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.
[1]
D di Bernardo,et al.
Inference of gene networks from temporal gene expression profiles.
,
2007,
IET systems biology.
[2]
Dawei Hong,et al.
A theoretical approach to gene network identification
,
2012,
2012 IEEE Information Theory Workshop.
[3]
D. di Bernardo,et al.
How to infer gene networks from expression profiles
,
2007,
Molecular systems biology.
[4]
Richard Bonneau,et al.
DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models
,
2010,
PloS one.
[5]
D. Floreano,et al.
Revealing strengths and weaknesses of methods for gene network inference
,
2010,
Proceedings of the National Academy of Sciences.
[6]
Dario Floreano,et al.
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
,
2011,
Bioinform..