Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Mappings between probabilistic boolean networks
Signal Processing - Special issue: Genomic signal processing
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Reduction mappings between probabilistic Boolean networks
EURASIP Journal on Applied Signal Processing
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Genomic Signal Processing (Princeton Series in Applied Mathematics)
Intervention in context-sensitive probabilistic Boolean networks revisited
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Adaptive intervention in probabilistic boolean networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Optimal infinite-horizon control for probabilistic Boolean networks
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing - Part II
Robust Intervention in Probabilistic Boolean Networks
IEEE Transactions on Signal Processing
Dynamics Preserving Size Reduction Mappings for Probabilistic Boolean Networks
IEEE Transactions on Signal Processing
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 35.68 |
Context-sensitive probabilistic Boolean networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks and have served as the main model for the application of intervention methods, including optimal control strategies, to favorably effect system dynamics. Since it is believed that the steady-state behavior of a context-sensitive PBN is indicative of the phenotype, it is important to study the alternation in the steady-state probability distribution due to any variations in the formulations of the context-sensitive PBNs. Furthermore, the huge computational complexity of the context-sensitive PBN model necessitates generation of size-reduction techniques and approximate methods for calculation of the steady-state probability distribution of context-sensitive PBNs. The goal of this paper is threefold: i) to study the effects of the various definitions of context-sensitive PBNs on the steady-state probability distributions and the downstream control policy design; ii) to propose a reduction technique that maintains the steady-state probability distribution; and iii) to provide an approximation method for calculating the steady-state probability distribution of a context-sensitive PBN.