Multilayer neural network of adaptive bidirectional associative memories

The analysis of the functioning of the brain allows to propose a computational model of multilayer artificial neural network susceptible of associating some response to a particular input, so that when we present that input, we get the required output by the stability of its states and by minimizing the function of energy of the network. The problem of explosion in the number of interconnections has been solved by the introduction of a layer between the input and the output layer of the network. In this paper, we propose the adaptive bidirectional associative memory by conjugate gradient algorithm, so as to study the behavior and performances of the network on pairs of patterns through using the autoassociative or heteroassociative memories.     

Linear conjugacy of chemical reaction networks

Under suitable assumptions, the dynamic behaviour of a chemical reaction network is governed by an autonomous set of polynomial ordinary differential equations over continuous variables representing the concentrations of the reactant species. It is known that two networks may possess the same governing mass-action dynamics despite disparate network structure. To date, however, there has only been limited work exploiting this phenomenon even for the cases where one network possesses known dynamics while the other does not. In this paper, we bring these known results into a broader unified theory which we call conjugate chemical reaction network theory. We present a theorem which gives conditions under which two networks with different governing mass-action dynamics may exhibit the same qualitative dynamics and use it to extend the scope of the well-known theory of weakly reversible systems.     

Tetrakis(imidazolyl)borate-based coordination polymers: group II network solids,M[B(Im)4]2(H2O)2 (M = Mg,Ca, Sr)

We are using the coordinating anion tetrakis(imidazolyl)borate to construct new metal-organic framework structures. In this report, we present three alkaline earth metal network solids incorporating this anion. All three compounds have the same formula, M[B(Im)(4)](2)(H(2)O)(2) (M = Mg, Ca, Sr), and the same coordination environment about the metal. However, the three compounds have different network structures with different degrees of hydrogen bonding; the Mg material forms a two-dimensional network and the Ca and Sr compounds form one-dimensional chains. In addition, we present the structure of the protonated anion B(HIm)(Im)(3) as a model for the default structure of this anion and discuss how the conformation of tetrakis(imidazolyl)borate can affect the structure of network solids.     

Node-based differential network analysis in genomics

Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.     

Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals

There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantially increases the potentialities of this kind of networks, avoiding time and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one species. To solve this problem we developed a multispecies QSAR classification model, in which the outputs were the inputs of the aforementioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extend model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power law network with few mechanisms (network hubs).     

Simulational study of anomalous tracer diffusion in hydrogels

In this article, we analyze different factors that affect the diffusion behavior of small tracer particles (as they are used, e.g., in fluorescence correlation spectroscopy (FCS)) in the polymer network of a hydrogel and perform simulations of various simplified models. We observe, that under certain circumstances the attraction of a tracer particle to the polymer network strands might cause subdiffusive behavior on intermediate time scales. In theory, this behavior could be employed to examine the network structure and swelling behavior of weakly crosslinked hydrogels with the help of FCS.     

A strategy based on protein-protein interface motifs may help in identifying drug off-targets

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.     

Constrained Rouse model of rubber viscoelasticity

In this work we use a new approach to investigate the equilibrium and linear dynamic-mechanical response of a polymer network. The classical Rouse model is extended to incorporate quenched constraints on its end-boundary conditions; a microscopic stress tensor for the network system is then derived in the affine deformation limit. To test the model we calculate the macroscopic stress in equilibrium, corresponding to the long-time limit of relaxation. Particular attention is paid to the treatment of compressibility and hydrostatic pressure in a sample with open boundaries. Although quite different in general, for small strains the model compares well with the classic equilibrium rubber-elasticity models. The dynamic shear modulus is obtained for a network relaxing after an instantaneous step strain by keeping track of relaxation of consecutive Rouse modes of constrained network strands. The results naturally cover the whole time range--from the dynamic glassy state down to the equilibrium incompressible rubber plateau.     

