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At least 127 records · Page 7Linked to original sources
PubMed2000

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised 'associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.

Algorithms↗
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↗
PubMed2006

Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back.

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.

Action Potentials↗
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↗
PubMed1995

Inner connections within domain of dementia praecox: role of supervisory mental processes in schizophrenia.

Kraepelin's conclusion that there were underlying common features justifying the amalgamation of catatonia, hebephrenia and paranoia hallucinatoria to form a single illness is confirmed by factor analytic studies delineating the various dimensions of schizophrenic psychopathology. Neuropsychological studies reveal that the three cardinal dimensions reflect disorder of the supervisory mental processes responsible for initiation, selection and monitoring of self-generated mental activity. Brain-imaging studies indicate that the underlying neuropathology entails disordered functional connectivity within the neural networks in multimodal association cortex that are the substrate of the supervisory mental processes, consistent with Kraepelin's own speculation about the essential nature of the condition.

Humans↗
PubMed2006

Asymptotic states and topological structure of an activation-deactivation chemical network.

The influence of the topology on the asymptotic states of a network of interacting chemical species has been studied by simulating its time evolution. Random and scale-free networks have been designed to support relevant features of activation-deactivation reactions networks (mapping signal transduction networks) and the system of ordinary differential equations associated to the dynamics has been numerically solved. We analysed stationary states of the dynamics as a function of the network's connectivity and of the distribution of the chemical species on the network; we found important differences between the two topologies in the regime of low connectivity. In particular, only for low connected scale-free networks it is possible to find zero activity patterns as stationary states of the dynamics which work as signal off-states. Asymptotic features of random and scale-free networks become similar as the connectivity increases.

Animals↗
PubMed2002

Left dorso-lateral repetitive transcranial magnetic stimulation affects cortical excitability and functional connectivity, but does not impair cognition in major depression.

PURPOSE: Transcranial magnetic stimulation (TMS) has been used for over a decade to investigate cortical function. More recently, it has been employed to treat conditions such as major depression. This study was designed to explore the effects of differential treatment parameters, such as stimulation frequency. In addition, the data were examined to determine whether a change in connectivity occurred following TMS. METHOD: Fifteen patients with major depression were entered into a combined imaging and treatment experiment with single photon emission computed tomography (SPECT) and repetitive transcranial magnetic stimulation (rTMS) over left dorso-lateral prefrontal cortex (DLPFC). Brain perfusion during a verbal fluency task was compared between pre- and poststimulation conditions. Patients were then treated with 80% of motor threshold for a total of 10 days, using 5000 stimuli at 5, 10 or 20 Hz. Tests of cortical excitability and neuropsychological tests were done throughout the trial. FINDINGS: Patients generally improved with treatment. There was no perceptible difference between stimulation frequencies, which may have reflected low study power. An increase in rostral anterior cingulate activation after the treatment day was associated with increased functional connectivity in the dorso-lateral frontal loop on the left and the limbic loop on both sides. No noticeable deterioration in neuropsychological function was observed. CONCLUSION: TMS at the stimulation frequencies used seems to be safe over a course of 5000 stimuli. It appears to have an activating effect in anterior limbic structures and increase functional connectivity in the neuroanatomical networks under the stimulation coil within an hour of stimulation.

Adult↗
PubMed2003

Protein kinase C and ERK involvement in dendritic spine plasticity in cultured rodent hippocampal neurons.

The roles of protein kinase C and the MAP-kinase extracellular receptor kinase in structural changes associated with long-term potentiation of network activity were examined in cultured hippocampal neurons. A brief exposure to a conditioning medium that favours activation of the N-methyl-d-aspartate receptor caused a rapid and specific increase in staining of neurons for the phosphorylated form of extracellular receptor kinase as well as of cyclic AMP response element binding protein. Exposure of the cultures to the conditioning medium was followed by a protein synthesis-dependent formation of novel dendritic spines. An extracellular receptor kinase antagonist PD98059 blocked the phosphorylated form of extracellular receptor kinase response and the formation of novel spines. A selective protein kinase C agonist, phorbol 12-myristate 13-acetate, caused activation of extracellular receptor kinase and formation of novel spines. The protein kinase C antagonist GF109203x blocked the phosphorylated form of extracellular receptor kinase response and the subsequent spine formation by phorbol 12-myristate 13-acetate. Both the conditioning medium and phorbol 12-myristate 13-acetate caused a delayed increase in mean amplitude of miniature excitatory postsynaptic currents recorded in the hippocampal neurons. These results indicate that activation of extracellular receptor kinase mediates the effect of a conditioning protocol on the formation of dendritic spines. The formation of novel spines was associated with long-term increase in network activity and functional synaptic connectivity among the cultured neurons.

