DocumentCode :
1633394
Title :
Identification of binary gene networks
Author :
Birget, J.-C. ; Lun, D.S. ; Wirth, Andreas ; Dawei Hong
Author_Institution :
Dept. of Comput. Sci., Rutgers The State Univ. of New Jersey, Camden, NJ, USA
fYear :
2012
Firstpage :
1467
Lastpage :
1474
Abstract :
We continue our theoretical examination of the problem of gene network identification, which we introduced in a previous paper. Here we consider a purely binary model of gene networks, without the assumption of sensitivity side information made in our previous paper. We present the following somewhat intuitive result: A general acyclic binary gene network can be identified by a brute force approach (in which every assignment for all subsets of k genes is made, where k is the maximum number of genes by which a gene is controlled, followed by the measurement of steady-state expression response). Our proof shows that the result is not straightforward because of certain side-effects. We also describe a natural characterization of the set of non-acyclic networks that can be identified. Moreover, we show that without new assumptions, this brute force approach has optimal complexity in the worst case.
Keywords :
computational complexity; genetics; network theory (graphs); set theory; brute force approach; gene subsets; general acyclic binary gene network model identification; maximum gene number control; nonacyclic network set; optimal complexity; steady-state expression response measurement; Complexity theory; Force; Input variables; Mathematical model; Sensitivity; Steady-state; Strain;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on
Conference_Location :
Monticello, IL
Print_ISBN :
978-1-4673-4537-8
Type :
conf
DOI :
10.1109/Allerton.2012.6483392
Filename :
6483392
Link To Document :
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