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At least 1,657 records · Page 92Linked to original sources
PubMed1999

Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering.

Rational metabolic engineering requires powerful theoretical methods such as pathway analysis, in which the topology of metabolic networks is considered. All metabolic capabilities in steady states are composed of elementary flux modes, which are minimal sets of enzymes that can each generate valid steady states. The modes of the fructose-2,6-bisphosphate cycle, the combined tricarboxylic-acid-glyoxylate-shunt system and tryptophan synthesis are used here for illustration. This approach can be used for many biotechnological applications such as increasing the yield of a product, channelling a product into desired pathways and in functional reconstruction from genomic data.

Biochemistry↗
PubMed2006

Growth of cortical neuronal network in vitro: modeling and analysis.

We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed.

Action Potentials↗
PubMed1994

Neural networks as a new method for EEG analysis. A basic introduction.

This paper discusses the general usability of artificial neural networks for the analysis of EEG data. The advantages and drawbacks of this new technology are compared, especially from the perspective of medical computer science. Furthermore this text clarifies the fundamental principles of neural networks, the relations to the biological model and how they compare to classical evaluation methods.

Electroencephalography↗
PubMed2004

Integrated environmental assessment of the Mid-Atlantic region with analytical network process.

A decision analysis method for integrating environmental indicators was developed. This was a combination of Principal Component Analysis (PCA) and the Analytic Network Process (ANP). Being able to take into account the interdependency among variables, the method was capable of ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic Region of the United States, we were able to point out areas which were in relatively poor condition and/or vulnerable to future deterioration regarding various environmental aspects. The method offered an easy and comprehensive way to combine the strengths of conventional multivariate statistics (PCA) and decision-making science tool (ANP) for integrated environmental assessment.

Conservation of Natural Resources↗
PubMed2005

Collection and preparation of sensor network data to support modeling and analysis of outpatient clinics.

Simulation studies of outpatient clinics often involve significant data collection challenges. We describe an approach for data collection using sensor networks which facilitates the collection of a large volume of very detailed patient flow data through healthcare clinics. Such data requires extensive preprocessing before it is ready for analysis. We present a general data preparation framework for sensor network generated data with particular emphasis on the creation and analysis of patient path strings. Several examples of the analysis of sensor network data are also presented. Our approach has been used in two large outpatient clinics in the United States.

Ambulatory Care Facilities↗
PubMed1996

Analysis of nonlinear properties of immune network reactions.

On the basis of biochemical reaction dynamics, the temporal behavior of the immune network system was analyzed theoretically to promote the analysis of quantitative changes in the reactions of immune disorders and organ substitution. The idiotype immune network reaction system was expressed by 64 nonlinear differential equations that comprised four kinds of antibodies and B-cell subpopulations. All four kinds of antibodies decreased rapidly. With the progress of the reactions, they have increased gradually. The single and double bound antibodies increased rapidly from the onset of the reaction. The single-bound antibodies did not show a definite increase after the rapid increasing phase. The antibody-antibody complex increased parallel with the double bound antibodies. The effects of rate constant expand to all the immune complexes in the network system. The double bound antibodies and antibody-antibody complexes were oscillatory functions of a given antibody. Therefore, the idiotypic immune network system must be a chaotic one. The present theoretical method is available to evaluate the total ability of immune reaction system that operates as a network system.

Antibodies↗
PubMed2004

Finding community structure in very large networks.

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

Journal Article↗
PubMed1999

Classification and sensitivity analysis of a proposed primary metabolic reaction network for Streptomyces lividans.

Constructing a metabolic flux analysis model is in principle fairly straightforward. However, there are a number of mathematical pitfalls. First, dependent reactions are a recurring problem and, second, the choice of reactions to measure may not be straight-forward. A method for systematic identification of dependent reactions and a thorough reactions classification procedure is presented. A well-defined stoichiometric presentation can provide significant insight into metabolic control mechanisms. Two methods for analyzing the impact of perturbations in the measured fluxes on the remaining metabolism and the impact of changes in biomass composition on the calculated metabolic reactions is developed. A metabolic reaction network proposed for Streptomyces lividans is used as an example to demonstrate the outlined analysis. It is concluded that oxygen utilization has the highest influence on the pathway fluxes and that realistic perturbations in the biomass composition do not significantly alter the flux patterns.

Homeostasis↗
PubMed2006

Artificial neural networks for qualitative and quantitative analysis of target proteins with polymerized liposome vesicles.

