Inferring functional pathways from multi-perturbation data.

Nir Yosef; Alon Kaufman; Eytan Ruppin

doi:10.1093/bioinformatics/btl204

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Inferring functional pathways from multi-perturbation data.

Nir Yosef·Alon Kaufman·Eytan Ruppin

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Abstract

BACKGROUND: Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an iterative, extended version of FIN, the Functional Influence Network Extractor (FINE), which is specifically geared towards the accurate analysis of sparse cellular systems. We employ it to study a conceptually fundamental question of practical importance--how well should we know the system studied (such that we can predict its performance) so that we can understand its workings (i.e., chart its underlying functional network)? RESULTS AND CONCLUSIONS: The performance of FINE is studied in both simulated and biological sparse systems. It successfully obtains an accurate and compact description of the underlying functional network even with limited data, and outperforms FIN. We show that prior estimates of a system's functional complexity are instrumental in determining how much predictive knowledge is required to accurately chart its underlying functional network. AVAILABILITY: The FINE software is available for download at http://www.cns.tau.ac.il/resc.html.

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Algorithms Computer Simulation Databases, Factual Gene Expression Profiling Gene Expression Regulation Models, Biological Proteome Signal Transduction Journal Article Research Support, Non-U.S. Gov't

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Nir Yosef, Alon Kaufman, Eytan Ruppin. 2006-07-15. Inferring functional pathways from multi-perturbation data.. https://doi.org/10.1093/bioinformatics%2Fbtl204

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