Network Identification Method Research Articles

Identification of Functional Connections in Biological Neural Networks Using Dynamic Bayesian Networks

Abstract: Investigation of the underlying structural characteristics and network properties of biological networks is crucial to understanding the system-level regulatory mechanism of network behaviors. A Dynamic Bayesian Network (DBN) identification method is developed based on the Minimum Description Length (MDL) to identify and locate functional connections among Pulsed Neural Networks (PNN), which are typical in synthetic biological networks. A score of MDL is evaluated for each candidate network structure which includes two factors: i) the complexity of the network; and ii) the likelihood of the network structure based on network dynamic response data. These two factors are combined together to determine the network structure. The DBN is then used to analyze the time-series data from the PNNs, thereby discerning causal connections which collectively show the network structures. Numerical studies on PNN with different number of nodes illustrate the effectiveness of the proposed strategy in network structure identification.

Experimental determination of some equilibrium parameter of Damavand tokamak by magnetic probe measurements for representing a physical model for plasma vertical movement.

This investigation is about plasma modeling for the control of vertical instabilities in Damavand tokamak. This model is based on online magnetic measurement. The algebraic equation defining the vertical position in this model is based on instantaneous force-balance. Two parameters in this equation, including decay index, n, and lambda, Λ, have been considered as functions of time-varying poloidal field coil currents and plasma current. Then these functions have been used in a code generated for modeling the open loop response of plasma. The main restriction of the suitability analysis of the model is that the experiments always have to be performed in the presence of a control loop for stabilizing vertical position. As a result, open loop response of the system has been identified from closed loop experimental data by nonlinear neural network identification method. The results of comparison of physical model with identified open loop response from closed loop experiments show root mean square error percentage less than 10%. The results are satisfying that the physical model is useful as a Damavand tokamak vertical movement simulator.

Numerical Methods for Genetic Regulatory Network Identification Based on a Variational Approach

Abstract This paper studies differential equation-based mathematical models and their numerical solutions for genetic regulatory network identification. The primary objectives are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes an energy functional. The variational principle serves not only as a selection criterion to pick up the right solution of the underlying differential equation model but also provides an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed. Model validations using both synthetic network datasets and subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. coli are performed on all five proposed variational models and a performance comparison versus some existing genetic regulatory network identification methods is also provided.

A New Hybrid Modeling Method for Performance Digital Mock-Up of Hypersonic Vehicle

For the characteristics that a hypersonic vehicle has a large span of flight height and flight Mach number, and complicated flight environment, the model of which is highly nonlinear, unstable and multivariablecoupled, where using a single modelling approach is often difficult to achieve high modeling accuracy, hybrid modeling method is proposed to design its performance digital mock-up. The performance digital mock-up hybrid model are designed by using fuzzy set theory, neural networks and system identification method based on combination of the three fuzzy neural network identification method and technology. The simulation results show that high modeling accuracy of this modeling method is generally conventional neural network or fuzzy identification incomparable.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define ‘synconset wave’ as a cascade of first spikes within a synchronisation event. Synconset waves would occur in ‘synconset chains’, which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

Research on the Hybrid Modelling Method Based on Mechanism and Identification for Marine Diesel

Mechanism method and neural network identification method are widely used for system modelling. Due to the complexity of actual investigated objects, models built with individual method are usually not good enough for the problems to be solved. Therefore, a hybrid modelling method based on mechanism and identification is presented in the paper. In the hybrid model, the mechanism model is applied as the fundamental model in order to ensure the overall coincidence performance and the identification model is adopted as the compensated model in order to ensure the accuracy performance. The developing process of the hybrid model of a marine turbo-charged diesel is demonstrated in detail in the paper. Testing result shows that the hybrid modelling method is very suitable for modelling complex investigated objects.

Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence

The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.

Inference of gene regulatory subnetworks from time course gene expression data

BackgroundIdentifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.ResultsWe propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs.ConclusionsThe NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.

Identification and Control Optimization Algorithm Based on Neural Networks and Ant Colony

It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system’s delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.

Nonlinear systems identification using dynamic multi-time scale neural networks

In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results.

