Inferring functional pathways from multi-perturbation data.
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)?


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.


BACKGROUND
The FINE software is available for download at http://www.cns.tau.ac.il/resc.html.
Yosef
Nir
N
School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il
Kaufman
Alon
A
Ruppin
Eytan
E
D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome