The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search

Abstract
Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 18 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy.
Keywords No keywords specified (fix it)
Categories (categorize this paper)
Options
 Save to my reading list
Follow the author(s)
My bibliography
Export citation
Find it on Scholar
Edit this record
Mark as duplicate
Revision history Request removal from index
 
Download options
PhilPapers Archive


Upload a copy of this paper     Check publisher's policy on self-archival     Papers currently archived: 11,817
External links
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library
References found in this work BETA

No references found.

Citations of this work BETA

No citations found.

Similar books and articles
Rosario M. Piro (2011). Are All Genes Regulatory Genes? Biology and Philosophy 26 (4):595-602.
Ehud Lamm (2009). Conceptual and Methodological Biases in Network Models. Annals of the New York Academy of Sciences 1178:291-304.
Sharon S. Krag (2010). Issues in Data Management. Science and Engineering Ethics 16 (4):743-748.
Margi Joshi & Sharon Krag (2010). Issues in Data Management. Science and Engineering Ethics 16 (4):743-748.
Analytics

Monthly downloads

Added to index

2010-12-22

Total downloads

5 ( #234,761 of 1,099,912 )

Recent downloads (6 months)

1 ( #304,017 of 1,099,912 )

How can I increase my downloads?

My notes
Sign in to use this feature


Discussion
Start a new thread
Order:
There  are no threads in this forum
Nothing in this forum yet.