Genome-wide predicting disease-related protein complexes by walking on the heterogeneous network based on data integration and laplacian normalization

BackgroundAssociating protein complexes to human inherited diseases is critical for better understanding of biological processes and functional mechanisms of the disease. Many protein complexes have been identified and functionally annotated by computational and purification methods so far, however, the particular roles they were playing in causing disease have not yet been well determined.ResultsIn this study, we present a novel method to identify associations between protein complexes and diseases. First, we construct a disease-protein heterogeneous network based on data integration and laplacian normalization. Second, we apply a random walk with restart on heterogeneous network (RWRH) algorithm on this network to quantify the strength of the association between proteins and the query disease. Third, we sum over the scores of member proteins to obtain a summary score for each candidate protein complex, and then rank all candidate protein complexes according to their scores. With a series of leave-one-out cross-validation experiments, we found that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We test our approach with breast cancer and select top 20 highly ranked protein complexes, 17 of the selected protein complexes are evidenced to be connected with breast cancer.ConclusionsOur proposed method is effective in identifying disease-related protein complexes based on data integration and laplacian normalization.     

Light-emitting conjugated polymers with microporous network architecture: interweaving scaffold promotes electronic conjugation, facilitates exciton migration, and improves luminescence

Herein we report a strategy for the design of highly luminescent conjugated polymers by restricting rotation of the polymer building blocks through a microporous network architecture. We demonstrate this concept using tetraphenylethene (TPE) as a building block to construct a light-emitting conjugated microporous polymer. The interlocked network successfully restricted the rotation of the phenyl units, which are the major cause of fluorescence deactivation in TPE, thus providing intrinsic luminescence activity for the polymers. We show positive "CMP effects" that the network promotes π-conjugation, facilitates exciton migration, and improves luminescence activity. Although the monomer and linear polymer analogue in solvents are nonemissive, the network polymers are highly luminescent in various solvents and the solid state. Because emission losses due to rotation are ubiquitous among small chromophores, this strategy can be generalized for the de novo design of light-emitting materials by integrating the chromophores into an interlocked network architecture.     

Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Ordinary differential equations (ODE) have been widely used for modeling and analysis of dynamic gene networks in systems biology. In this paper, we propose an optimization method that can infer a gene regulatory network from time-series gene expression data. Specifically, the following four cases are considered: (1) reconstruction of a gene network from synthetic gene expression data with noise, (2) reconstruction of a gene network from synthetic gene expression data with time-delay, (3) reconstruction of a gene network from synthetic gene expression data with noise and time-delay, and (4) reconstruction of a gene network from experimental time-series data in budding yeast cell cycle.     

Conductivity of diblock copolymer system filled with conducting nano-particles: Effect of copolymer morphology

We explore the effect of temperature-induced morphological changes in insulating diblock copolymer system (DBC) filled with conductive fillers on the conductivity of this composite. By making use of the developed method that relies on the consistent phase-field model of DBC, Monte-Carlo simulations of the filler distribution in DBC, and resistor network model, we quantitatively relate the morphology of filled DBC and its conductivity. In particular, we demonstrate that the order–disorder transition between the random and ordered microphases of DBC causes the conductor-insulator transition in the network of conductive fillers immersed in this system. The order–order transition between the ordered lamellae and cylindrical microphases of DBC is found to co-occur with a jump in the composite conductivity caused by restructuring of the conductive filler network.     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

可拉伸超韧水凝胶的制备和应用

综述了可拉伸超韧水凝胶的设计原理及其在组织工程和柔性电子器件领域的应用. 通过将网络结构层次、 化学结构、 增韧机制与宏观力学性能相结合, 重点讨论了单网络水凝胶、 双网络水凝胶、 纳米复合水凝胶及其它水凝胶等可拉伸超韧水凝胶的研究进展, 并总结和展望了新思路和新方向.     

