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Results for “Network inference”

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PubMed1998

Cell transit analysis of ligand-induced stiffening of polymorphonuclear leukocytes.

A mathematical treatment of the mechanical behavior of transiently bonded polymer networks is used to interpret measurements of the pressure-induced passage of plant cells through microporous membranes. Cell transit times are inferred to be proportional to the instantaneous shear modulus of the cell cortex, a parameters that we then relate to properties of the cortical F-actin matrix. These theoretical results are used to analyze published data on chemoattractant-induced changes of rigidity of polymorphonuclear leukocytes. We thereby rationalize previously noted, peculiar, power-law logarithmic dependences of transit time on ligand concentration. As a consequence, we are able to deduce a linear relationship between the extent of F-actin polymerization and the logarithm of the chemoattractant concentration. The latter is examined with regard to the G-protein activation that is known to occur when chemoattractants bind to receptors on the surfaces of polymorphonuclear cells.

Actins↗

PubMed2006

Crystal structure of prephenate dehydrogenase from Aquifex aeolicus. Insights into the catalytic mechanism.

The enzyme prephenate dehydrogenase catalyzes the oxidative decarboxylation of prephenate to 4-hydroxyphenylpyruvate for the biosynthesis of tyrosine. Prephenate dehydrogenases exist as either monofunctional or bifunctional enzymes. The bifunctional enzymes are diverse, since the prephenate dehydrogenase domain is associated with other enzymes, such as chorismate mutase and 3-phosphoskimate 1-carboxyvinyltransferase. We report the first crystal structure of a monofunctional prephenate dehydrogenase enzyme from the hyper-thermophile Aquifex aeolicus in complex with NAD+. This protein consists of two structural domains, a modified nucleotide-binding domain and a novel helical prephenate binding domain. The active site of prephenate dehydrogenase is formed at the domain interface and is shared between the subunits of the dimer. We infer from the structure that access to the active site is regulated via a gated mechanism, which is modulated by an ionic network involving a conserved arginine, Arg250. In addition, the crystal structure reveals for the first time the positions of a number of key catalytic residues and the identity of other active site residues that may participate in the reaction mechanism; these residues include Ser126 and Lys246 and the catalytic histidine, His147. Analysis of the structure further reveals that two secondary structure elements, beta3 and beta7, are missing in the prephenate dehydrogenase domain of the bifunctional chorismate mutase-prephenate dehydrogenase enzymes. This observation suggests that the two functional domains of chorismate mutase-prephenate dehydrogenase are interdependent and explains why these domains cannot be separated.

Amino Acid Sequence↗

PubMed2004

Intervention in context-sensitive probabilistic Boolean networks.

MOTIVATION: Intervention in a gene regulatory network is used to help it avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is essentially a finite collection of Boolean networks in which at any discrete time point the gene state vector transitions according to the rules of one of the constituent networks. For an instantaneously random PBN, the governing Boolean network is randomly chosen at each time point. For a context-sensitive PBN, the governing Boolean network remains fixed for an interval of time until a binary random variable determines a switch. The theory of automatic control has been previously applied to find optimal strategies for manipulating external (control) variables that affect the transition probabilities of an instantaneously random PBN to desirably affect its dynamic evolution over a finite time horizon. This paper extends the methods of external control to context-sensitive PBNs. RESULTS: This paper treats intervention via external control variables in context-sensitive PBNs by extending the results for instantaneously random PBNs in several directions. First, and most importantly, whereas an instantaneously random PBN yields a Markov chain whose state space is composed of gene vectors, each state of the Markov chain corresponding to a context-sensitive PBN is composed of a pair, the current gene vector occupied by the network and the current constituent Boolean network. Second, the analysis is applied to PBNs with perturbation, meaning that random gene perturbation is permitted at each instant with some probability. Third, the (mathematical) influence of genes within the network is used to choose the particular gene with which to intervene. Lastly, PBNs are designed from data using a recently proposed inference procedure that takes steady-state considerations into account. The results are applied to a context-sensitive PBN derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation, since the available data suggest that disruption of this influence could reduce the chance of a melanoma metastasizing.

