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At least 613 records · Page 34Linked to original sources
PubMed2001

Architecture of idiotypic networks: percolation and scaling behavior.

We investigate a model where idiotypes (characterizing B lymphocytes and antibodies of an immune system) and anti-idiotypes are represented by complementary bit strings of a given length d allowing for a number of mismatches (matching rules). In this model, the vertices of the hypercube in dimension d represent the potential repertoire of idiotypes. A random set of (with probability p) occupied vertices corresponds to the expressed repertoire of idiotypes at a given moment. Vertices of this set linked by the above matching rules build random clusters. We give a structural and statistical characterization of these clusters, or in other words of the architecture of the idiotypic network. Increasing the probability p one finds at a critical p a percolation transition where for the first time a large connected graph occurs with probability 1. Increasing p further, there is a second transition above which the repertoire is complete in the sense that any newly introduced idiotype finds a complementary anti-idiotype. We introduce structural characteristics such as the mass distribution and the fragmentation rate for random clusters, and determine the scaling behavior of the cluster size distribution near the percolation transition, including finite size corrections. We find that slightly above the percolation transition the large connected cluster (the central part of the idiotypic network) consists typically of one highly connected part and a number of weakly connected constituents and coexists with a number of small, isolated clusters. This is in accordance with the picture of a central and a peripheral part of the idiotypic network and gives some support to idealized architectures of the central part used in recent dynamical mean field models.

Animals↗
PubMed2004

Predicting protein functions with message passing algorithms.

MOTIVATION: In the last few years, a growing interest in biology has been shifting toward the problem of optimal information extraction from the huge amount of data generated via large-scale and high-throughput techniques. One of the most relevant issues has recently emerged that of correctly and reliably predicting the functions of a given protein with that of functions exploiting information coming from the whole network of proteins physically interacting with the functionally undetermined one. In the present work, we will refer to an 'observed' protein as the one present in the protein-protein interaction networks published in the literature. METHODS: The method proposed in this paper is based on a message passing algorithm known as Belief Propagation, which accepts the network of protein's physical interactions and a catalog of known protein's functions as input, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast online analysis, and can easily be generalized into more complex graph topologies taking into account hypergraphs, i.e. complexes of more than two interacting proteins. RESULTS: Benchmarks of our method are the two Saccharomyces cerevisiae protein-protein interaction networks and the Database of Interacting Proteins. The validity of our approach is successfully tested against other available techniques. CONTACT: leone@isiosf.isi.it SUPPLEMENTARY INFORMATION: http://isiosf.isi.it/~pagnani

Algorithms↗
PubMed1993

Dynamic selection of models for a ventilator-management advisor.

A ventilator-management advisor (VMA) is a computer program that monitors patients who are treated with a mechanical ventilator. A VMA implements a patient-specific physiologic model to interpret patient data and to predict the effects of alternative control settings for the ventilator. Because a VMA evaluates its physiologic model repeatedly during each cycle of data interpretation, highly complex models may require more computation time than is available in this time-critical application. On the other hand, less complex models may be inaccurate if they are unable to represent a patient's physiologic abnormalities. For each patient, a VMA should select a model that balances the tradeoff of prediction accuracy and computation-time complexity. I present a method to select models that are at an appropriate level of detail for time-constrained decision tasks. The method is based on a local search in a graph of models (GoM) for a model that maximizes the tradeoff of computation-time complexity and prediction accuracy. For each model under consideration, a belief network computes a probability of model adequacy given the qualitative prior information, and the goodness of fit of the model to the data provides a measure of the conditional probability of adequacy given the quantitative observations. I apply this method to the problem of model selection for a VMA. I describe an implementation of a graph of physiologic models that range in complexity from VentPlan, a simple model with 3 compartments, to VentSim, a multicompartment model with detailed airway, circulation and mechanical ventilator components.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗
PubMed2002

Community structure in social and biological networks.

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.

Algorithms↗
PubMed2005

Graph theory for fused cubic clusters of water dodecamer.

The stable structures of the fused cubic water cluster (H2O)12 are examined using graph theoretical techniques and ab initio calculations. The calculations are obtained by scanning the symmetry of digraph structures of hydrogen-bond network spanning 12 oxygen atom vertexes. Using the Pólya theorem the cycle index expressions for 12 vertexes and 20 edges of a cuboid in point-group symmetry D(4h) are developed. A total of 91 energy-allowed fused cubic structures are obtained, which are classified by 8 point-group symmetries: 1 D(2h), 2 S4, 5 C4, 1 D2, 11 C2, 10 C(i), 1 C(s), and 60 C1. An energy level diagram of the structures reveals 14 bands that correspond to 14 unique two-colored graphs derived from the distributions of four free hydrogens of the cluster.