Atoms of multistationarity in chemical reaction networks

Chemical reaction systems are dynamical systems that arise in chemical engineering and systems biology. In this work, we consider the question of whether the minimal (in a precise sense) multistationary chemical reaction networks, which we propose to call ‘atoms of multistationarity,’ characterize the entire set of multistationary networks. Our main result states that the answer to this question is ‘yes’ in the context of fully open continuous-flow stirred-tank reactors (CFSTRs), which are networks in which all chemical species take part in the inflow and outflow. In order to prove this result, we show that if a subnetwork admits multiple steady states, then these steady states can be lifted to a larger network, provided that the two networks share the same stoichiometric subspace. We also prove an analogous result when a smaller network is obtained from a larger network by ‘removing species.’ Our results provide the mathematical foundation for a technique used by Siegal- Gaskins et al. of establishing bistability by way of ‘network ancestry.’ Additionally, our work provides sufficient conditions for establishing multistationarity by way of atoms and moreover reduces the problem of classifying multistationary CFSTRs to that of cataloging atoms of multistationarity. As an application, we enumerate and classify all 386 bimolecular and reversible two-reaction networks. Of these, exactly 35 admit multiple positive steady states. Moreover, each admits a unique minimal multistationary subnetwork, and these subnetworks form a poset (with respect to the relation of ‘removing species’) which has 11 minimal elements (the atoms of multistationarity).     

Identifying essential pairwise interactions in elastic network model using the alpha shape theory

Elastic network models (ENM) are based on the idea that the geometry of a protein structure provides enough information for computing its fluctuations around its equilibrium conformation. This geometry is represented as an elastic network (EN) that is, a network of links between residues. A spring is associated with each of these links. The normal modes of the protein are then identified with the normal modes of the corresponding network of springs. Standard approaches for generating ENs rely on a cutoff distance. There is no consensus on how to choose this cutoff. In this work, we propose instead to filter the set of all residue pairs in a protein using the concept of alpha shapes. The main alpha shape we considered is based on the Delaunay triangulation of the Cα positions; we referred to the corresponding EN as EN(∞). We have shown that heterogeneous anisotropic network models, called αHANMs, that are based on EN(∞) reproduce experimental B‐factors very well, with correlation coefficients above 0.99 and root‐mean‐square deviations below 0.1 Ų for a large set of high resolution protein structures. The construction of EN(∞) is simple to implement and may be used automatically for generating ENs for all types of ENMs. © 2014 Wiley Periodicals, Inc.     

Boundedness of trajectories for weakly reversible,single linkage class reaction systems

This paper is concerned with the dynamical properties of deterministically modeled chemical reaction systems with mass-action kinetics. Such models are ubiquitously found in chemistry, population biology, and the burgeoning field of systems biology. A basic question, whose answer remains largely unknown, is the following: for which network structures do trajectories of mass-action systems remain bounded in time? In this paper, we conjecture that the result holds when the reaction network is weakly reversible, and prove this conjecture in the case when the reaction network consists of a single linkage class, or connected component.     

Scattering form factors for self-assembled network junctions

The equilibrium microstructures in microemulsions and other self-assembled systems show complex, connected shapes such as symmetric bicontinuous spongelike structures and asymmetric bicontinuous networks formed by cylinders interconnected at junctions. In microemulsions, these cylinder network microstructures may mediate the structural transition from a spherical or globular phase to the bicontinuous microstructure. To understand the structural and statistical properties of such cylinder network microstructures as measured by scattering experiments, models are needed to extract the real-space structure from the scattering data. In this paper, we calculate the scattering functions appropriate for cylinder network microstructures. We focus on such networks that contain a high density of network junctions that connect the cylindrical elements. In this limit, the network microstructure can be regarded as an assembly of randomly oriented, closed packed network junctions (i.e., the cylinder scattering contributions are neglected). Accordingly, the scattering spectrum of the network microstructure can be calculated as the product of the junction number density, the junction form factor, which describes the scattering from the surface of a single junction, and a structure factor, which describes the local correlations of different junctions due to junction interactions (including their excluded volume). This approach is applied to analyze the scattering data from a bicontinuous microemulsion with equal volumes of water and oil. In a second approach, we included the cylinder scattering contribution in the junction form factor by calculating the scattering intensity of Y junctions to which three rods with spherical cross section are attached. The respective theoretical predictions are compared with results of neutron scattering measurements on a water-in-oil microemulsion with a connected microstructure.