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Probability-based model of protein-protein interactions on biological timescales.
Tournier, Alexander L; Fitzjohn, Paul W; Bates, Paul A.
Afiliação
  • Tournier AL; Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3PX, UK. Alexander.tournier@cancer.org.uk
Algorithms Mol Biol ; 1: 25, 2006 Dec 11.
Article em En | MEDLINE | ID: mdl-17156482
ABSTRACT

BACKGROUND:

Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other.

RESULTS:

In this study, a new model of bimolecular interactions is presented that uses a simple, probability-based description of the reaction process. This description is well-suited to simulations on timescales relevant to biological systems (from seconds to hours), and provides an alternative to the previous description given by Smoluchowski. In the present approach (TFB) the diffusion process is explicitly taken into account in generating the probability that two freely diffusing chemical entities will interact within a given time interval. It is compared to the Smoluchowski method, as modified by Andrews and Bray (AB).

CONCLUSION:

When implemented, the AB & TFB methods give equivalent results in a variety of situations relevant to biology. Overall, the Smoluchowski method as modified by Andrews and Bray emerges as the most simple, robust and efficient method for simulating biological diffusion-reaction processes currently available.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Algorithms Mol Biol Ano de publicação: 2006 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Algorithms Mol Biol Ano de publicação: 2006 Tipo de documento: Article País de afiliação: Reino Unido