Macromolecular Substances↗
PubMed2006

Applications of small-world network theory in alcohol epidemiology.

OBJECTIVE: This study develops a mathematical model of alcohol abuse in structured populations, such as communities and college campuses. The study employs a network model that has the capacity to incorporate a variety of forms of connectivity membership besides personal acquaintance, such as geographic proximity and common organizations. The model also incorporates a resilience dimension that indicates the susceptibility of each individual in a network to alcohol abuse. The model has the capacity to simulate the effect of moving alcohol abusers into networks of nonabusers, either as the result of treatment or membership in self-help organizations. METHOD: The study employs a small-world model. A cubic equation for each person (vertex on a graph) governs the evolution of an individual's state between 0 and 1 with regard to alcohol dependence, with 1 indicating absolute certainty of alcohol dependence. The simulations are dependent on initial conditions, the structure of the network, and the resilience distribution of the network. The simulations incorporate multiple realizations of social networks, showing the effect of different network structures. RESULTS: The model suggests that the prevalence of alcohol abuse can be minimized by treating a relatively small percentage of the study population. In the small populations that we studied, the critical point was 10% or less of the study population, but we emphasize that this is within the limitations and assumptions of this model. CONCLUSIONS: The use of a simple model that incorporates the influence of the social network neighbors in structured populations shows promise for helping to inform treatment and prevention policy.

Alcoholics Anonymous↗
PubMed1995

A three-level graph-based model for the management of hospital information systems.

Information processing in hospitals, especially in university hospitals, is currently faced with two major issues: low-cost hardware and progress in networking technology leads to a further decentralization of computing capacity, due to the increasing need for information processing in hospitals and due to economic restrictions, it is necessary to use commercial software products. This leads to heterogeneous hospital information systems using a variety of software and hardware products, and to a stronger demand for integrating these products and, in general, for a dedicated methodology for the management of hospital information systems to support patient care and medical research. We present a three-level graph-based model (3LGM) to support the systematic management of hospital information systems. 3LGM can serve as a basis for assessing the quality of information processing in hospitals. 3LGM distinguishes between a procedural level for describing the information procedures (and their information interchange) of a hospital information system and thus its functionality, a logical too level, focusing on application systems and communication links, and a physical tool level with physical subsystems (e.g., computer systems) and data transmission. The examples that are presented have been taken from the Heidelberg University Hospital Information System.

Computer Graphics↗
PubMedDate not supplied

Analysis of the internal representations developed by neural networks for structures applied to quantitative structure--activity relationship studies of benzodiazepines.

An application of recursive cascade correlation (CC) neural networks to quantitative structure-activity relationship (QSAR) studies is presented, with emphasis on the study of the internal representations developed by the neural networks. Recursive CC is a neural network model recently proposed for the processing of structured data. It allows the direct handling of chemical compounds as labeled ordered directed graphs, and constitutes a novel approach to QSAR. The adopted representation of molecular structure captures, in a quite general and flexible way, significant topological aspects and chemical functionalities for each specific class of molecules showing a particular chemical reactivity or biological activity. A class of 1,4-benzodiazepin-2-ones is analyzed by the proposed approach. It compares favorably versus the traditional QSAR treatment based on equations. To show the ability of the model in capturing most of the structural features that account for the biological activity, the internal representations developed by the networks are analyzed by principal component analysis. This analysis shows that the networks are able to discover relevant structural features just on the basis of the association between the molecular morphology and the target property (affinity).

Benzodiazepines↗
PubMed2005

Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism.

Qualitative theoretical approaches such as graph theory and stoichiometric analyses are beginning to uncover the architecture and systemic functions of complex metabolic reaction networks. At present, however, only a few, largely unproven quantitative concepts propose functional design principles of the global flux distribution. As operational units of function, molecular fluxes determine the systemic cell phenotype by linking genes, proteins and metabolites to higher-level biological functions. In sharp contrast to other 'omics' analyses, 'fluxome' analysis remained tedious. By large-scale quantification of in vivo flux responses, we identified a robust flux distribution in 137 null mutants of Bacillus subtilis. On its preferred substrate, B. subtilis has suboptimal metabolism because regulators of developmental programs maintain a 'standby' mode that invests substantial resources in anticipation of changing environmental conditions at the expense of optimal growth. Network rigidity and robustness are probably universal functional design principles, whereas the standby mode may be more specific.