Activating Transcription Factor 1↗
PubMed2005

Finding biologically relevant protein domain interactions: conserved binding mode analysis.

Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.

Algorithms↗
PubMed2004

Computational alanine scanning of protein-protein interfaces.

Protein-protein interactions are key components of all signal transduction processes, so methods to alter these interactions promise to become important tools in dissecting function of connectivities in these networks. We have developed a fast computational approach for the prediction of energetically important amino acid residues in protein-protein interfaces (available at http://robetta.bakerlab.org/alaninescan), which we, following Peter Kollman, have termed "computational alanine scanning." The input consists of a three-dimensional structure of a protein-protein complex; output is a list of "hot spots," or amino acid side chains that are predicted to significantly destabilize the interface when mutated to alanine, analogous to the results of experimental alanine-scanning mutagenesis. 79% of hot spots and 68% of neutral residues were correctly predicted in a test of 233 mutations in 19 protein-protein complexes. A single interface can be analyzed in minutes. The computational methodology has been validated by the successful design of protein interfaces with new specificity and activity, and has yielded new insights into the mechanisms of receptor specificity and promiscuity in biological systems.

Alanine↗
PubMed1992

Physiological function of granule cells: a hypothesis.

In this chapter we review the physiological properties of granule cells in vivo and in vitro. We conclude from the literature that in intact rats granule cells fire rhythmic bursts of action potentials concurrent with exploration-associated theta waves. The population discharge of granule cells coincides with the maximum probability of firing of CA3 pyramidal cells on the positive phase of focally recorded theta waves. During consummatory behaviors, immobility and anesthesia the firing rate of granule cells substantially decreases. We propose that the conjoint activity of the discharging CA3 cells and tetanization of these same cells by mossy fibers during exploratory (theta) behavior will temporarily increase synaptic efficacy among the active CA3 neurons even after the termination of exploration. As a result, the very same CA3 cells that carried information during exploration now become the burst-initiator cells of the sharp-wave associated population bursts during consummatory behaviors and sleep. The creation of new burst-initiator neurons is hypothesized to be essential for memory trace formation. From this perspective the main physiological function of granule cells is to 'tetanize' CA3 pyramidal neurons during exploratory behaviors and induce a meaningful reorganization of the functional connectivity of the CA3 network.

Animals↗
PubMedDate not supplied

[Effect of ethanol on the interaction of hippocampal neurons in tissue culture].

Background unit activity and that evoked by ethanol of neighbouring neurones in hippocampal tissue culture of 9-19 div was studied by means of cross-correlational analysis. The results obtained point to a possibility of existence of complex neuronal networks consolidated by functional synaptic connections, even in such a simple model system as neuronal tissue culture. Excitatory interactions of neighbouring neurones were more sensitive to ethanol than the inhibitory ones. The effect of ethanol administration consisted mainly in a weakening of correlational connections. Such changes are supposed to be the basis for different coordination disorders in the work of brain structures observed during chronic alcoholic intoxication.

Animals↗
PubMedDate not supplied

[Three-dimentional organization of synapses and astroglia in the hippocampus of rats and ground squirrels: new structural and functional paradigms of the synapse function].

