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Application of a generalized MWC model for the mathematical simulation of metabolic pathways regulated by allosteric enzymes

Application of a generalized MWC model for the mathematical ...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： T.S. Najdi C.-R. Yang B.E. Shapiro G.W. Hatfield B.E. Mjolsness Department of Microbiology and Molecular Genetics College of Medicine University of California Irvine CA USA University of California Irvine Irvine CA US Inst. for Genomics & Bioinformatics California Univ. Irvine CA USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

In our effort to elucidate the systems biology of the model organism, Escherichia coli, we have developed a mathematical model that simulates the allosteric regulation for threonine biosynthesis pathway starting from aspartate. To achieve this goal, we used kMech, a Cellerator language extension that describes enzyme mechanisms for the mathematical modeling of metabolic pathways. These mechanisms are converted by Cellerator into ordinary differential equations (ODEs) solvable by Mathematica/spl trade/. In this paper, we describe a more flexible model in Cellerator, which generalizes the Monod, Wyman, Changeux (MWC) model for enzyme allosteric regulation to allow for multiple substrate, activator and inhibitor binding sites. Furthermore, we have developed a model that describes the behavior of the bifunctional allosteric enzyme aspartate Kinase I-Homoserine Dehydrogenase I (AKI-HDHI). This model predicts the partition of enzyme activities in the steady state which paves a way for a more generalized prediction of the behavior of bifunctional enzymes.

关键词： Mathematical model Biochemistry Biological system modeling systems biology Organisms Amino acids Differential equations Inhibitors Predictive models Steady-state

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Gene teams with relaxed proximity constraint

Gene teams with relaxed proximity constraint

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S. Kim J.-H. Choi J. Yang School ofInformatics Center for Genomics and Bioinformatics Indiana University IN USA School of Informatics Indiana University IN USA EECS Case Western Reserve University OH USA

ISBN: (纸本)0769523447

Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes. However there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally verified operons and functionally related clusters. Our program is fast enough to provide a service on the web at http://***/platcom/. Users can select any combination of 97 genomes to predict gene teams.

关键词： bioinformatics Genomics Informatics Sun Testing Predictive models Protein sequence Databases Gene expression Physics computing

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Multi-metric and multi-substructure biclustering analysis for gene expression data

Multi-metric and multi-substructure biclustering analysis fo...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S.Y. Kung Man-Wai Mak I. Tagkopoulos Princeton University USA Hong Kong Polytechnic University Hong Kong China

A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while presenting the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.

关键词： Gene expression Cancer Neoplasms Matrix converters Drugs Data analysis bioinformatics Vehicles Coherence Fungi

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Tree decomposition based fast search of RNA structures including pseudoknots in genomes

Tree decomposition based fast search of RNA structures inclu...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Y. Song C. Liu R. Malmberg F. Pan L. Cai Department of Computer Science University of Georgia Athens GA USA Department of Plant Biology University of Georgia Athens GA USA

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t=2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k/sup t/N/sup 2/), where k is a small parameter, and N is the size of the profiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a covariance model with a significant reduction in computation time. In particular, very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site http://***/RNA-Informatics/software/***.

关键词： RNA Genomics bioinformatics Computer science Tree graphs Plants (biology) computational biology Biology computing Sequences Stochastic systems

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A robust meta-classification strategy for cancer diagnosis from gene expression data

A robust meta-classification strategy for cancer diagnosis f...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： G. Alexe G. Bhanot B. Venkataraghavan R. Ramaswamy J. Lepre A.J. Levine G. Stolovitzky IBM Computational Biology Center IBM Thomas J. Watson Research Center Yorktown Heights NY USA IBM T.J. Watson Research IBM Computational Biology Center Yorktown Heights NY USA IBM Computational Biology Center Institute for Advanced Study Einstein Drive Princeton NJ USA Center for Systems Biology Institute for Advanced Study Einstein Drive Princeton NJ USA Center for Systems Biology Institute of Advanced Study in Science and Technology Princeton NJ USA Center for Systems Biology Cancer Institute of New Jersey Einstein Drive Princeton New Brunswick USA

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome. wi. mit. du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

关键词： Cancer Robustness Gene expression USA Councils computational biology systems biology Biological system modeling Laboratories bioinformatics Data mining

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Discriminative discovery of transcription factor binding sites from location data

Discriminative discovery of transcription factor binding sit...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Y. Kawada Y. Sakakibara Department of Biosciences and Informatics Keio University Yokohama Japan

The availability of genome-wide location analyses based on chromatin immunoprecipitation (CMP) data gives a new insight for in silico analysis of transcriptional regulations. We propose a novel discriminative discovery framework for precisely identifying transcriptional regulatory motifs from both positive and negative samples (sets of upstream sequences of both bound and unbound genes by a transcription factor (TF)) based on the genome-wide location data. In this framework, our goal is to find such discriminative motifs that best explain the location data in the sense that the motifs precisely discriminate the positive samples from the negative ones. First, in order to discover an initial set of discriminative substrings between positive and negative samples, we apply a decision tree learning method which produces a text-classification tree. We extract several clusters consisting of similar substrings from the internal nodes of the learned tree. Second, we start with initial profile-HMMs constructed from each cluster for representing putative motifs and iteratively refine the profile-HMMs to improve the discrimination accuracies. Our genome-wide experimental results on yeast show that our method successfully identifies the consensus sequences for known TFs in the literature and further presents significant performances for discriminating between positive and negative samples in all the TFs, while most other motif detecting methods show very poor performances on the problem of discriminations. Our learned profile-HMMs also improve false negative predictions of ChIP data.

