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 -