AMMAR, Amine; CUETO, Elías; CHINESTA, Francisco

doi:10.1002/cnm.2476

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AMMAR, Amine
Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]

CUETO, Elías
Aragón Institute of Engineering Research [Zaragoza] [I3A]

CHINESTA, Francisco
Institut de Recherche en Génie Civil et Mécanique [GeM]

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International Journal for Numerical Methods in Biomedical Engineering. 2012-09, vol. 28, n° 9, p. 960-973

John Wiley and Sons

Résumé en anglais

The numerical solution of the chemical master equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task owing to its complexity, exponentially growing with the number of species involved. Although most of the existing techniques rely on the use of Monte Carlo-like techniques, we present here a new technique based on the approximation of the unknown variable (the probability of having a particular chemical state) in terms of a finite sum of separable functions. In this framework, the complexity of the CME grows only linearly with the number of state space dimensions. This technique generalizes the so-called Hartree approximation, by using terms as needed in the finite sums decomposition for ensuring convergence. But noteworthy, the ease of the approximation allows for an easy treatment of unknown parameters (as is frequently the case when modeling gene regulatory networks, for instance). These unknown parameters can be considered as new space dimensions. In this way, the proposed method provides solutions for any value of the unknown parameters (within some interval of arbitrary size) in one execution of the program.< Réduire

Mots clés en anglais

Gene regulatory networks

Chemical master equation

Curse of dimensionality

Proper generalized decomposition

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Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions

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