关键词： bioinformatics Genomics Decision trees Data mining Fungi Machine learning Hidden Markov models Histograms Informatics Availability

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Consensus genetic maps: a graph theoretic approach

Consensus genetic maps: a graph theoretic approach

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： B.N. Jackson S. Aluru P.S. Schnable Department of Electrical and Computer Engineering Iowa State University Ames IA USA Center for Plant Genomics Iowa State University Ames IA USA

A genetic map is an ordering of genetic markers constructed from genetic linkage data for use in linkage studies and experimental design. While traditional methods have focused on constructing maps from a single population study, increasingly maps are generated for multiple lines and populations of the same organism. For example, in crop plants, where the genetic variability is high, researchers have created maps for many populations. In the face of these new data, we address the increasingly important problem of generating a consensus map an ordering of all markers in the various population studies. In our method, each input map is treated as a partial order on a set of markers. To find the most consistent order shared between maps, we model the partial orders as directed graphs. We create an aggregate by merging the transitive closure of the input graphs and taking the transitive reduction of the result. In this process, cycles may need to be broken to resolve inconsistencies between the inputs. Tlie cycle breaking problem is NP-hard, but the problem size depends upon the scope of the inconsistency between the input graphs, which will be local if the input graphs are from closely related organisms. We present results of running the resulting software on maps generated from seven populations of the crop plant Zea Mays.

关键词： Biological cells Organisms bioinformatics Genomics Couplings Data engineering Genetic engineering Design engineering Crops Design for experiments

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Clustering genes using gene expression and text literature data

Clustering genes using gene expression and text literature d...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： C. Yang E. Zeng T. Li G. Narasimhan Bioinformatics Research Group (BioRG) School of Computer Science Florida International University Miami FL USA

Clustering of gene expression data is a standard technique used to identify closely related genes. In this paper, we develop a new clustering algorithm, MSC (Multi-Source Clustering), to perform exploratory analysis using two or more diverse sources of data. In particular, we investigate the problem of improving the clustering by integrating information obtained from gene expression data with knowledge extracted from biomedical text literature. In each iteration of algorithm MSC, an EM-type procedure is employed to bootstrap the model obtained from one data source by starting with the cluster assignments obtained in the previous iteration using the other data sources. Upon convergence, the two individual models are used to construct the final cluster assignment. We compare the results of algorithm MSC for two data sources with the results obtained when the clustering is applied on the two sources of data separately. We also compare it with that obtained using the feature level integration method that performs the clustering after simply concatenating the features obtained from the two data sources. We show that the z-scores of the clustering results from MSC are better than that from the other methods. To evaluate our clusters better, function enrichment results are presented using terms from the Gene Ontology database. Finally, by investigating the success of motif detection programs that use the clusters, we show that our approach integrating gene expression data and text data reveals clusters that are biologically more meaningful than those identified using gene expression data alone.

关键词： Gene expression Clustering algorithms Data mining bioinformatics Data analysis Databases Information analysis Computer science Performance analysis Algorithm design and analysis

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A learned comparative expression measure for Affymetrix genechip DNA microarrays

A learned comparative expression measure for Affymetrix gene...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： W. Sheffler E. Upfal J. Sedivy W.S. Noble Department of Genome Sciences University of Washington USA Department of Computer Science Brown University USA Department of Molecular and Cell Biology and Biochemistry Brown University USA Department of Genome Sciences Department of Computer Science and Engineering University of Washington USA

Perhaps the most common question that a microarray study can ask is, "Between two given biological conditions, which genes exhibit changed expression levels?" Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting small expression changes. At the levels of selectivity that are typical in microarray analysis, the LCEM shows a lower false discovery rate than either MAS5 or RMA trained from a single chip. When many chips are available to RMA, LCEM performs better on two out of the three data sets, and nearly as well on the third. Performance of the MAS5 log ratio statistic was notably bad on all datasets.

关键词： DNA Probes bioinformatics Support vector machines Testing Semiconductor device measurement Genomics Computer science Gene expression Statistics

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1st International Symposium on computational and Information Science, CIS 2004

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1st International Symposium on computational and Information Science, CIS 2004

ISBN: (纸本)9783540241270

The proceedings contain 190 papers. The special focus in this conference is on computational and Information Science. The topics include: High order finite difference schemes for the solution of elliptic PDEs;an algorithm for optimal tuning of fuzzy PID controllers on precision measuring device;a grid portal model based on security and storage resource proxy;optimal designs of directed double-loop networks;a QoS-based access and scheduling algorithm for wireless multimedia communications;feedforward wavelet neural network and multi-variable functional approximation;the distributed wavelet-based fusion algorithm;alternating direction finite element method for a class of moving boundary problems;binomial-tree fault tolerant routing in dual-cubes with large number of faculty nodes;an effective compressed sparse preconditioner for large scale biomolecular simulations;a study on lower bound of direct proportional length-based DNA computing for shortest path problem;key management for secure multicast using the ringnet hierarchy;open middleware-based infrastructure for context-aware in pervasive computing;boundary integral simulation of the motion of highly deformable derops in a viscous flow with spontaneous marangoni effect;solving separable nonlinear equations with jacobians of rank deficiency one;optimal capacity expansion arc algorithm on networks;solving non-linear finite difference systems by normalized approximate inverses;an adaptive two-dimensional mesh refinement method for the problems in fluid engineering;high order locally one-dimensional method for parabolic problems and networked control system design accounting for delay information.

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