Algorithms↗

PubMed2003

LOC3D: annotate sub-cellular localization for protein structures.

LOC3D (http://cubic.bioc.columbia.edu/db/LOC3d/) is both a weekly-updated database and a web server for predictions of sub-cellular localization for eukaryotic proteins of known three-dimensional (3D) structure. Localization is predicted using four different methods: (i) PredictNLS, prediction of nuclear proteins through nuclear localization signals; (ii) LOChom, inferring localization through sequence homology; (iii) LOCkey, inferring localization through automatic text analysis of SWISS-PROT keywords; and (iv) LOC3Dini, ab initio prediction through a system of neural networks and vector support machines. The final prediction is based on the method that predicts localization with the highest confidence. The LOC3D database currently contains predictions for >8700 eukaryotic protein chains taken from the Protein Data Bank (PDB). The web server can be used to predict sub-cellular localization for proteins for which only a predicted structure is available from threading servers. This makes the resource of particular interest to structural genomics initiatives.

Animals↗

PubMed2000

Advection and diffusion of substances in biological tissues with complex vascular networks.

For highly diffusive solutes the kinetics of blood-tissue exchange is only poorly represented by a model consisting of sets of independent parallel capillary-tissue units. We constructed a more realistic multicapillary network model conforming statistically to morphometric data. Flows through the tortuous paths in the network were calculated based on constant resistance per unit length throughout the network and the resulting advective intracapillary velocity field was used as a framework for describing the extravascular diffusion of a substance for which there is no barrier or permeability limitation. Simulated impulse responses from the system, analogous to tracer water outflow dilution curves, showed flow-limited behavior over a range of flows from about 2 to 5 ml min(-1) g(-1), as is observed for water in the heart in vivo. The present model serves as a reference standard against which to evaluate computationally simpler, less physically realistic models. The simulated outflow curves from the network model, like experimental water curves, were matched to outflow curves from the commonly used axially distributed models only by setting the capillary wall permeability-surface area (PS) to a value so artifactually low that it is incompatible with the experimental observations that transport is flow limited. However, simple axially distributed models with appropriately high PSs will fit water outflow dilution curves if axial diffusion coefficients are set at high enough values to account for enhanced dispersion due to the complex geometry of the capillary network. Without incorporating this enhanced dispersion, when applied to experimental curves over a range of flows, the simpler models give a false inference that there is recruitment of capillary surface area with increasing flow. Thus distributed models must account for diffusional as well as permeation processes to provide physiologically appropriate parameter estimates.

Animals↗

PubMed2004

A first-draft human protein-interaction map.

BACKGROUND: Protein-interaction maps are powerful tools for suggesting the cellular functions of genes. Although large-scale protein-interaction maps have been generated for several invertebrate species, projects of a similar scale have not yet been described for any mammal. Because many physical interactions are conserved between species, it should be possible to infer information about human protein interactions (and hence protein function) using model organism protein-interaction datasets. RESULTS: Here we describe a network of over 70,000 predicted physical interactions between around 6,200 human proteins generated using the data from lower eukaryotic protein-interaction maps. The physiological relevance of this network is supported by its ability to preferentially connect human proteins that share the same functional annotations, and we show how the network can be used to successfully predict the functions of human proteins. We find that combining interaction datasets from a single organism (but generated using independent assays) and combining interaction datasets from two organisms (but generated using the same assay) are both very effective ways of further improving the accuracy of protein-interaction maps. CONCLUSIONS: The complete network predicts interactions for a third of human genes, including 448 human disease genes and 1,482 genes of unknown function, and so provides a rich framework for biomedical research.

Databases, Protein↗

PubMed2000

CADMIUM II: acquisition and representation of radiological knowledge for computerized decision support in mammography.

