TY - GEN
T1 - Synthesizing Boolean networks with a given attractor structure
AU - Pal, Ranadip
AU - Ivanov, Ivan
AU - Datta, Aniruddha
AU - Bittner, Michael L.
AU - Dougherty, Edward R.
PY - 2006
Y1 - 2006
N2 - The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). The long-run behavior of a BN is characterized by its attractors. We derive two algorithms for the attractor inverse problem where the attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion has been used to test a designed Probabilistic Boolean Network (PBN) constructed from melanoma gene-expression data.
AB - The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). The long-run behavior of a BN is characterized by its attractors. We derive two algorithms for the attractor inverse problem where the attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion has been used to test a designed Probabilistic Boolean Network (PBN) constructed from melanoma gene-expression data.
UR - http://www.scopus.com/inward/record.url?scp=48649088966&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2006.353162
DO - 10.1109/GENSIPS.2006.353162
M3 - Conference contribution
AN - SCOPUS:48649088966
SN - 1424403855
SN - 9781424403851
T3 - 2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
SP - 73
EP - 74
BT - 2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006
T2 - 2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
Y2 - 28 May 2006 through 30 May 2006
ER -