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At least 73 records · Page 4Linked to original sources
PubMed2003

Effect of physiological temperature on the mechanical properties and network structure of biodegradable poly(propylene fumarate)-based networks.

Poly(propylene fumarate) (PPF)-based networks have exhibited increases in mechanical properties during their initial stages of degradation. This study was designed to investigate whether physiological temperatures are the source of this reinforcing behavior by influencing the formation of additional crosslinks within the network. Utilizing a model PPF network formed with the crosslinking agent poly(propylene fumarate)-diacrylate (PPF-DA), cylindrical specimens were stored in an inert environment and conditioned at -20 and 37 degrees C while their mechanical properties and network structure were monitored over a six week period. The PPF/PPF-DA specimens exposed to physiological temperatures showed an increase in compressive modulus from 1674 +/- 88 to 2059 +/- 75 MPa. The double bond conversion improved as well, from 64 +/- 1 to 70 +/- 1%, indicating that crosslinks were being formed in the network. The additional reactivity occurred exclusively with unreacted fumarate bonds. PPF/PPF-DA networks stored at -20 degrees C showed no changes in mechanical properties; however, they increased when subsequently conditioned at 37 degrees C. The results were used to explain that PPF-based networks undergo a biphasic degradation behavior due to the competing hydrolytic degradation and thermal induced crosslinking. In addition, heat treating the networks at higher temperatures can be utilized as a means to further reinforce PPF-based materials.

Biocompatible Materials↗
PubMed2002

A functional asymmetry in the Leech Heartbeat Timing Network is revealed by driving the network across various cycle periods.

We tested predictions of a computational model (Hill et al., 2002) of the leech heartbeat timing network. The timing network consists of two segmental oscillators located in the third (G3) and fourth (G4) segmental ganglia. Each oscillator consists of two reciprocally inhibitory oscillator interneurons along with the coordinating interneuron fibers that link them. In the model, the network was driven to cycle periods around the normal period of the network by repeatedly stimulating one of the paired oscillator interneurons in G3 or G4. Here we replicate these experiments in the biological system. The model predicts that the G3 and G4 oscillators can entrain the timing network to periods faster but not slower than the inherent period of the nondriven ("follower") oscillator and that they can do so symmetrically. The biological system can be driven to periods both faster (such that the driven oscillator leads in phase) and slower (such that the driven oscillator lags in phase) than the inherent period of the timing network. Although both oscillators can entrain the network, the G4 oscillator does so over a narrower range of periods. Two differences between the assumptions of the model and the properties of the biological network, spike frequency adaptation in coordinating interneurons and asymmetry in the connections from the oscillator interneurons to the coordinating interneurons, may account for these discrepancies. Individual coordinating interneurons were also able to entrain the oscillators but with little effect of the phase relationship between the oscillators, suggesting that phase relations are determined by properties inherent to the oscillator interneurons.

Action Potentials↗
PubMed2006

[Military telemedicine: a network of networks].

Military telemedicine is a form of collaborative medicine based on the use of communication and information networks. It is more a network of networks than of independent systems. It comprises electronic medical files, epidemiological networks, and surgical and medical databases. Each system must be able to communicate with the others, thereby enabling the development of remote consultation, expertise and assistance. This requires networking between the army, the navy, and the air force communication networks, especially during special operations conducted abroad. We must also develop interoperability with systems in other countries, and with the French civilian health service. This means respecting the general rules governing these networks. The military health network is unique, in that it focuses on battlefield injuries. In addition, the French military health service operates under a single headquarters, governing nurses, paramedics and physicians.

Military Medicine↗
PubMed2002

Neural network based optimal routing algorithm for communication networks.

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known asflow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.

Algorithms↗
PubMed1998

A locally inhibited lateral neural network: on the fundamental features of a network consisting of neurons with a restricted range of interaction.

A network model that consists of neurons with a restricted range of interaction is presented. The neurons are connected mutually by inhibition weights. The inhibition of the whole network can be controlled by the range of interaction of a neuron. By this local inhibition mechanism, the present network can produce patterns with a small activity from input patterns with various large activities. Moreover, it is shown in simulation that the network has attractors for input patterns. The appearance of attractors is caused by the local interaction of neurons. Thus, we expect that the network not only works as a kind of filter, but also as a memory device for storing the produced patterns. In the present paper, the fundamental features and behavior of the network are studied by using a simple network structure and a simple rule of interaction of neurons. In particular, the relation between the interaction range of a neuron and the activity of input-output patterns is shown in simulation. Furthermore, the limit of the transformation and the size of basin are studied numerically.

Computer Simulation↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
PubMed1994

Neural network for control of rearrangeable Clos networks.

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

Algorithms↗
PubMedDate not supplied

Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? The Steering Committee of the Cardiac Care Network of Ontario.