The literature data and our own data on the synaptic plasticity and remodeling of synaptic organelles in the central nervous system are reviewed. Modern techniques of laser scanning confocal microscopy and serial thin sectioning for in vivo and in vitro studies of dendritic spines, including the relationship between morphological changes and the efficacy of synaptic transmission, are discussed using, in particular, a model of long-term potentiation. The organization of dendritic spines and postsynaptic densities of different categories as well as the role of filopodia in spine genesis were analyzed. It was shown that the method of serial ultrathin sections is the most effective for unbiased quantitative stereological analysis and 3D reconstructions. By using the refined method of serial ultrathin sections with subsequent three-dimensional reconstructions, the presence of giant mitochondria in hippocampal neuronal dendrites was demonstrated. It was shown that smooth endoplasmic reticulum forms a unified continuum with the outer membrane of the mitochondrial envelope within dendrites. It was suggested that this continuum provides calcium tunneling, which makes possible intracellular signal transduction during synaptic transmission. Evidence is presented indicating the presence of gap junctions ("electrical synapses") in the synapses of mammalian brain, as well as between glial processes, and between glial cells and neurons. Our data and the data of other authors show that glial cell processes form a structural and functional glial network, which modulates the functioning of the neuronal network. The connection of dendritic spines with the glial network is shown on 3D reconstructions by analyzing the neuropil volume in CA1 hippocampal area of ground squirrels in three functional states: normothermia, provoked arousal, and hibernation when brain temperature falls below 6 degrees C. The own data of the authors are discussed indicating the formation of more than five presynaptic boutons (multiple synapses) on both CA1 mushroom-like dendritic spines and CA3 thorny excrescences. On the basis of the analysis, new ideas of the organization and functioning of synapses were suggested.

Animals↗
PubMed1997

A functional magnetic resonance imaging study of episodic memory.

We have used whole brain fMRI and canonical variate analysis to investigate the anatomical location of brain activity subserving episodic memory encoding and recognition. Recognition of previously presented words did not specifically activate right prefrontal areas although right-sided activations were seen during transitions between the target and reference phases of the paradigm. Encoding was associated with left sided prefrontal activation, but this was within Broca's area rather than dorsolateral prefrontal cortex. The temporal resolution of fMRI, combined with analysis of the apparent functional connectivity of activations observed, revealed activity within complex networks each functionally specialized for the demands of particular aspects of a cognitive task.

Adult↗
PubMed2005

A general framework for weighted gene co-expression network analysis.

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.

Journal Article↗
PubMed2005

Transcriptional regulation and metabolism.

Understanding organisms from a systems perspective is essential for predicting cellular behaviour as well as designing gene-metabolic circuits for novel functions. The structure, dynamics and interactions of cellular networks are all vital components of systems biology. To facilitate investigation of these aspects, we have developed an integrative technique called network component analysis, which utilizes mRNA expression and transcriptional network connectivity to determine network component dynamics, functions and interactions. This approach has been applied to elucidate transcription factor dynamics in Saccharomyces cerevisiae cell-cycle regulation, detect cross-talks in Escherichia coli two-component signalling pathways, and characterize E. coli carbon source transition. An ultimate test of system-wide understanding is the ability to design and construct novel gene-metabolic circuits. To this end, artificial feedback regulation, cell-cell communication and oscillatory circuits have been constructed, which demonstrate the design principles of gene-metabolic regulation in the cell.

Escherichia coli↗
PubMed2005

Functional connectivity with anterior cingulate and orbitofrontal cortices during decision-making.

Recent neuroscience research is beginning to discover the brain regions involved in decision-making under uncertainty, but little is known about whether or how these regions functionally interact with each other. Here, we used event-related functional magnetic resonance imaging to examine both changes in overall activity and changes in functional connectivity during risk-taking. Results showed that choosing high-risk over low-risk decisions was associated with increased activity in both anterior cingulate and orbitofrontal cortices. Connectivity analyses revealed that largely distinct, but somewhat overlapping, cortical and subcortical regions exhibited significant functional connectivity with anterior cingulate and orbitofrontal cortices. Additionally, connectivity with the anterior cingulate in some regions, including the orbitofrontal cortex and nucleus accumbens, was modulated by the decision participants chose. These findings (1) elucidate large networks of brain regions that are functionally connected with both anterior cingulate and orbitofrontal cortices during decision-making and (2) demonstrate that the roles of orbitofrontal and anterior cingulate cortices can be functionally differentiated by examining patterns of connectivity.

Adult↗