The Experts below are selected from a list of 840 Experts worldwide ranked by ideXlab platform

Christine D Smart - One of the best experts on this subject based on the ideXlab platform.

  • temporal Genetic Dynamics of an experimental biparental field population of phytophthora capsici
    Frontiers in Genetics, 2017
    Co-Authors: Maryn O Carlson, Elodie Gazave, Michael A Gore, Christine D Smart
    Abstract:

    Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a grower's field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; multi-generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

  • temporal Genetic Dynamics of an experimental biparental field population of phytophthora capsici
    bioRxiv, 2016
    Co-Authors: Maryn O Carlson, Elodie Gazave, Michael A Gore, Christine D Smart
    Abstract:

    Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a grower9s field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; mixed generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

Chenhsiang Yeang - One of the best experts on this subject based on the ideXlab platform.

  • impact of Genetic Dynamics and single cell heterogeneity on development of nonstandard personalized medicine strategies for cancer
    Proceedings of the National Academy of Sciences of the United States of America, 2012
    Co-Authors: Robert A Beckman, Gunter S Schemmann, Chenhsiang Yeang
    Abstract:

    Cancers are heterogeneous and Genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual’s cancer or the dynamic nature of cancer due to Genetic and epiGenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating Genetic evolutionary Dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable “patients” demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, Dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

  • Abstract LB-448: Next generation personalized medicine strategies incorporating Genetic Dynamics and single cell heterogeneity may lead to improved outcomes
    Cancer Research, 2012
    Co-Authors: Robert A Beckman, Gunter S Schemmann, Chenhsiang Yeang
    Abstract:

    Introduction: Cancers are heterogeneous and often Genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type occurring within different individuals. However, it does not yet address heterogeneity at the single cell level within individual cancers or the dynamic nature of cancer, due to heritable Genetic and epiGenetic change, as well as transient functional changes. We established methods for evaluating personalized medicine strategies, and compared the current personalized medicine strategy to alternatives. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression. This strategy focuses on the average, static, and current properties of the sample. Next-generation strategies also consider minor sub-clones, Dynamics, and predicted future tumor states. Methods: We developed a mathematical model of targeted cancer therapy incorporating Genetic evolutionary Dynamics and single cell heterogeneity, and examined simulated clinical outcomes (cell numbers of clones and sub-clones, projected survival). We compared the current personalized medicine strategy to 5 alternative personalized strategies. The latter strategies explicitly considered sub-clones, evolutionary Dynamics, and likely future sub-clones in addition to the current predominant clone. Particular emphasis was given to the prevention of incurable, multiply resistant sub-clones. Results: We carried out a computerized virtual clinical trial of over 3 million evaluable cancer “patients,” comparing current personalized medicine and 5 alternative strategies. While the current personalized medicine strategy was equally effective to the alternatives in 2/3 of the cases, in 1/3 of the cases alternative strategies led to improved outcomes. All alternatives tested resulted in an approximate doubling in mean and median survival compared to current personalized medicine and an increase in the apparent cure rate from 0.7% for current personalized medicine to 17-20% for alternatives. In no case was the current personalized medicine strategy superior. Conclusions: These findings may lead to improved patient outcomes. Further, they suggest global enhancements to translational oncology research paradigms: for example, molecular characterization of incurable, multiply resistant “end states” from autopsy may be equally or more important than characterizing initial diagnostic states. We have developed methods to evaluate alternative personalized medicine strategies. Next generation strategies may consider sub-clones, evolutionary Dynamics, and predicted future states. Application of knowledge from growing molecular and empirical oncology databases may allow more informative therapeutic simulations than previously possible. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-448. doi:1538-7445.AM2012-LB-448

Maryn O Carlson - One of the best experts on this subject based on the ideXlab platform.

  • temporal Genetic Dynamics of an experimental biparental field population of phytophthora capsici
    Frontiers in Genetics, 2017
    Co-Authors: Maryn O Carlson, Elodie Gazave, Michael A Gore, Christine D Smart
    Abstract:

    Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a grower's field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; multi-generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

  • temporal Genetic Dynamics of an experimental biparental field population of phytophthora capsici
    bioRxiv, 2016
    Co-Authors: Maryn O Carlson, Elodie Gazave, Michael A Gore, Christine D Smart
    Abstract:

    Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a grower9s field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; mixed generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

Christopher R. Stephens - One of the best experts on this subject based on the ideXlab platform.

  • SYSTEMATIC APPROXIMATIONS FOR Genetic Dynamics
    Advances in Complex Systems, 2009
    Co-Authors: Christopher R. Stephens, Adolfo Zamora
    Abstract:

    Although much progress has been made in recent years in describing the Dynamics of Genetic systems, both in population Genetics and evolutionary computation, there is still a conspicuous lack of tools with which to derive systematic, approximate solutions to their Dynamics. In this article, we propose and study perturbation theory and the renormalization group as potential tools to fill this gap. We concentrate mainly on selection–mutation systems, showing different implementations of the perturbative framework, developing, for example, perturbative expansions for the eigenvalues and eigenvectors of the transition matrix. The main focus, however, is on diagrammatic methods, taken from physics, where we show how approximations can be built up using a pictorial representation generated by a simple set of rules, and how the renormalization group can be used to systematically improve the perturbation theory.

