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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaoyong Sun Di Wu Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Western Kentucky University Bowling Green KY USA Department of Biochemistry Biophysics and Molecular Biology Department of Mathematics Iowa State University Ames IA USA

PRTAD is a dedicated database and structural bioinformatics system for protein analysis and modeling. The database is developed to host and analyze the statistical data for protein residue level "virtual" bond and torsion angles obtained from their distributions in databases of known protein structures such as in the PDB Data Bank. PRTAD is capable of generating, caching, and displaying the statistical distributions of the angles of various types. The collected information can be used to extract geometric restraints or define statistical potentials for protein structure determination. PRTAD is supported with a friendly designed web interface so that users can easily specify the angle types, and retrieve, visualize, or download the distributions of the angles as they desire. The database PRTAD is freely accessible at http://***/prtad.

关键词： Bonding Spatial databases Protein engineering Amino acids bioinformatics Computational biology mathematics Data mining Niobium compounds Nuclear magnetic resonance

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Modeling protein interaction network and mechanisms in exocytosis

Modeling protein interaction network and mechanisms in exocy...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Zhou, Wen Xia, Tian Tong, Jiansong Dickerson, Julie Su, Bo Gu, Xun Department of Mathematics Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology Iowa State University Ames IA 50010 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50010 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50010 United States

ISBN: (纸本)1424415098

Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles which contain a wide range of intracellular molecules such as hormones, matrix proteins and neurotransmitters. Recent studies have shown this process is regulated by molecular interactions among a group of well defined and conserved proteins. To gain insight into the dynamics of these interactions, we use protein interaction network modeling to investigate exocytotic system (particularly in endocrine) computationally and mathematically. The protein interactions are formulated into ordinary differential equations (ODE). We then apply a novel mathematic approach to estimate model parameters from experimental data for SNAREs-only network (with three key proteins), which is a subset of this system. Our approach is able to sense temporal changes in protein concentration and ratios of multiple proteins and precisely reconstruct the dynamical process of exocytosis, including vesicle docking, priming and fusion. Additionally, the model suggests that initial concentration of synaptic proteins plays a crucial role in efficiency of vesicle fusion, based on system stability analysis. ©2007 IEEE.

关键词： Proteins

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Modeling protein interaction network and mechanisms in exocytosis

Modeling protein interaction network and mechanisms in exocy...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Wen Zhou Tian Xia Jiansong Tong Julie Dickerson Bo Su Xun Gu Department of Mathematics Iowa State University Ames IA USA Program of Bioinformatics and computational biology Iowa State University Ames IA USA Department of Biochemistry Iowa State University Ames IA USA Department of Mathematics Iowa State University Ames IA Department of Genetics Iowa State University Ames IA USA

关键词： Proteins Mathematical model Biochemistry Neurotransmitters Endocrine system Computer networks Differential equations mathematics Parameter estimation Stability analysis

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Finding phylogenetically informative genes by estimating multispecies gene entropy

Finding phylogenetically informative genes by estimating mul...

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International Joint Conference on Neural Networks 2006, IJCNN '06

作者： Xiaoxu, Han Department of Mathematics Bioinformatics Program Eastern Michigan University Ypsilanti MI 48197 United States

ISBN: (纸本)0780394909

Although entropy and relative entropy (K-L distance) are widely applied in many bioinformatics areas, there is no method given to compute the multispecies gene entropy yet. This paper presents the first multispecies gene entropy estimation method from the data mining point of view. In this study, a self-organizing map (SOM) is employed to mine a multispecies gene set to obtain the probability distribution of each gene in the feature space, which is the approximation of its corresponding probability distribution in the original sequence space. The multispecies gene entropy is computed by the probability distribution of a gene in the feature space. The phylogenetic applications of the multispecies gene entropy are investigated in an example of resolving incongruence between gene trees and species trees. It is found that genes with nearest K-L distances to the minimum entropy gene are more likely to be phylogenetically informative. A K-L distance based gene concatenation approach under gene clustering is proposed to resolve the gene tree and species tree problem. Under the same testing dataset, the K-L distance based approach not only avoids the ad-hoc mechanism of the original gene concatenation method but also is easy to extend to other dataset and free from prohibitive phylogenetic computing from large number of taxa. © 2006 IEEE.