Acetyl Coenzyme A↗
PubMed2001

SPiD: a subtilis protein interaction database.

MOTIVATION: Protein-protein interactions are a potential source of valuable clues in determining the functional role of as yet uncharacterized gene products in metabolic pathways. Graph-like structures emerging from the accumulation of interaction data make it difficult to maintain a consistent and global overview by hand. Bioinformatics tools are needed to perform this graph visualization while maintaining a link to the experimental data. RESULTS: "SPiD" is an online database for exploring networks of interacting proteins in Bacillus subtilis characterized by the two-hybrid system. Graphical displays of interaction networks are created dynamically as users interactively navigate through these networks. Third party applications can interface the database through a Common Object Request Broker Architecture (CORBA) tier. AVAILABILITY: SPiD is available through its web site at http://www-mig.versailles.inra.fr/bdsi/SPiD, and through an Interoperable Object Reference (IOR) and its associated Interface Definition Language (IDL). CONTACT: hoebeke@versailles.inra.fr

Bacillus subtilis↗
PubMed2002

The complexity of comparing reaction systems.

MOTIVATION: As more genomic data becomes available there is increased attention on understanding the mechanisms encoded in the genome. New XML dialects like CellML and Systems Biology Markup Language (SBML) are being developed to describe biological networks of all types. In the absence of detailed kinetic information for these networks, stoichiometric data is an especially valuable source of information. Network databases are the next logical step beyond storing purely genomic information. Just as comparison of entries in genomic databases has been a vital algorithmic problem through the course of the sequencing project, comparison of networks in network databases will be a crucial problem as we seek to integrate higher-order network knowledge. RESULTS: We show that comparing the stoichiometric structure of two reactions systems is equivalent to the graph isomorphism problem. This is encouraging because graph isomorphism is, in practice, a tractable problem using heuristics. The analogous problem of searching for a subsystem of a reaction system is NP-complete. We also discuss heuristic issues in implementations for practical comparison of stoichiometric matrices.

Algorithms↗
PubMed2006

Using complexity for the estimation of Bayesian networks.

Statistical inference of graphical models has become an important tool in the reconstruction of biological networks of the type which model, for example, gene regulatory interactions. In particular, the construction of a score-based Bayesian posterior density over the space of models provides an intuitive and computationally feasible method of assessing model uncertainty and of assigning statistical confidence to structural features. One problem which frequently occurs with this approach is the tendency to overestimate the degree of model complexity. Spurious graphical features obtained in this way may affect the inference in unpredictable ways, even when using scoring techniques, such as the Bayesian Information Criterion (BIC), that are specifically designed to compensate for overfitting. In this article we propose a simple adjustment to a BIC-based scoring procedure. The method proceeds in two steps. In the first step we derive an independent estimate of the parametric complexity of the model. In the second we modify the BIC score so that the mean parametric complexity of the posterior density is equal to the estimated value. The method is applied to a set of test networks, and to a collection of genes from the yeast genome known to possess regulatory relationships. A Bayesian network model with binary responses is employed. In the examples considered, we find that the number of spurious graph edges inferred is reduced, while the effect on the identification of true edges is minimal.

Algorithms↗
PubMed1998

Conceptual integration of information databases into an Intranet.

Large information systems handle massive volume of data stored in heterogeneous sources of information. Each server has its own model of concepts representation with regard to its aims. One of the main problems encountered by end-users when accessing different servers is to match their own viewpoint on biomedical concepts with their various representations that are made in the database servers. The aim of the project ARIANE is to provide end-users with easy-to-use and natural means to access and query heterogeneous information databases. The objectives of this research work consist in building a conceptual interface by means of the Internet technology inside an enterprise Intranet, and to propose a method to realize it. Moreover, this method provides designers of web sites with a powerful tool to manage them on the basis of an ontology of the biomedical domain. This method is based on the knowledge sources provided by the Unified Medical Language System project of the U.S. National Library of Medicine and exploits intensively the conceptual graphs theory.

Computer Communication Networks↗
PubMed1996

The "SentiWeb" method for exploring a database on the Net.