CADMIUM II is a system for the interpretation of mammograms. A novel aspect of the system is that it combines symbolic reasoning with image processing, in contrast with most other approaches, which use only image processing and rely on artificial neural networks (ANNs) to classify mammograms. A problem of ANNs is that the advice they give cannot be traced back to communicable diagnostic inferences. Our approach is to provide advice based on explicit knowledge about the diagnostic process. To this end, we have conducted a knowledge elicitation study which looked at the descriptors used by expert radiologists when making diagnostic decisions about mammograms. The analysis of the radiologists' reports yielded a set of salient diagnostic features. These were used to inform the advice provided by the symbolic decision making component of CADMIUM II.

Breast Diseases↗

PubMed2003

Modeling of pain using artificial neural networks.

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.

Acute Disease↗

PubMed2005

Finding biologically relevant protein domain interactions: conserved binding mode analysis.

Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.

Algorithms↗

PubMed1991

Network theory of glycosylation--etiologic and pathogenic implications of changes in IgG glycoform levels in autoimmunity.

It is now well established that glycoproteins are populations of individual glycoforms. While it has been inferred from in vitro experiments that the differential glycosylation of glycoproteins diversifies their function, evidence is lacking for such a role in vivo. Alterations in IgG glycosylation in both normal and disease states in vivo, however, provide strong evidence that glycosylation is not static and may be a highly regulated event. The large amount of data correlating disease activity and severity in autoimmune diseases which have a strong B cell component with changes in the incidence of IgG glycoforms, now suggest that glycoform population shifts may not be just a marker of disease activity, but may also contribute directly to disease persistence and pathogenesis.

Arthritis, Rheumatoid↗

PubMed2005

On the relationship between deterministic and probabilistic directed Graphical models: from Bayesian networks to recursive neural networks.

Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directed acyclic graph. In BNs, the conditional relationships between parent and child variables are probabilistic, whereas in RNNs they are deterministic and parameterized by neural networks. Here, we study the formal relationship between both classes of models and show that when the source nodes variables are observed, RNNs can be viewed as limits, both in distribution and probability, of BNs with local conditional distributions that have vanishing covariance matrices and converge to delta functions. Conditions for uniform convergence are also given together with an analysis of the behavior and exactness of Belief Propagation (BP) in 'deterministic' BNs. Implications for the design of mixed architectures and the corresponding inference algorithms are briefly discussed.

Bayes Theorem↗

PubMed2006

Population and systems genetics analyses of cortisol in pigs divergently selected for stress.

This study presents a systems genetic analysis on the physiology of cortisol in mice and pigs with an aim to show the potential of a comprehensive computational approach to quickly identify candidate genes and avoid a costly whole-genome quantitative trait locus (QTL) mapping. Population genetics analyses were performed on measurements of cortisol from a pig selection experiment. Expression QTL were mapped and gene networks were built using gene expressions for Crhr1 (corticotrophin-releasing hormone receptor) gene and single nucleotide polymorphisms from public mouse data. Results from mouse data were used to infer potential candidate regulatory genes involved in pig cortisol regulation, using a comparative or translational systems genetics approach. The pig data used were from a 10-yr divergent genetic selection experiment, providing data on 417 individuals. Population genetics analysis showed that cortisol is highly genetically determined with heritabilities of 0.40-0.70. Furthermore, a major gene with an additive effect of 86 ng/ml is segregating. Genetical-genomics investigations revealed two trans-acting eQTL for Crhr1 gene expression on chromosomes 2 and 13. Candidate gene search under trans-eQTL peaks yielded 63 genes for Crhr1 expression phenotypes. Functional links for Crhr1 genes with other genes/proteins in the gene network using mouse data were shown for the first 10 statistically significant genes involved. Results show translational or comparative systems genetics approaches reduce costs and time in large-scale genetics and "-omics" investigations. This is the first study to report a strong genetic basis for cortisol physiology using a systems approach.

Animals↗

PubMed2005

White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging.