We investigate the feasibility of using the luminescence response of polymerized liposomes incorporating ethylenediaminetetraacetate europium(III) (EDTA-Eu(3+)) for monitoring protein concentrations in aqueous media. Quantitative analysis is based on the linear relationship between the luminescence enhancement of the lanthanide ion and protein concentration. Analytical figures of merit are presented for carbonic anhydrase, human serum albumin, gamma-globulins, and thermolysin. Qualitative analysis is based on the luminescence lifetime of the liposome sensor. This parameter, which follows well-behaved single exponential decays and provides characteristic values for each of the four studied proteins, demonstrates the selective potential for protein identification. Then partial least squares-1 and artificial neural networks are compared toward the quantitative and qualitative analysis of human serum albumin and carbonic anhydrase in binary mixtures without previous separation at the concentration levels found in aqueous humor.

Deuterium Oxide↗
PubMed2001

Complementarity of ecological goal functions.

This paper summarizes, in the framework of network environ analysis, a set of analyses of energy-matter flow and storage in steady-state systems. The network perspective is used to codify and unify ten ecological orientors or extremal principles: maximum power (Lotka), maximum storage (Jørgensen-Mejer), maximum empower and emergy (Odum), maximum ascendency (Ulanowicz), maximum dissipation (Schneider-Kay), maximum cycling (Morowitz), maximum residence time (Cheslak-Lamarra), minimum specific dissipation (Onsager, Prigogine), and minimum empower to exergy ratio (Bastianoni-Marchettini). We show that, seen in this framework, these seemingly disparate extrema are all mutually consistent, suggesting a common pattern for ecosystem development. This pattern unfolds in the network organization of systems.

Animals↗
PubMed2005

A theoretical framework for specificity in cell signaling.

Different cellular signal transduction pathways are often interconnected, so that the potential for undesirable crosstalk between pathways exists. Nevertheless, signaling networks have evolved that maintain specificity from signal to cellular response. Here, we develop a framework for the analysis of networks containing two or more interconnected signaling pathways. We define two properties, specificity and fidelity, that all pathways in a network must possess in order to avoid paradoxical situations where one pathway activates another pathway's output, or responds to another pathway's input, more than its own. In unembellished networks that share components, it is impossible for all pathways to have both mutual specificity and mutual fidelity. However, inclusion of either of two related insulating mechanisms--compartmentalization or the action of a scaffold protein--allows both properties to be achieved, provided deactivation rates are fast compared to exchange rates.

Algorithms↗
PubMed2002

Modeling population access to New Zealand public hospitals.

This paper demonstrates a method for estimating the geographical accessibility of public hospitals. Cost path analysis was used to determine the minimum travel time and distance to the closest hospital via a road network. This analysis was applied to 38,000 census enumeration district centroids in New Zealand allowing geographical access to be linked to local populations. Average time and distance statistics have been calculated for local populations by modeling the total travel of a population if everybody visited a hospital once. These types of statistics can be generated for different population groups and enable comparisons to be made between regions. This study has shown that the northern and southern parts of New Zealand have high average travel times to hospital services.

Journal Article↗
PubMed1997

Noradrenergic regulation of cyclic GnRH secretion.

The GnRH cells represent the final output neurones of an integrated neuronal network used by the brain to generate pulsatile LH secretion from the pituitary gland. Changes in LH secretion profile throughout the ovarian cycle, including the preovulatory LH surge, result principally from alterations in the output of this GnRH network and it has been a key goal of many neurobiologists to elucidate the components and nature of this network. This review documents recent progress in understanding the role of noradrenaline within the GnRH network and highlights and explains its 'enabling' or permissive characteristics. Network behaviour analysis suggests that noradrenaline should be considered as a permissive agent promoting high output states of the GnRH network. On the basis of recent molecular and neuroanatomical data, it is proposed that oestrogen influences brainstem noradrenergic neurones specifically within the nucleus tractus solitarius to facilitate synaptic transmission within the GnRH network. In this manner, noradrenaline is likely to play a role in bringing about the increased GnRH messenger RNA expression and secretion necessary for ovulation.

Animals↗
PubMed2004

Qualitative simulation of genetic regulatory networks using piecewise-linear models.

In order to cope with the large amounts of data that have become available in genomics, mathematical tools for the analysis of networks of interactions between genes, proteins, and other molecules are indispensable. We present a method for the qualitative simulation of genetic regulatory networks, based on a class of piecewise-linear (PL) differential equations that has been well-studied in mathematical biology. The simulation method is well-adapted to state-of-the-art measurement techniques in genomics, which often provide qualitative and coarse-grained descriptions of genetic regulatory networks. Given a qualitative model of a genetic regulatory network, consisting of a system of PL differential equations and inequality constraints on the parameter values, the method produces a graph of qualitative states and transitions between qualitative states, summarizing the qualitative dynamics of the system. The qualitative simulation method has been implemented in Java in the computer tool Genetic Network Analyzer.

Computer Simulation↗
PubMed2000

Use of adult day services and home health care services by older adults: a comparative analysis.

