Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Contemporary Logic Design
Regulation of Cellular States in Mammalian Cells from a Genomewide View
Gene Regulations and Metabolism - Postgenomic Computational Approaches
Controllability and observability of Boolean control networks
Automatica (Journal of IFAC)
Bilinear Control Systems: Matrices in Action
Bilinear Control Systems: Matrices in Action
Input-state approach to boolean networks
IEEE Transactions on Neural Networks
Realization of Boolean control networks
Automatica (Journal of IFAC)
State-space analysis of Boolean networks
IEEE Transactions on Neural Networks
Identification of Boolean control networks
Automatica (Journal of IFAC)
Discrete Applied Mathematics
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Brief paper: Controllability of Boolean control networks via the Perron-Frobenius theory
Automatica (Journal of IFAC)
Paper: Observation of bilinear systems with application to biological control
Automatica (Journal of IFAC)
Survey A survey of computational complexity results in systems and control
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Symbolic dynamics of Boolean control networks
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
Boolean networks (BNs) are discrete-time dynamical systems with Boolean state-variables and outputs. BNs are recently attracting considerable interest as computational models for genetic and cellular networks. We consider the observability of BNs, that is, the possibility of uniquely determining the initial state given a time sequence of outputs. Our main result is that determining whether a BN is observable is NP-hard. This holds for both synchronous and asynchronous BNs. Thus, unless P=NP, there does not exist an algorithm with polynomial time complexity that solves the observability problem. We also give two simple algorithms, with exponential complexity, that solve this problem. Our results are based on combining the algebraic representation of BNs derived by D. Cheng with a graph-theoretic approach. Some of the theoretical results are applied to study the observability of a BN model of the mammalian cell cycle.