The Experts below are selected from a list of 306087 Experts worldwide ranked by ideXlab platform
Daphne Koller - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic discovery of overlapping Cellular Processes and their regulation.
Journal of Computational Biology, 2005Co-Authors: Alexis Battle, Eran Segal, Daphne KollerAbstract:In this paper, we explore modeling overlapping biological Processes. We discuss a probabilistic model of overlapping biological Processes, gene membership in those Processes, and an addition to that model that identifies regulatory mechanisms controlling process activation. A key feature of our approach is that we allow genes to participate in multiple Processes, thus providing a more biologically plausible model for the process of gene regulation. We present algorithms to learn each model automatically from data, using only genomewide measurements of gene expression as input. We compare our results to those obtained by other approaches and show that significant benefits can be gained by modeling both the organization of genes into overlapping Cellular Processes and the regulatory programs of these Processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.
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probabilistic discovery of overlapping Cellular Processes and their regulation
Research in Computational Molecular Biology, 2004Co-Authors: Alexis Battle, Eran Segal, Daphne KollerAbstract:Many of the functions carried out by a living cell are regulated at the transcriptional level, to ensure that genes are expressed when they are needed. Thus, to understand biological Processes, it is thus necessary to understand the cell's transcriptional network. In this paper, we propose a novel probabilistic model of gene regulation for the task of identifying overlapping biological Processes and the regulatory mechanism controlling their activation. A key feature of our approach is that we allow genes to participate in multiple Processes, thus providing a more biologically plausible model for the process of gene regulation. We present an algorithm to learn this model automatically from data, using only genome-wide measurements of gene expression as input. We compare our results to those obtained by other approaches, and show significant benefits can be gained by modeling both the organization of genes into overlapping Cellular Processes and the regulatory programs of these Processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.
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decomposing gene expression into Cellular Processes
Pacific Symposium on Biocomputing, 2002Co-Authors: Eran Segal, Alexis Battle, Daphne KollerAbstract:We propose a probabilistic model for Cellular Processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that participate in it; unlike clustering techniques, our model allows genes to participate in multiple Processes. Each process may be active to a different degree in each experiment. The expression measurement for gene g in array a is a sum, over all Processes in which g participates, of the activity levels of these Processes in array a. We describe an iterative procedure, based on the EM algorithm, for decomposing the expression matrix into a given number of Processes. We present results on Yeast gene expression data, which indicate that our approach identifies real biological Processes.
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Pacific Symposium on Biocomputing - Decomposing gene expression into Cellular Processes.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2002Co-Authors: Eran Segal, Alexis Battle, Daphne KollerAbstract:We propose a probabilistic model for Cellular Processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that participate in it; unlike clustering techniques, our model allows genes to participate in multiple Processes. Each process may be active to a different degree in each experiment. The expression measurement for gene g in array a is a sum, over all Processes in which g participates, of the activity levels of these Processes in array a. We describe an iterative procedure, based on the EM algorithm, for decomposing the expression matrix into a given number of Processes. We present results on Yeast gene expression data, which indicate that our approach identifies real biological Processes.
Donald L Court - One of the best experts on this subject based on the ideXlab platform.
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probing Cellular Processes with oligo mediated recombination and using the knowledge gained to optimize recombineering
Journal of Molecular Biology, 2011Co-Authors: James A Sawitzke, Nina Costantino, Xintian Li, Lynn C Thomason, Mikhail Bubunenko, Carolyn Court, Donald L CourtAbstract:Recombination with single-strand DNA oligonucleotides (oligos) in Escherichia coli is an efficient and rapid way to modify replicons in vivo. The generation of nucleotide alteration by oligo recombination provides novel assays for studying Cellular Processes. Single-strand exonucleases inhibit oligo recombination, and recombination is increased by mutating all four known exonucleases. Increasing oligo concentration or adding nonspecific carrier oligo titrates out the exonucleases. In a model for oligo recombination, λ Beta protein anneals the oligo to complementary single-strand DNA at the replication fork. Mismatches are created, and the methyl-directed mismatch repair (MMR) system acts to eliminate the mismatches inhibiting recombination. Three ways to evade MMR through oligo design include, in addition to the desired change (1) a C·C mismatch 6 bp from that change; (2) four or more adjacent mismatches; or (3) mismatches at four or more consecutive wobble positions. The latter proves useful for making high-frequency changes that alter only the target amino acid sequence and even allows modification of essential genes. Efficient uptake of DNA is important for oligo-mediated recombination. Uptake of oligos or plasmids is dependent on media and is 10,000-fold reduced for cells grown in minimal versus rich medium. Genomewide engineering technologies utilizing recombineering will benefit from both optimized recombination frequencies and a greater understanding of how biological Processes such as DNA replication and cell division impact recombinants formed at multiple chromosomal loci. Recombination events at multiple loci in individual cells are described here.
Monika Janczarek - One of the best experts on this subject based on the ideXlab platform.