This study compares adult day services (ADS) and home health care (HHC) users on the indicators of personal characteristics, physical health, mental health and social network contacts. Analysis includes all 62 reported ADS users and a random sample of 91 HHC users from the Longitudinal Study of Aging. Results indicate that ADS users are younger, have greater cognitive impairment, need more supervisory assistance with activities of daily living (ADLs), and have more social contacts than HHC users. The findings also underscore the importance of emphasizing the functional and cognitive limitations in differentiating home- and community-based services. Instead of exclusively focusing on physical needs, authors suggest strategies that ensure integrated care models to address physical, mental and social needs of elders in both settings.

Activities of Daily Living↗
PubMed1997

Neural networks as a prognostic tool for patients with non-small cell carcinoma of the lung.

Patients with non-small cell carcinoma of the lung (NSCLC) have a poor prognosis (64 and 41% survival rates in Stages I and II). It is currently not possible to predict which patients with Stage I or II NSCLC will survive the disease. Sixty-seven patients with NSCLC, including 49 patients with Stage I NSCLC and 18 with Stage II disease (11 with squamous cell carcinomas, 35 with adenocarcinomas, and 21 with large cell carcinomas) were treated with lobectomy and followed for a minimum of 5 years. The tumors were studied with DNA flow cytometry and quantitative immunocytochemical studies for proliferation cell nuclear antigen, p53 protein, and MIB-1. The data were analyzed with backpropagation neural networks, univariate analysis of variance, the Kaplan-Meier survival method, and Cox proportional hazards model. The dependent variables were "free of disease" and "recurrence or dead from disease." Twenty neural network models were trained, using all cases but one, after 1883 to 2000 training cycles. At 5 years, 30 patients were free of disease and 37 were dead or had recurrence. Proliferating cell nuclear antigen was the only statistically significant prognostic factor by univariate analysis of variance and Cox proportional hazards analysis. The S phase was statistically significant by univariate analysis of variance (P <.05). All of the 20 models classified the test cases correctly. Study with backpropagation neural networks using multiple prognostic features from patients with NSCLC suggests that this technology might be useful for prediction of survival. This preliminary study must be validated with data from a larger group of patients with NSCLC before its clinical adequacy is established.

Adenocarcinoma↗
PubMed2006

On the consistency of the blocked neural network estimator in time series analysis.

We describe a nonlinear regression problem, where the regression functions have an additive structure and the dependent variable is a one-dimensional time series. Multivariate time series with unknown time delay operators are used as independent variables. By fitting a feedforward neural network with block structure to the data, we estimated the additive regression function and, parallel to this, the time lags. We present the consistency proof of neural network weights estimator and the time lag estimator independently from each other. In the practical part of the article, we present the useful feature of blocked neural networks to estimate the relevance measures of each input variable in a simple way. Furthermore, we propose an approach to solve the well-known variable selection problem for the class of nonlinear multivariate beta-mixing time series models considered here. Finally, we apply the methodology to an artificial example.

Animals↗
PubMed2000

A comparison of the yeast and rabbit 80 S ribosome reveals the topology of the nascent chain exit tunnel, inter-subunit bridges and mammalian rRNA expansion segments.

Protein synthesis in eukaryotes is mediated by both cytoplasmic and membrane-bound ribosomes. During the co-translational translocation of secretory and membrane proteins, eukaryotic ribosomes dock with the protein conducting channel of the endoplasmic reticulum. An understanding of these processes will require the detailed structure of a eukaryotic ribosome. To this end, we have compared the three-dimensional structures of yeast and rabbit ribosomes at 24 A resolution. In general, we find that the active sites for protein synthesis and translocation have been highly conserved. It is interesting that a channel was visualized in the neck of the small subunit whose entrance is formed by a deep groove. By analogy with the prokaryotic small subunit, this channel may provide a conserved portal through which mRNA is threaded into the decoding center. In addition, both the small and large subunits are built around a dense tubular network. Our analysis further suggests that the nascent chain exit tunnel and the docking surface for the endoplasmic reticulum channel are formed by this network. We surmise that many of these features correspond to rRNA, based on biochemical and structural data. Ribosomal function is critically dependent on the specific association of small and large subunits. Our analysis of eukaryotic ribosomes reveals four conserved inter-subunit bridges with a geometry similar to that found in prokaryotes. In particular, a double-bridge connects the small subunit platform with the interface canyon on the large subunit. Moreover, a novel bridge is formed between the platform and the base of the L1 domain. Finally, size differences between mammalian and yeast large subunit rRNAs have been correlated with five expansion segments that form two large spines and three extended fingers. Overall, we find that expansion segments within the large subunit rRNA have been incorporated at positions distinct from the active sites for protein synthesis and translocation.

Animals↗