand the general properties of these protein interaction networks, a systematic analysis of topological structure and robustness was performed on the protein interaction networks of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. It shows that the three protein interaction networks have a scale-free and high-degree clustering nature as the consequence of their hierarchical organization. It also shows that they have the small-world property with similar diameter at 4-5. Evaluation of the consequences of random removal of both proteins and interactions from the protein interaction networks suggests their high degree of robustness. Simulation of a protein's removal shows that the protein interaction network's error tolerance is accompanied by attack vulnerability. These fundamental analyses of the networks might serve as a starting point for further exploring complex biological networks and the coming research of "systems biology".

Algorithms↗
PubMed2006

Similarity networks of protein binding sites.

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all-against-all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale-free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular.

Animals↗
PubMed1994

Polymer networks with grafted cell adhesion peptides for highly biospecific cell adhesive substrates.

Polymer networks of poly(ethylene glycol) (PEG) in densely cross-linked matrices of acrylic acid (AA) and trimethylolpropane triacrylate were synthesized as biospecific cell adhesive substrates. Networks grafted with synthetic adhesion peptides produced substrates to investigate long-term, receptor-mediated cell/surface interactions, without nonspecific protein adsorption producing spurious adhesion signals. PEG rendered the networks very resistant to cell adhesion in vitro, and AA provided reactive carboxyl moieties for N-terminal grafting of peptides. Networks with higher mass fractions of AA had greater background cell adhesion, which diminished with higher mass fractions of PEG such that complete resistance to cell adhesion could be obtained. Networks grafted with inactive control peptides (GRGES or no peptide) remained completely cell nonadhesive in the presence of serum or even when preincubated with adhesion proteins, while networks grafted with bioadhesive peptides (GRGDS, GYIGSRY, or GREDVY) supported morphologically complete fibroblast adhesion. The amount of AA in the network readily controlled the amount of incorporated peptide. These networks may be suitable as analytical tools specifically to investigate long-term cell/substrate interactions in the presence of serum, yet without non-specific protein adsorption producing adhesion signals other than those immobilized for study.

Acrylates↗
PubMed2002

Modeling DNA sequence-based cis-regulatory gene networks.

Gene network analysis requires computationally based models which represent the functional architecture of regulatory interactions, and which provide directly testable predictions. The type of model that is useful is constrained by the particular features of developmentally active cis-regulatory systems. These systems function by processing diverse regulatory inputs, generating novel regulatory outputs. A computational model which explicitly accommodates this basic concept was developed earlier for the cis-regulatory system of the endo16 gene of the sea urchin. This model represents the genetically mandated logic functions that the system executes, but also shows how time-varying kinetic inputs are processed in different circumstances into particular kinetic outputs. The same basic design features can be utilized to construct models that connect the large number of cis-regulatory elements constituting developmental gene networks. The ultimate aim of the network models discussed here is to represent the regulatory relationships among the genomic control systems of the genes in the network, and to state their functional meaning. The target site sequences of the cis-regulatory elements of these genes constitute the physical basis of the network architecture. Useful models for developmental regulatory networks must represent the genetic logic by which the system operates, but must also be capable of explaining the real time dynamics of cis-regulatory response as kinetic input and output data become available. Most importantly, however, such models must display in a direct and transparent manner fundamental network design features such as intra- and intercellular feedback circuitry; the sources of parallel inputs into each cis-regulatory element; gene battery organization; and use of repressive spatial inputs in specification and boundary formation. Successful network models lead to direct tests of key architectural features by targeted cis-regulatory analysis.

DNA↗
PubMed1993

A model for the evolution of networks of genes.

