Identification of Replicator Mutator models

Laura Giarre, Paola Falugi

    Risultato della ricerca: Other

    Abstract

    The complexity of biology literally calls for quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, the Replicator-Mutator models for Evolutionary Dynamics are validated/invalidated in a worst-case deterministic setting. These models analyze the DNA and RNA evolution or describe the population dynamics of viruses and bacteria. We identify the Fitness and the Replication Probability parameters of a genetic sequences, subject to a set of stringent constraints to have physical meaning and to guarantee positiveness. The conditional central estimate is determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments while tests on real data are under concern.
    Lingua originaleEnglish
    Pagine1-6
    Numero di pagine6
    Stato di pubblicazionePublished - 2006

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    Identification (control systems)
    Population dynamics
    RNA
    Viruses
    Bacteria
    DNA
    Experiments

    All Science Journal Classification (ASJC) codes

    • Computer Science Applications
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

    Cita questo

    Identification of Replicator Mutator models. / Giarre, Laura; Falugi, Paola.

    2006. 1-6.

    Risultato della ricerca: Other

    Giarre, L & Falugi, P 2006, 'Identification of Replicator Mutator models', pagg. 1-6.
    Giarre L, Falugi P. Identification of Replicator Mutator models. 2006.
    Giarre, Laura ; Falugi, Paola. / Identification of Replicator Mutator models. 6 pag.
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    AU - Falugi, Paola

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    AB - The complexity of biology literally calls for quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, the Replicator-Mutator models for Evolutionary Dynamics are validated/invalidated in a worst-case deterministic setting. These models analyze the DNA and RNA evolution or describe the population dynamics of viruses and bacteria. We identify the Fitness and the Replication Probability parameters of a genetic sequences, subject to a set of stringent constraints to have physical meaning and to guarantee positiveness. The conditional central estimate is determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments while tests on real data are under concern.

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    UR - http://hdl.handle.net/10447/32933

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