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Results for “molecular network”

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At least 37 records · Page 2Linked to original sources
PubMed2006

Site-directed electronic tunneling through a vibrating molecular network.

The effect of electronic-nuclear coupling on electronic transport through a complex molecular network is studied. Electronic tunneling dynamics in a network of N donor/acceptor sites, connected by molecular bridges, is shown to be controlled by electronic-nuclear coupling at the bridges. Particularly, electronic coupling to an accepting nuclear mode at the contact site between the donor and the rest of the network is shown to affect the tunneling path selection to specific acceptors. In the "deep" tunneling regime, the network is mapped onto an N-level system using a recursive perturbation expansion, enabling analytical treatment of the electronic dynamics. The analytic formulation is applied for two model systems, demonstrating site-directed tunneling by electronic-nuclear coupling. Numerical simulations suggest that this phenomenon is not limited to the deep tunneling regime.

Journal Article↗
PubMed2004

WebInterViewer: visualizing and analyzing molecular interaction networks.

Molecular interaction networks, such as those involving protein-protein and protein-DNA interactions, often consist of thousands of nodes or even more, which severely limits the usefulness of many graph drawing tools because they become too slow for interactive analysis of the networks and because they produce cluttered drawings with many edge crossings. We present a new, fast-layout algorithm and its implementation called WebInterViewer for visualizing large-scale molecular interaction networks. WebInterViewer (i) finds a layout of the connected components of an entire network, (ii) finds a global layout of nodes with respect to pivot nodes within the connected components and (iii) refines the local layout of each connected component by first relocating midnodes with respect to their cutvertices and the direct neighbors of the cutvertices, and then relocating all nodes with respect to their neighbors within distance 2. The advantages of WebInterViewer over classical graph drawing methods include the facts that (i) it is an order of magnitude faster, (ii) it can visualize data directly from protein interaction databases and (iii) it provides several abstraction and comparison operations for analyzing large-scale biological networks effectively. WebInterViewer is accessible at http://interviewer.inha.ac.kr/.

Computer Graphics↗
PubMed2003

Deformation of porous molecular networks induced by the exchange of guests in single crystals.

A strategy for making molecular networks that are porous and deformable is revealed by the behavior of compound 1, in which groups that form hydrogen bonds are attached to a nominally tetrahedral Si core. Compound 1 crystallizes from various carboxylic acids to produce a porous hydrogen-bonded diamondoid network, with up to 65% of the volume available for including guests. Changing the guests expands or contracts the network up to 30% in one direction, and single crystals can accommodate these exchange-induced deformations without loss of crystallinity. This resilience appears to result in part from the incorporation of flexible Si nodes in an otherwise robust network maintained by multiple hydrogen bonds. In certain cases, exchange is faster than deformation of the network, allowing the isolation of metastable structures with a new guest included in an otherwise unchanged network. Such processes can provide new materials that would be difficult or impossible to obtain in other ways.

Journal Article↗
PubMed2004

Analyzing cellular biochemistry in terms of molecular networks.

One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.

Biochemical Phenomena↗
PubMed2026

Comprehensive identification of carboxylic acids by using bromine isotope-based chemical isotope labelling and structure-guided molecular network.

Carboxylic acids (CAs) are important contributors to the flavor quality of sauce-flavor Chinese Baijiu, yet their comprehensive analysis remains challenging due to poor ionization efficiency, weak chromatographic retention, and limited annotation capability. Herein, we developed a workflow for the high-coverage discovery and annotation of CAs in Baijiu by coupling chemical isotope labeling-liquid chromatography-mass spectrometry with a structure-guided molecular network strategy (SGMNS). A bromine-containing derivatization reagent, 1-(3-aminopropyl)-3-bromoquinolin-1-ium bromide (APBQ), was designed and synthesized to exploit the natural isotope distribution of bromine and characteristic MS/MS fragmentation behavior. Following APBQ derivatization, the target CAs showed superior chromatographic retention and favorable analytical performance. Based on isotopic peak pairing in MS1 and diagnostic fragment validation in MS2, 372 potential CA derivatives were discovered from pooled Baijiu samples and 355 of them were validated by diagnostic fragments in MS2 spectra. To address the scarcity of derivatized spectral libraries, SGMNS was employed for annotation using a background network constructed from APBQ-labeled candidates derived from the Expanded Chinese Baijiu Compound Database. The developed method was further applied to profile Baijiu samples, revealing pronounced differences in CA composition across the seven fermentation rounds. Notably, rounds 3 to 5 exhibited the largest numbers of differential CAs. This study provided an effective analytical strategy for large-scale CA profiling, offering new insight into the chemical basis of flavor formation during multi-round fermentation of sauce-flavor Baijiu.

Isotope Labeling↗
PubMed2004

Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer's disease.

A major challenge in human genetics is identifying the molecular basis of common heritable disorders. In contrast to rare single-gene diseases, multifactorial disorders are thought to arise from the combined effect of multiple gene variants, such that any single variant may have only a modest effect on disease susceptibility. We present a method to identify genes that may harbor a significant proportion of the genetic variation that predisposes individuals to a given multifactorial disorder. First, we perform an automated literature analysis that predicts physical interactions (edges) among candidate disease genes (seed nodes, selected on the basis of prior information) and other molecular entities. We derive models of molecular networks from this analysis and map the seed nodes to them. We then compute the graph-theoretic distance (the minimum number of edges that must be traversed) between the seed nodes and all other nodes in the network. We assume that nodes that are found in close proximity to multiple seed nodes are the best disease-related candidate genes. To evaluate this approach, we selected four seed genes, each with a proven role in Alzheimer's disease (AD). The method performed well in predicting additional network nodes that match AD gene candidates identified manually by an expert. We also show that the method prioritizes among the seed nodes themselves, rejecting false-positive seeds that are derived from (noisy) whole-genome genetic-linkage scans. We propose that this strategy will provide a valuable means to bridge genetic and genomic knowledge in the search for genetic determinants of multifactorial disorders.