An organism persists through the activity of structural genes, which is co-ordinated by clusters of coupled regulatory genes. During evolution, changes of coupling within a cluster can increase the reliability with which its structural genes perform a task. To study the evolution of coupling, we have simulated and analyzed a stochastic model for a simple problem. The assumptions of the model are these: A network of regulatory genes co-ordinates the synthesis of four structural proteins, which associate in distinct heterodimers that form a heterotetramer. Mutation in cis-regulatory regions produces transitions among 64 types of network. In a population, each network reproduces in proportion to its fitness, which depends on its probability (reliability) of synthesizing the tetramer. Fitness-dependent attrition keeps the size of the population constant. Regulatory genes occur in a sequence of levels; each level is associated with a different family of transcription factors. The following results emerge: Because different messengers within a family can give networks with the same connectivity, the 64 types of networks cluster into eight equivalence classes. During evolution with a low mutation rate, high-fitness classes can be approached through various paths on a fitness landscape. With a higher mutation rate, networks remain more uniformly distributed among the 64 types, and lower-fitness networks remain preponderant. An initially homogeneous population becomes more heterogeneous through mutation, but selection according to fitness later reduces its diversity. During this process the dispersion of the population over the possible networks increases, then decreases as the population approaches a unique steady state.

Animals↗
PubMed1999

Population and quantitative genetics of regulatory networks.

I evolved boolean regulatory networks in a computer simulation. I varied mutation, recombination, the size of the network, and the number of connections per node. I measured the performance of networks and the heritability and epistasis of genetic effects. Networks of intermediate connectivity performed best. The distinction between metabolic and quantitative genetic additivity explained some of the variation in performance. Metabolic additivity describes the interaction between changes in a single network, whereas quantitative genetic additivity measures the consistency of phenotypic effect caused by gene substitution in randomly chosen members of the population. I analysed metabolic additivity by the distribution of epistatic effects of pairs of mutations in individual networks. I measured quantitative genetic additivity by heritability. Highly connected networks had greater metabolic additivity for perturbations to individual networks, but had lower additivity when measured by the average effect of a gene substitution (heritability). The lower heritability of highly connected nets appeared to reduce the effectiveness of recombination in searching evolutionary space.

Animals↗
PubMed2005

Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination.

Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points. We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.

Algorithms↗
PubMed2007

Chaperones as parts of cellular networks.

The most important interactions between cellular molecules have a high affinity, are unique and specific, and require a network approach for a detailed description. Molecular chaperones usually have many first and second neighbors in protein-protein interaction networks and they play a prominent role in signaling and transcriptional regulatory networks of the cell. Chaperones may uncouple protein, signaling, membranous, organellar and transcriptional networks during stress, which gives an additional protection for the cell at the network-level. Recent advances uncovered that chaperones act as genetic buffers stabilizing the phenotype of various cells and organisms. This chaperone effect on the emergent properties of cellular networks may be generalized to proteins having a specific, central position and low affinity, weak links in protein networks. Cellular networks are preferentially remodeled in various diseases and aging, which may help us to design novel therapeutic and anti-aging strategies.

Animals↗
PubMed1995

A ternary logic model for recurrent neuromime networks with delay.

In contrast to popular recurrent artificial neural network (RANN) models, biological neural networks have unsymmetric structures and incorporate significant delays as a result of axonal propagation. Consequently, biologically inspired neural network models are more accurately described by nonlinear differential-delay equations rather than nonlinear ordinary differential equations (ODEs), and the standard techniques for studying the dynamics of RANNs are wholly inadequate for these models. This paper develops a ternary-logic based method for analyzing these networks. Key to the technique is the realization that a nonzero delay produces a bounded stability region. This result significantly simplifies the construction of sufficient conditions for characterizing the network equilibria. If the network gain is large enough, each equilibrium can be classified as either asymptotically stable or unstable. To illustrate the analysis technique, the swim central pattern generator (CPG) of the sea slug Tritonia diomedea is examined. For wide range of reasonable parameter values, the ternary analysis shows that none of the network equilibria are stable, and thus the network must oscillate. The results show that complex synaptic dynamics are not necessary for pattern generation.

Animals↗