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Transcriptome profiling of a Rhizobium leguminosarum bv. trifolii rosR mutant reveals the role of the transcriptional regulator RosR in motility, synthesis of cell-surface components, and other Cellular Processes
BMC Genomics, 2015Co-Authors: Kamila Rachwał, Ewa Matczyńska, Monika JanczarekAbstract:Background Rhizobium leguminosarum bv. trifolii is a soil bacterium capable of establishing a symbiotic relationship with red clover ( Trifolium pratense ). The presence of surface polysaccharides and other extraCellular components as well as motility and competitiveness are essential traits for both adaptation of this bacterium to changing environmental conditions and successful infection of host plant roots. The R. leguminosarum bv. trifolii rosR gene encodes a protein belonging to the family of Ros/MucR transcriptional regulators, which contain a Cys_2His_2-type zinc-finger motif and are involved in the regulation of exopolysaccharide synthesis in several rhizobial species. Previously, it was established that a mutation in the rosR gene significantly decreased exopolysaccharide synthesis, increased bacterial sensitivity to some stress factors, and negatively affected infection of clover roots. Results RNA-Seq analysis performed for the R. leguminosarum bv. trifolii wild-type strain Rt24.2 and its derivative Rt2472 carrying a rosR mutation identified a large number of genes which were differentially expressed in these two backgrounds. A considerable majority of these genes were up-regulated in the mutant (63.22 %), indicating that RosR functions mainly as a repressor. Transcriptome profiling of the rosR mutant revealed a role of this regulator in several Cellular Processes, including the synthesis of cell-surface components and polysaccharides, motility, and bacterial metabolism. Moreover, it was established that the Rt2472 strain was characterized by a longer generation time and showed an increased aggregation ability, but was impaired in motility as a result of considerably reduced flagellation of its cells. Conclusions The comparative transcriptome analysis of R. leguminosarum bv. trifolii wild-type Rt24.2 and the Rt2472 mutant identified a set of genes belonging to the RosR regulon and confirmed the important role of RosR in the regulatory network. The data obtained in this study indicate that this protein affects several Cellular Processes and plays an important role in bacterial adaptation to environmental conditions.
John Mclauchlan - One of the best experts on this subject based on the ideXlab platform.
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properties of the hepatitis c virus core protein a structural protein that modulates Cellular Processes
Journal of Viral Hepatitis, 2000Co-Authors: John MclauchlanAbstract:The core protein of hepatitis C virus (HCV) is believed to form the capsid shell of virus particles. Maturation of the protein is achieved through cleavage by host cell proteases to give a product of 21 000 MW, which is found in tissue culture systems and sera from infected individuals. However, efficient propagation of the virus is not possible at present in tissue culture. Hence, studies have focused on the properties of the core protein and its possible role in pathologies associated with HCV infection. This review describes key features of the polypeptide and the status of current knowledge on its ability to influence several Cellular Processes.
Alexis Battle - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic discovery of overlapping Cellular Processes and their regulation.
Journal of Computational Biology, 2005Co-Authors: Alexis Battle, Eran Segal, Daphne KollerAbstract:In this paper, we explore modeling overlapping biological Processes. We discuss a probabilistic model of overlapping biological Processes, gene membership in those Processes, and an addition to that model that identifies regulatory mechanisms controlling process activation. A key feature of our approach is that we allow genes to participate in multiple Processes, thus providing a more biologically plausible model for the process of gene regulation. We present algorithms to learn each model automatically from data, using only genomewide measurements of gene expression as input. We compare our results to those obtained by other approaches and show that significant benefits can be gained by modeling both the organization of genes into overlapping Cellular Processes and the regulatory programs of these Processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.
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probabilistic discovery of overlapping Cellular Processes and their regulation
Research in Computational Molecular Biology, 2004Co-Authors: Alexis Battle, Eran Segal, Daphne KollerAbstract:Many of the functions carried out by a living cell are regulated at the transcriptional level, to ensure that genes are expressed when they are needed. Thus, to understand biological Processes, it is thus necessary to understand the cell's transcriptional network. In this paper, we propose a novel probabilistic model of gene regulation for the task of identifying overlapping biological Processes and the regulatory mechanism controlling their activation. A key feature of our approach is that we allow genes to participate in multiple Processes, thus providing a more biologically plausible model for the process of gene regulation. We present an algorithm to learn this model automatically from data, using only genome-wide measurements of gene expression as input. We compare our results to those obtained by other approaches, and show significant benefits can be gained by modeling both the organization of genes into overlapping Cellular Processes and the regulatory programs of these Processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.
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decomposing gene expression into Cellular Processes
Pacific Symposium on Biocomputing, 2002Co-Authors: Eran Segal, Alexis Battle, Daphne KollerAbstract:We propose a probabilistic model for Cellular Processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that participate in it; unlike clustering techniques, our model allows genes to participate in multiple Processes. Each process may be active to a different degree in each experiment. The expression measurement for gene g in array a is a sum, over all Processes in which g participates, of the activity levels of these Processes in array a. We describe an iterative procedure, based on the EM algorithm, for decomposing the expression matrix into a given number of Processes. We present results on Yeast gene expression data, which indicate that our approach identifies real biological Processes.
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Pacific Symposium on Biocomputing - Decomposing gene expression into Cellular Processes.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2002Co-Authors: Eran Segal, Alexis Battle, Daphne KollerAbstract:We propose a probabilistic model for Cellular Processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that participate in it; unlike clustering techniques, our model allows genes to participate in multiple Processes. Each process may be active to a different degree in each experiment. The expression measurement for gene g in array a is a sum, over all Processes in which g participates, of the activity levels of these Processes in array a. We describe an iterative procedure, based on the EM algorithm, for decomposing the expression matrix into a given number of Processes. We present results on Yeast gene expression data, which indicate that our approach identifies real biological Processes.