Determination of axonal pathways provides an invaluable means to study the connectivity of the human brain and its functional network. Diffusion tensor imaging (DTI) is unique in its ability to capture the restricted diffusion of water molecules which can be used to infer the directionality of tissue components. In this paper, we introduce a white matter tractography method based on anisotropic wavefront propagation in diffusion tensor images. A front propagates in the white matter with a speed profile governed by the isocontour of the diffusion tensor ellipsoid. By using the ellipsoid, we avoid possible misclassification of the principal eigenvector in oblate regions. The wavefront evolution is described by an anisotropic version of the static Hamilton-Jacobi equation, which is solved by a sweeping method in order to obtain correct arrival times. Pathways of connection are determined by tracing minimum-cost trajectories using the characteristic vector field of the resulting partial differential equation. A validity index is described to rate the goodness of the resulting pathways with respect to the directionality of the tensor field. Connectivity results using normal human DTI brain images are illustrated and discussed. We also compared our method with a similar level set-based tractography technique, and found that the anisotropic evolution increased the validity index of the obtained pathways by 18%.

Algorithms↗

PubMed2003

Reverse engineering of genetic networks with Bayesian networks.

This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described. The article demonstrates an application to a simple synthetic toy problem and evaluates the inference performance in terms of ROC (receiver operator characteristic) curves.

Bayes Theorem↗

PubMed2003

Hierarchical Bayesian modeling of spatially correlated health service outcome and utilization rates.

We present Bayesian hierarchical spatial models for spatially correlated small-area health service outcome and utilization rates, with a particular emphasis on the estimation of both measured and unmeasured or unknown covariate effects. This Bayesian hierarchical model framework enables simultaneous modeling of fixed covariate effects and random residual effects. The random effects are modeled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighboring areas. The model inference is implemented using Markov chain Monte Carlo methods. Specifically, a hybrid Markov chain Monte Carlo algorithm (Neal, 1995, Bayesian Learning for Neural Networks; Gustafson, MacNab, and Wen, 2003, Statistics and Computing, to appear) is used for posterior sampling of the random effects. To illustrate relevant problems, methods, and techniques, we present an analysis of regional variation in intraventricular hemorrhage incidence rates among neonatal intensive care unit patients across Canada.

Bayes Theorem↗

PubMed2004

Systematic quantification of gene interactions by phenotypic array analysis.

A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth.

Cluster Analysis↗

PubMed2000

[Experimental study on effect of bushen yijing recipe in delaying senility of bone and brain of aged male rats].

OBJECTIVE: To evaluate the effect of Bushen Yijing recipe (BSYJR) in delaying senility of bone and brain of aged male rats and infer its mechanism in delaying systemic senility. METHODS: Forty male SD rats, 24 months old were randomly divided into 4 groups, the baseline control group, the aged control group (30 months old), the BSYJR high dose group and the BSYJR low dose group. The latter two groups received BSYJR treatment from 24 months old to 30 months old. Bone indexes (bone density of the proximal, middle and distal segments of left femur and break bending load of right femur) and brain indexes (binding capacity of M receptor and cholinesterase activity of brain) were measured after responding treatment. RESULTS: In bone, BSYJR could not only increase the bone mineral density in various segments of femur, but also raise the bending break load of femur dose-effect dependently. In brain, BSYJR could both up-regulate the binding capacity of M receptor and inhibit the activity of cholinesterase. CONCLUSION: BSYJR could delay the senility of bone and brain in male rats, inferring that it might regulate integrally the abnormality of aging in Kidney Asthenia and Essence Deficiency through mediation of nerve-endocrine-immunity network.

Aging↗

PubMed1992

HIV prevalence among intravenous drug users: model-based estimates from New Haven's legal needle exchange.

The legal needle exchange in New Haven, Connecticut, was signed into law in July 1990. As part of a rigorous effort to evaluate this program, all distributed syringes receive unique tracking codes, and a sample of returned needles are tested for the presence of HIV proviral DNA via polymerase chain reaction. These data, in conjunction with "back-of-the-envelope" statistical models, allow the estimation of HIV prevalence among those intravenous drug users participating in the needle exchange. We present four new techniques for calculating prevalence estimates: the majority rule cutoff model, the random sharing model, the sharing network model, and a version of Kaplan's "needles that kill" model. To our knowledge, these estimates are the first that attempt to infer HIV prevalence among intravenous drug users via syringe tracking and testing. All four techniques suggest a prevalence of infection of approximately 60%.

Connecticut↗