The Experts below are selected from a list of 109734 Experts worldwide ranked by ideXlab platform
Bert Gold - One of the best experts on this subject based on the ideXlab platform.
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or ‘omic’) data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer‐ associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancerassociated genes. Combinations of mutated and/or aberrantly expressed tumor suppressor genes and oncogenes, or ‘cancer genes’, are thought to be responsible for most steps of cancer progression. Although fundamental principles have emerged from the study of known cancer genes and their products, many questions remain unanswered. Notably,
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancer-associated genes.
Miguel Angel Pujana - One of the best experts on this subject based on the ideXlab platform.
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or ‘omic’) data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer‐ associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancerassociated genes. Combinations of mutated and/or aberrantly expressed tumor suppressor genes and oncogenes, or ‘cancer genes’, are thought to be responsible for most steps of cancer progression. Although fundamental principles have emerged from the study of known cancer genes and their products, many questions remain unanswered. Notably,
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancer-associated genes.
Van Der Ma Martin Hoef - One of the best experts on this subject based on the ideXlab platform.
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numerical simulation of dense gas solid fluidized beds a multiscale Modeling Strategy
Annual Review of Fluid Mechanics, 2008Co-Authors: Van Der Ma Martin Hoef, N Niels G Deen, Van Martin Sint M Annaland, Jam Hans KuipersAbstract:Gas-solid fluidized beds are widely applied in many chemical processes involving physical and/or chemical transformations, and for this reason they are the subject of intense research in chemical engineering science. Over the years, researchers have developed a large number of numerical models of gas-fluidized beds that describe gas-solid flow at different levels of detail. In this review, we discriminate these models on the basis of whether a Lagrangian or a Eulerian approach is used for the gas and/or particulate flow and subsequently classify them into five main categories, three of which we discuss in more detail. Specifically, these are resolved discrete particle models (also called direct numerical simulations), unresolved discrete particle models (also called discrete element models), and two-fluid models. For each of the levels of description, we give the general equations of motion and indicate how they can be solved numerically by finite-difference techniques, followed by some illustrative examples of a fluidized bed simulation. Finally, we address some of the challenges ahead in the multiscale Modeling of gas-fluidized beds
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computational fluid dynamics for dense gas solid fluidized beds a multi scale Modeling Strategy
China Particuology, 2005Co-Authors: Van Der Ma Martin Hoef, M Van Sint Annaland, J A M KuipersAbstract:Dense gas-particle flows are encountered in a variety of industrially important processes for large scale production of fuels, fertilizers and base chemicals. The scale-up of these processes is often problematic and is related to the intrinsic complexities of these flows which are unfortunately not yet fully understood despite significant efforts made in both academic and industrial research laboratories. In dense gas-particle flows both (effective) fluid-particle and (dissipative) particle-particle interactions need to be accounted for because these phenomena to a large extent govern the prevailing flow phenomena, i.e. the formation and evolution of heterogeneous structures. These structures have significant impact on the quality of the gas-solid contact and as a direct consequence thereof strongly affect the performance of the process. Due to the inherent complexity of dense gas-particles flows, we have adopted a multi-scale Modeling approach in which both fluid-particle and particle-particle interactions can be properly accounted for. The idea is essentially that fundamental models, taking into account the relevant details of fluid-particle (lattice Boltzmann model) and particle-particle (discrete particle model) interactions, are used to develop closure laws to feed continuum models which can be used to compute the flow structures on a much larger (industrial) scale. Our multi-scale approach (see Fig.1) involves the lattice Boltzmann model, the discrete particle model, the continuum model based on the kinetic theory of granular flow, and the discrete bubble model. In this paper we give an overview of the multi-scale Modeling Strategy, accompanied by illustrative computational results for bubble formation. In addition, areas which need substantial further attention will be highlighted.
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computational fluid dynamics for dense gas solid fluidized beds a multi scale Modeling Strategy
Chemical Engineering Science, 2004Co-Authors: Van Der Ma Martin Hoef, M Van Sint Annaland, J A M KuipersAbstract:Abstract Dense gas–particle flows are encountered in a variety of industrially important processes for large scale production of fuels, fertilizers and base chemicals. The scale-up of these processes is often problematic, which can be related to the intrinsic complexities of these flows which are unfortunately not yet fully understood despite significant efforts made in both academic and industrial research laboratories. In dense gas–particle flows both (effective) fluid–particle and (dissipative) particle–particle interactions need to be accounted for because these phenomena, to a large extent, govern the prevailing flow phenomena, i.e. the formation and evolution of heterogeneous structures. These structures have significant impact on the quality of the gas–solid contact and as a direct consequence thereof strongly affect the performance of the process. Due to the inherent complexity of dense gas-particles flows, we have adopted a multi-scale Modeling approach in which both fluid–particle and particle–particle interactions can be properly accounted for. The idea is essentially that fundamental models, taking into account the relevant details of fluid–particle (lattice Boltzmann model (LBM)) and particle–particle (discrete particle model (DPM)) interactions, are used to develop closure laws to feed continuum models which can be used to compute the flow structures on a much larger (industrial) scale. Our multi-scale approach (see Fig. 1) involves the LBM, the DPM, the continuum model based on the kinetic theory of granular flow, and the discrete bubble model. In this paper we give an overview of the multi-scale Modeling Strategy, accompanied by illustrative computational results for bubble formation. In addition, areas which need substantial further attention will be highlighted.
Jingdong J Han - One of the best experts on this subject based on the ideXlab platform.
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or ‘omic’) data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer‐ associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancerassociated genes. Combinations of mutated and/or aberrantly expressed tumor suppressor genes and oncogenes, or ‘cancer genes’, are thought to be responsible for most steps of cancer progression. Although fundamental principles have emerged from the study of known cancer genes and their products, many questions remain unanswered. Notably,
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancer-associated genes.
Jin Sook Ahn - One of the best experts on this subject based on the ideXlab platform.
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or ‘omic’) data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer‐ associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancerassociated genes. Combinations of mutated and/or aberrantly expressed tumor suppressor genes and oncogenes, or ‘cancer genes’, are thought to be responsible for most steps of cancer progression. Although fundamental principles have emerged from the study of known cancer genes and their products, many questions remain unanswered. Notably,
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network Modeling links breast cancer susceptibility and centrosome dysfunction
Nature Genetics, 2007Co-Authors: Miguel Angel Pujana, Jingdong J Han, Jin Sook Ahn, Tomas Kirchhoff, Kristen N Stevens, Lea M Starita, Gad Rennert, Muneesh Tewari, Victor Moreno, Bert GoldAbstract:Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network Modeling Strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network Modeling Strategy should be useful for the discovery of additional cancer-associated genes.