  • Covariant Genetic Dynamics
    Evolutionary Computation, 2007
    Co-Authors: Chryssomalis Chryssomalakos, Christopher R. Stephens
    Abstract:

    We present a covariant form for the Dynamics of a canonical GA of arbitrary cardinality, showing how each Genetic operator can be uniquely represented by a mathematical object — a tensor — that transforms simply under a general linear coordinate transformation. For mutation and recombination these tensors can be written as tensor products of the analogous tensors for one-bit strings thus giving a greatly simplified formulation of the Dynamics. We analyze the three most well known coordinate systems — string, Walsh and Building Block — discussing their relative advantages and disadvantages with respect to the different operators, showing how one may transform from one to the other, and that the associated coordinate transformation matrices can be written as a tensor product of the corresponding one-bit matrices. We also show that in the Building Block basis the dynamical equations for all Building Blocks can be generated from the equation for the most fine-grained block (string) by a certain projection (“zapping”).

  • FOGA - Perturbation theory and the renormalization group in Genetic Dynamics
    Foundations of Genetic Algorithms, 2005
    Co-Authors: Christopher R. Stephens, Adolfo Zamora, Alden H. Wright
    Abstract:

    Although much progress has been made in recent years in the theory of GAs and GP, there is still a conspicuous lack of tools with which to derive systematic, approximate solutions to their Dynamics. In this article we propose and study perturbation theory as a potential tool to fill this gap. We concentrate mainly on selection-mutation systems, showing different implementations of the perturbative framework, developing, for example, perturbative expansions for the eigenvalues and eigenvectors of the transition matrix. The main focus however, is on diagrammatic methods, taken from physics, where we show how approximations can be built up using a pictorial representation generated by a simple set of rules, and how the renormalization group can be used to systematically improve the perturbation theory.

  • GECCO (1) - What Basis for Genetic Dynamics
    Genetic and Evolutionary Computation – GECCO 2004, 2004
    Co-Authors: Chryssomalis Chryssomalakos, Christopher R. Stephens
    Abstract:

    We present a covariant form for Genetic Dynamics and show how different formulations are simply related by linear coordinate transformations. In particular, in the context of the simple Genetic algorithm, we show how the Vose model, in either the string or Walsh bases, is related to recent coarse-grained formulations that are naturally interpreted in terms of the Building Block basis (BBB). We also show that the latter is dual to the Taylor basis. The tensor product structure of the dynamical equations is analyzed, permitting the factorization of the N-bit operators in 1-bit factors.

  • Some exact results from a coarse grained formulation of Genetic Dynamics
    2001
    Co-Authors: Christopher R. Stephens
    Abstract:

    We extend a recently developed exact schema based, or coarse grained, formulation of Genetic Dynamics [8, 9, 10] and its associated exact Schema theorem to an arbitrary selection scheme and a general crossover operator. We show that the intuitive "building block" interpretation of the former is preserved leading to hierarchical formal solutions of the equations that upon iteration lead to new results for the limiting distribution of a population in the case of 1-point crossover and "weak" selection, where we define quantitatively "weak". We also derive an exact, analytic form for the population distribution as a function of time for a flat landscape and 1-point crossover.

Robert A Beckman - One of the best experts on this subject based on the ideXlab platform.

  • impact of Genetic Dynamics and single cell heterogeneity on development of nonstandard personalized medicine strategies for cancer
    Proceedings of the National Academy of Sciences of the United States of America, 2012
    Co-Authors: Robert A Beckman, Gunter S Schemmann, Chenhsiang Yeang
    Abstract:

    Cancers are heterogeneous and Genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual’s cancer or the dynamic nature of cancer due to Genetic and epiGenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating Genetic evolutionary Dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable “patients” demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, Dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

  • Abstract LB-448: Next generation personalized medicine strategies incorporating Genetic Dynamics and single cell heterogeneity may lead to improved outcomes
    Cancer Research, 2012
    Co-Authors: Robert A Beckman, Gunter S Schemmann, Chenhsiang Yeang
    Abstract:

    Introduction: Cancers are heterogeneous and often Genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type occurring within different individuals. However, it does not yet address heterogeneity at the single cell level within individual cancers or the dynamic nature of cancer, due to heritable Genetic and epiGenetic change, as well as transient functional changes. We established methods for evaluating personalized medicine strategies, and compared the current personalized medicine strategy to alternatives. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression. This strategy focuses on the average, static, and current properties of the sample. Next-generation strategies also consider minor sub-clones, Dynamics, and predicted future tumor states. Methods: We developed a mathematical model of targeted cancer therapy incorporating Genetic evolutionary Dynamics and single cell heterogeneity, and examined simulated clinical outcomes (cell numbers of clones and sub-clones, projected survival). We compared the current personalized medicine strategy to 5 alternative personalized strategies. The latter strategies explicitly considered sub-clones, evolutionary Dynamics, and likely future sub-clones in addition to the current predominant clone. Particular emphasis was given to the prevention of incurable, multiply resistant sub-clones. Results: We carried out a computerized virtual clinical trial of over 3 million evaluable cancer “patients,” comparing current personalized medicine and 5 alternative strategies. While the current personalized medicine strategy was equally effective to the alternatives in 2/3 of the cases, in 1/3 of the cases alternative strategies led to improved outcomes. All alternatives tested resulted in an approximate doubling in mean and median survival compared to current personalized medicine and an increase in the apparent cure rate from 0.7% for current personalized medicine to 17-20% for alternatives. In no case was the current personalized medicine strategy superior. Conclusions: These findings may lead to improved patient outcomes. Further, they suggest global enhancements to translational oncology research paradigms: for example, molecular characterization of incurable, multiply resistant “end states” from autopsy may be equally or more important than characterizing initial diagnostic states. We have developed methods to evaluate alternative personalized medicine strategies. Next generation strategies may consider sub-clones, evolutionary Dynamics, and predicted future states. Application of knowledge from growing molecular and empirical oncology databases may allow more informative therapeutic simulations than previously possible. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-448. doi:1538-7445.AM2012-LB-448