Metabolic Flux Estimation Using Particle Swarm Optimization with Penalty Function.

Metabolic flux estimation through C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimi...

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Publicado en:Biology Forum / Rivista di Biologia Vol. 102; no. 2; pp. 237 - 253
Autores principales: Hai-Xia Long, Wen-Bo Xu, Jun Sun
Formato: Artículo
Publicado: Fabrizio Serra Editore 2009
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Metabolic Flux Estimation Using Particle Swarm Optimization with Penalty Function.
      aug:
        au:
          Hai-Xia Long
          Wen-Bo Xu
          Jun Sun
        affil: School of Information Technology, Jiangnan University, No. 1800, Lihudadao Road, Wuxi, Jiangsu 214122, China
      su:
        Particle swarm optimization
        Corynebacterium glutamicum
        Glucose
        Metabolites
        Stoichiometry
        Statistics
      sug:
        subj:
          Particle swarm optimization
          Corynebacterium glutamicum
          Glucose
          Metabolites
          Stoichiometry
          Statistics
      keyword:
        Metabolic flux estimation
        particle swarm optimization
        penalty function
      ab: Metabolic flux estimation through C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using PSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum. From simulation results, it is shown that the proposed algorithm has superior performance and fast convergence ability when compared to other existing algorithms.
      pubtype: Academic Journal
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    language: English
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