Estimating nurse migration from the Bahamas between 1994 and 2005: An exploratory descriptive study using a social network identification methodology

The purpose of this study was to collect primary data on the migration patterns of Bahamian nurses who had registered as nurses during the period 1994–2005. We used an established social network identification method developed by Delanyo Dovlo to determine whether (and to where) Bahamian nurses had migrated. We reviewed nursing registrants’ records from the Nursing Council of the Commonwealth of the Bahamas. We telephoned two nurses per cohort of registration and graduation year, asking the interviewee to identify the current location of colleagues registered in their same cohort. Between 1994 and 2005, a total of 18 out of 282 nurses were either confirmed or probably migrated (6%). Ninety-six per cent of those nurses registered during the study time frame were located during the exercise, partially because of an improvement on the Dovlo method – calling a nurse leader who could locate nursing classmates and colleagues beyond his or her own year of graduation. Nurse migration in the Bahamas appears lower than in surrounding countries, posing a research opportunity to investigate the causes for this positive deviation. Future studies employing this method should include interviews of nurse leaders who can confirm the location of a much wider range of people than can the average participant.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdös-Rényi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabási-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree <k> variation, decreasing its network recovery rate with the increase of <k>. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

On Sparsity as a Criterion in Reconstructing Biochemical Networks

AbstractA common problem in inference of gene regulatory networks from experimental response data is the relatively small number of samples available in relation to the number of nodes/states. In many cases the identification problem is underdetermined and prior knowledge is required for the network reconstruction. A specific prior that has gained widespread popularity is the assumption that the underlying network is sparsely connected. This has led to a flood of network reconstruction algorithms based on subset selection and regularization techniques, mainly adopted from the statistics and signal processing communities. In particular, methods based on ℓ1 and ℓ2-penalties on the interaction strengths, such as LASSO, have been widely proposed and applied. We briefly review some of these methods and discuss their suitability for inferring the structure of biochemical networks. A particular problem is the fact that these methods provide little or no information on the uncertainty of individual identified edges, combined with the fact that the identified networks usually have a large fraction of false positives as well as false negatives. To partly overcome these problems we consider conditions that can be used to classify edges into those that can be uniquely determined based on a given incomplete data set, those that cannot be uniquely determined due to collinearity in the data, and those for which no information is available. Apart from providing a label of confidence for the individual edges in the identified network, the classification can be used to improve the reconstruction by employing standard unbiased identification methods to the identifiable edges while employing sparse approximation methods for the remaining network. The method is demonstrated through application to a synthetic network in yeast which has recently been proposed for in vivo assessment of network identification methods.

The Method of Neuron Network Identification of Seismic Signals

The Method of Neuron Network Identification of Seismic Signals

Genome-wide expression analysis of genetic networks in Neurospora crassa

The products of five structural genes and two regulatory genes of the qa gene cluster of Neurospora crassa control the metabolism of quinic acid (QA) as a carbon source. A detailed genetic network model of this metabolic process has been reported. This investigation is designed to expand the current model of the QA reaction network. The ensemble method of network identification was used to model RNA profiling data on the qa gene cluster. Through microarray and cluster analysis, genome-wide identification of RNA transcripts associated with quinic acid metabolism in N. crassa is described and suggests a connection to other metabolic circuits. More than 100 genes whose products include carbon metabolism, protein degradation and modification, amino acid metabolism and ribosome synthesis appear to be connected to quinic acid metabolism. The core of the qa gene cluster network is validated with respect to RNA profiling data obtained from microarrays.

A genetic network for the clock of Neurospora crassa

A diverse array of organisms from bacteria to humans may have evolved the ability to tell time in the presence or absence of external environmental cues. In the lowly bread mould, Neurospora crassa, biomolecular reactions involving the white-collar-1 (wc-1), white-collar-2 (wc-2), and frequency (frq) genes and their products constitute building blocks of a biological clock. Here we use genetic network models to explain quantitatively, from a systems perspective, how these building blocks interact, and how a complex trait like clock oscillation emerges from these interactions. We use a recently developed method of genetic network identification to find an ensemble of oscillating network models quantitatively consistent with available RNA and protein profiling data on the N. crassa clock. Predicted key features of the N. crassa clock system are a dynamically frustrated closed feedback loop, cooperativity in frq gene activation, and/or WC-1/WC-2 protein complex deactivation and substantial posttranscriptional enhancement of wc-1 RNA lifetime. Measuring the wc-1 mRNA lifetime provides a critical test of the genetic networks.

Identification of large-scale networks in the brain using fMRI

Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0–0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the “default” network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253–258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.

Systems Biology

Systems Biology

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Efficient modelling and simulation of soft tissue deformation using mass-spring systems

In this paper, we use a mass-spring system to simulate facial soft tissue deformation resulting from the bone realignment at the lower jaw area. Since the materials concerned often exhibit significant nonlinearity, correct simulation parameters are needed to capture the nonlinear characteristics in order to achieve satisfying simulation accuracy. We propose a neural network identification method that takes mass-spring structure into account and uses only two neural networks to identify these parameters, which are usually nonlinear functions. An adaptive learning rate formula is also introduced to improve the simulation accuracy and convergence speed.