关键词： Gene expression

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Finding Phylogenetically Informative Genes by Estimating Multispecies Gene Entropy

Finding Phylogenetically Informative Genes by Estimating Mul...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoxu Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI USA

关键词： Entropy Sequences DNA Probability distribution bioinformatics Phylogeny Data mining Distributed computing Testing Information analysis

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A machine learning approach to resolving incongruence in molecular phylogenies and visualization analysis

A machine learning approach to resolving incongruence in mol...

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2005 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB '05

作者： Xiaoxu, Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI 48197 United States

ISBN: (纸本)0780393872

The incongruence between gene trees and species trees is one of the most pervasive challenges in molecular phylogenetics. In this work, a machine learning approach is proposed to overcome this problem. In the machine learning approach, the gene data set is clustered by a self-organizing map (SOM). Then a phylogenetically informative core gene set is created by combining the maximum entropy gene from each cluster to conduct phylogenetic analysis. Using the same data set, this approach performs better than the previous random gene concatenation method. The SOM based information visualization is also employed to compare the species patterns in the phylogenetic tree constructions. © 2005 IEEE.

关键词： Learning systems

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and Computational Biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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Training finite state classifiers to improve PCR primer design

Training finite state classifiers to improve PCR primer desi...

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Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

作者： Ashlock, Daniel A. Bryden, K.M. Corns, Steven M. Schnable, Patrick S. Wen, T. Iowa State University Ames IA 50011 Mathematics Department Bioinformatics Program Department of Mechanical Engineering Agronomy Department

ISBN: (纸本)1563477165

We present results on training finite state machines as classifiers for polymerase chain reaction primers. The goal is to decrease the number of primers that fail to amplify correctly. Finite state classifiers are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multi-population hybridization. The system presented here creates a post-production add-on to a standard primer picking program intended to compensate for organism- and lab-specific factors. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Enzymes

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A comparison of evolved finite state classifiers and interpolated Markov models for improving PCR primer design

A comparison of evolved finite state classifiers and interpo...

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Proceedings of the 2004 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB'04

作者： Ashlock, Daniel A. Emrich, Scott J. Bryden, Kenneth M. Corns, Steve M. Wen, Tsui-Jung Schnable, Patrick S. Mathematics Department Iowa State University Ames IA 50011 Bioinformatics Program Iowa State University Ames IA 50011 Mechanical Engineering Iowa State University Ames IA 5001 Agronomy Department Iowa State University Ames IA 50011

ISBN: (纸本)0780387287

This presents results on training both finite state classifiers and interpolated Markov models as classifiers for polymerase chain reaction primers. The goal of the study is to find techniques to decrease the number of primers that fail to amplify correctly within a large genomics project. Standard primer design packages already select primers in a manner consistent with current knowledge of the biophysics of DNA. The classifiers trained in this effort are used to capture lab and organism specific features of primer data and are used to post-process the output of standard primer design packages. The finite state classifiers in this study are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multi-population hybridization. This hybridization is akin to population seeding, not the more usual hybridization of evolutionary computation with other techniques. The interpolated Markov model is a form of Markov model that adapts to data rich and data sparse portions of the training set by using a variable order in its modeling. The interpolated Markov models exhibited slightly superior performance and trains with far higher speed. The finite state classifiers provide a substantially different classification, however, and require less training data. ©2004 IEEE.

关键词： Enzymes

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A comparison of the robustness of evolutionary computation and random walks

A comparison of the robustness of evolutionary computation a...

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Congress on Evolutionary Computation

作者： J. Schonfeld D.A. Ashlock Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Mathematics Department Iowa State University Ames IA USA

Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model systems that are easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection evolutionary algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.

关键词： Robustness Evolutionary computation Genetic mutations Evolution (biology) Biological systems Proteins Stochastic processes Systems engineering and theory Stochastic systems bioinformatics

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