Algorithms↗
PubMed2004

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data.

The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

Amino Acids↗
PubMed2004

The next step in systems biology: simulating the temporospatial dynamics of molecular network.

As a result of the time- and context-dependency of gene expression, gene regulatory and signaling pathways undergo dynamic changes during development. Creating a model of the dynamics of molecular interaction networks offers enormous potential for understanding how a genome orchestrates the developmental processes of an organism. The dynamic nature of pathway topology calls for new modeling strategies that can capture transient molecular links at the runtime. The aim of this paper is to present a brief and informative, but not all-inclusive, viewpoint on the computational aspects of modeling and simulation of a non-static molecular network.

Algorithms↗
PubMed2003

Charge assisted chiral hybrid H-bonded molecular networks.

The co-crystallisation of [Fe(CN)5NO]2- and cyclic achiral 1(2+) and chiral 2(2+) or 3(2+) bisamidinium tectons leads in the crystalline phase to the formation of 1-D H-bonded achiral and chiral molecular networks respectively. In all cases, the network is formed by mutual bridging of the anionic and cationic units through a chelate mode of H-bonding.

Journal Article↗
PubMed2004

Molecular networks in model systems.

Model organisms, especially the budding yeast, are leading systems in the transformation of biology into an information science. With the availability of genome sequences and genome-scale data generation technologies, the extraction of biological insight from complex integrated molecular networks has become a major area of research. Here I examine key concepts and review research developments. I propose specific areas of research effort to drive network analysis in directions that will promote modeling with increasing predictive power.

Computational Biology↗
PubMed2004

Design of a directed molecular network.

An ability to rationally design complex networks from the bottom up can offer valuable quantitative model systems for use in gaining a deeper appreciation for the principles governing the self-organization and functional characteristics of complex systems. We report herein the de novo design, graph prediction, experimental analysis, and characterization of simple self-organized, nonlinear molecular networks. Our approach makes use of the sequence-dependent auto- and cross-catalytic functional characteristics of template-directed peptide fragment condensation reactions in neutral aqueous solutions. Starting with an array of 81 sequence similar 32-residue coiled-coil peptides, we estimated the relative stability difference between all plausible A(2)B-type coiled-coil ensembles and used this information to predict the auto- and cross-catalysis pathways and the resulting plausible network motif and connectivities. Similar to most complex systems, the generated graph displays clustered nodes with an overall hierarchical architecture. To test the validity of the design principles used, nine nodes composing a main segment of the graph were experimentally analyzed for their capacity in establishing the predicted network connectivity. The resulting self-organized chemical network is shown to display 25 directed edges in good agreement with the graph analysis estimations. Moreover, we show that by varying the system parameters (presence or absence of certain substrates or templates), its operating network motif can be altered, even to the extremes of turning pathways on or off. We suggest that this approach can be expanded for the construction of large-scale networks, offering a means to study and to understand better the emergent, collective behaviors of networks.

Amino Acid Sequence↗
PubMed1998

Self-organizing molecular networks.

Strong diffusional mixing and short delivery times typical for micrometer and sub-micrometer reaction volumes lead to a special situation where the turnover times of individual enzyme molecules become the largest characteristic time scale of the chemical kinetics. Under these conditions, populations of cross-regulating allosteric enzymes form molecular networks that exhibit various kinds of self-organized coherent collective dynamics.

Cell Physiological Phenomena↗
PubMed1999

Self-organizing molecular networks.

Strong diffusional mixing and short delivery times typical for micrometer and sub-micrometer reaction volumes lead to a special situation where the turnover times of individual enzyme molecules become the largest characteristic time scale of the chemical kinetics. Under these conditions, populations of cross-regulating allosteric enzymes form molecular networks that exhibit various kinds of self-organized coherent collective dynamics.

Allosteric Regulation↗
PubMed2016

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Biological Products↗
PubMed2025

Exploring biosynthetic potential of the endophytic Penicillium turbatum BLH34 using whole-genome sequence analysis and molecular networking.

An in-depth genomic and metabolomic investigation was conducted on the endophytic fungus Penicillium turbatum BLH34, isolated from Macleaya cordata. Hybrid sequencing (Illumina-Nanopore) generated a high-quality 27.9 Mb genome (GC 48.6%) encoding 9798 proteins, with functional annotation linking 5350 genes to the NCBI non-redundant database and 3404 to KEGG pathways. AntiSMASH analysis uncovered 35 biosynthetic gene clusters (BGCs), 23 of which lacked homology to known pathways, highlighting BLH34's potential for novel metabolite discovery. Molecular networking (GNPS) and LC-MS/MS identified 19 specialised metabolites, including antimicrobial polyketides. Bioassays demonstrated potent inhibition against Staphylococcus aureus (36 mm), Bacillus subtilis (28 mm) and Escherichia coli (24 mm), underscoring its pharmaceutical relevance.

Penicillium↗
PubMed2005

Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma.

Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.

Carcinoma↗