Return of information is one of the main goals of any public health information system. About 25,000 maps and 10,000 graphs may be obtained from the time-series collected in the database of the French Communicable Diseases Network (FCDN). Furthermore, this huge epidemiological atlas is updated each week. What is the optimal way of returning such information? This report discloses the strategies used for enhancing the access facilities to the FCDN database for any users, particularly those without specific training in epidemiology or database query language. The technical options implemented in the SentiWeb server (http:/(/)www.b3e.jussieu.fr) are discussed.

Communicable Diseases↗
PubMed2006

Multiscale modeling of macromolecular conformational changes combining concepts from rigidity and elastic network theory.

The development of a two-step approach for multiscale modeling of macromolecular conformational changes is based on recent developments in rigidity and elastic network theory. In the first step, static properties of the macromolecule are determined by decomposing the molecule into rigid clusters by using the graph-theoretical approach FIRST and an all-atom representation of the protein. In this way, rigid clusters are not limited to consist of residues adjacent in sequence or secondary structure elements as in previous studies. Furthermore, flexible links between rigid clusters are identified and can be modeled as such subsequently. In the second step, dynamical properties of the molecule are revealed by the rotations-translations of blocks approach (RTB) using an elastic network model representation of the coarse-grained protein. In this step, only rigid body motions are allowed for rigid clusters, whereas links between them are treated as fully flexible. The approach was tested on a data set of 10 proteins that showed conformational changes on ligand binding. For efficiency, coarse-graining the protein results in a remarkable reduction of memory requirements and computational times by factors of 9 and 27 on average and up to 25 and 125, respectively. For accuracy, directions and magnitudes of motions predicted by our approach agree well with experimentally determined ones, despite embracing in extreme cases >50% of the protein into one rigid cluster. In fact, the results of our method are in general comparable with when no or a uniform coarse-graining is applied; and the results are superior if the movement is dominated by loop or fragment motions. This finding indicates that explicitly distinguishing between flexible and rigid regions is advantageous when using a simplified protein representation in the second step. Finally, motions of atoms in rigid clusters are also well predicted by our approach, which points to the need to consider mobile protein regions in addition to flexible ones when modeling correlated motions.

Elasticity↗
PubMed2003

An efficient approximation algorithm for finding a maximum clique using Hopfield network learning.

In this article, we present a solution to the maximum clique problem using a gradient-ascent learning algorithm of the Hopfield neural network. This method provides a near-optimum parallel algorithm for finding a maximum clique. To do this, we use the Hopfield neural network to generate a near-maximum clique and then modify weights in a gradient-ascent direction to allow the network to escape from the state of near-maximum clique to maximum clique or better. The proposed parallel algorithm is tested on two types of random graphs and some benchmark graphs from the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). The simulation results show that the proposed learning algorithm can find good solutions in reasonable computation time.

Algorithms↗
PubMed2005

A network representation of protein structures: implications for protein stability.

This study views each protein structure as a network of noncovalent connections between amino acid side chains. Each amino acid in a protein structure is a node, and the strength of the noncovalent interactions between two amino acids is evaluated for edge determination. The protein structure graphs (PSGs) for 232 proteins have been constructed as a function of the cutoff of the amino acid interaction strength at a few carefully chosen values. Analysis of such PSGs constructed on the basis of edge weights has shown the following: 1), The PSGs exhibit a complex topological network behavior, which is dependent on the interaction cutoff chosen for PSG construction. 2), A transition is observed at a critical interaction cutoff, in all the proteins, as monitored by the size of the largest cluster (giant component) in the graph. Amazingly, this transition occurs within a narrow range of interaction cutoff for all the proteins, irrespective of the size or the fold topology. And 3), the amino acid preferences to be highly connected (hub frequency) have been evaluated as a function of the interaction cutoff. We observe that the aromatic residues along with arginine, histidine, and methionine act as strong hubs at high interaction cutoffs, whereas the hydrophobic leucine and isoleucine residues get added to these hubs at low interaction cutoffs, forming weak hubs. The hubs identified are found to play a role in bringing together different secondary structural elements in the tertiary structure of the proteins. They are also found to contribute to the additional stability of the thermophilic proteins when compared to their mesophilic counterparts and hence could be crucial for the folding and stability of the unique three-dimensional structure of proteins. Based on these results, we also predict a few residues in the thermophilic and mesophilic proteins that can be mutated to alter their thermal stability.

Computer Simulation↗