J. Comput. Biol. - Reconstructing Boolean models of signaling.

Tópicos

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{ can(981) present(881) function(850) }
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{ structur(1116) can(940) graph(676) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Since the first emergence of protein-protein interaction networks more than a decade ago, they have been viewed as static scaffolds of the signaling-regulatory events taking place in cells, and their analysis has been mainly confined to topological aspects. Recently, functional models of these networks have been suggested, ranging from Boolean to constraint-based methods. However, learning such models from large-scale data remains a formidable task, and most modeling approaches rely on extensive human curation. Here we provide a generic approach to learning Boolean models automatically from data. We apply our approach to growth and inflammatory signaling systems in humans and show how the learning phase can improve the fit of the model to experimental data, remove spurious interactions, and lead to better understanding of the system at hand.

Resumo Limpo

sinc first emerg proteinprotein interact network decad ago view static scaffold signalingregulatori event take place cell analysi main confin topolog aspect recent function model network suggest rang boolean constraintbas method howev learn model largescal data remain formid task model approach reli extens human curat provid generic approach learn boolean model automat data appli approach growth inflammatori signal system human show learn phase can improv fit model experiment data remov spurious interact lead better understand system hand

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