OBJECTIVE: To compare the abilities of artificial neural network and logistic regression models to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery. METHODS: Neural network and logistic regression models were developed using a training set of 4,782 patients undergoing CABG surgery in Ontario, Canada, in 1991, and they were validated in two test sets of 5,309 and 5,517 patients having CABG surgery in 1992 and 1993, respectively. RESULTS: The probabilities predicted from a fully trained neural network were similar to those of a "saturated" regression model, with both models detecting all possible interactions in the training set and validating poorly in the two test sets. A second neural network was developed by cross-validating a network against a new set of data and terminating network training early to create a more generalizable model. A simple "main effects" regression model without any interaction terms was also developed. Both of these models validated well, with areas under the receiver operating characteristic curves of 0.78 and 0.77 (p > 0.10) in the 1993 test set. The predictions from the two models were very highly correlated (r=0.95). CONCLUSIONS: Artificial neural networks and logistic regression models learn similar relationships between patient characteristics and mortality after CABG surgery.

Aged↗
PubMed2004

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

BACKGROUND: Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. RESULTS: We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. CONCLUSIONS: We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Algorithms↗
PubMed2006

A social network profile and HIV risk among men on methadone: do social networks matter?

The paper describes structural and HIV-related network characteristics and examines associations between these various social network domains and HIV risk behaviors among a sample of 356 men randomly selected from a methadone maintenance treatment program (MMTP) in New York City. Multiple logistic regression analyses suggest that (1) a higher level of perceived sexual risk among network members, referred to as "alters" in this study, was associated with an increased likelihood of the participant engaging in sexual risk behaviors; (2) participants who indicated that they exchanged encouragement with a higher number of network alters about using condoms were less likely to report engaging in unprotected sex; and (3) participants who indicated that they talked about HIV risks with a higher number of network alters were less likely to engage in unprotected sex in the past 6 months. Collectively, these findings support the notion that networks may influence the adoption of risk reduction strategies in this population. Implications of the findings for HIV prevention network interventions for men in MMTPs are discussed.

Adult↗
PubMedDate not supplied

In vitro cytotoxicity of injectable and biodegradable poly(propylene fumarate)-based networks: unreacted macromers, cross-linked networks, and degradation products.

This study evaluates the in vitro biocompatibility of an injectable and biodegradable polymeric network based on poly(propylene fumarate) (PPF) and the cross-linking agent PPF-diacrylate (PPF-DA). Using a methyl tetrazolium (MTT) assay, the effect of the concentrations of PPF and PPF-DA on the cytotoxicity of its unreacted macromers, cross-linked networks, and degradation products was examined. The influence of network structure properties on cell viability and attachment to the cross-linked material was also investigated. The unreacted macromers exhibited a time- and dose-dependent cytotoxic response that increased with more PPF-DA in the mixture. Conversely, the cross-linked networks formed with more PPF-DA did not demonstrate an adverse response because increases in conversion and cross-linking density prevented the extraction of toxic products. Fibroblast attachment was observed on the PPF/PPF-DA networks with the highest double bond conversions. The degradation products, obtained from the complete breakdown of the networks in basic conditions, displayed a dose-dependent cytotoxic response. These results show that there are concerns regarding the biocompatibility of injectable, biodegradable PPF/PPF-DA networks but also sheds light onto potential mechanisms to reduce the cytotoxic effects.

Absorbable Implants↗
PubMed2005

Social network schemas and the learning of incomplete networks.

Social networks that are missing relations among some of their members--termed incomplete networks--have been of critical theoretical and empirical interest in sociological research on weak ties and structural holes but typically have been overlooked in social psychological studies of network learning. Five studies tested for schematic processing differences in the encoding and recalling of incomplete networks. In Studies 1 and 2, prior knowledge of missing relations facilitated learning an unfamiliar, incomplete network. Study 3 ruled out differences in general pattern recognition ability as an explanation. Study 4 manipulated the degree of familiarity with missing relations, which produced predicted differences in learning rates. Finally, Study 5 examined how improved learning of an incomplete network affected a strategic organizational choice. The findings suggest that people can become schematic for complex, incomplete social networks.

Adult↗
PubMed2004

Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction.

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

Amino Acid Motifs↗
PubMed2003

Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection.

We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then re-estimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as a real application.

Bayes Theorem↗
PubMed2006

BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.

UNLABELLED: BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant sub-modules of regulatory networks with high confidence are reconstructed by using our extended sub-network mining algorithm of directed graphs. BNArray can handle microarray datasets with missing data. To evaluate the statistical features of generated Bayesian networks, re-sampling procedures are utilized to yield collections of candidate 1st-order network sets for mining dense coherent sub-networks. AVAILABILITY: The R package and the supplementary documentation are available at http://www.cls.zju.edu.cn/binfo/BNArray/.

Algorithms↗
PubMed2003

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Computational Biology↗
PubMed2004

Network-induced oscillatory behavior in material flow networks and irregular business cycles.

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units--and linear chains of these units--behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

Journal Article↗
PubMed2006

Effects of network structure and routing strategy on network capacity.

The capacity of maximum end-to-end traffic flow the network is able to handle without overloading is an important index for network performance in real communication systems. In this paper, we estimate the variations of network capacity under different routing strategies for three different topologies. Simulation results reveal that the capacity depends on the underlying network structure and the capacity increases as the network becomes more homogeneous. It is also observed that the network capacity is greatly enhanced when the new traffic awareness routing strategy is adopted in each network structure.

Journal Article↗