Selection policy-induced reduction mappings for Boolean networks

Authors:
Ivan Ivanov;Plamen Simeonov;Noushin Ghaffari;Xiaoning Qian;Edward R. Dougherty
Affiliations:
Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX;Department of Mathematics, University of Houston Downtown, Houston, TX;Department of Electrical and Computer Engineering and Department of Statistics, Texas A&M University, College Station, TX;Department of Computer Science and Engineering, University of South Florida, Tampa, FL;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX and Translational Genomics Research Institute, Phoenix, AZ
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 9
Cited 0

Coefficient of determination in nonlinear signal processing

Signal Processing - Special section on signal processing technologies for short burst wireless communications
Mappings between probabilistic boolean networks

Signal Processing - Special issue: Genomic signal processing
Growing genetic regulatory networks from seed genes

Bioinformatics
Steady-state probabilities for attractors in probabilistic boolean networks

Signal Processing
Generating Boolean networks with a prescribed attractor structure

Bioinformatics
Reduction mappings between probabilistic Boolean networks

EURASIP Journal on Applied Signal Processing
Dynamic Programming and Optimal Control, Vol. II

Dynamic Programming and Optimal Control, Vol. II
A CoD-based reduction algorithm for designing stationary control policies on Boolean networks

Bioinformatics
Robust Intervention in Probabilistic Boolean Networks

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

Developing computational models paves the way to understanding, predicting, and influencing the long-term behavior of genomic regulatory systems. However, several major challenges have to be addressed before such models are successfuUy appJied in practice. Their inherent high complexity requires strategies for complexity reduction. Reducing the complexity of the model by removing genes and interpreting them as latent variables leads to the problem of selecting which states and their corresponding transitions best account for the presence of such latent variables. We use the Boolean network (BN) model to develop the general framework for selection and reduction of the model's complexity via designating some of the model's variables as latent ones. We also study the effects of the selection policies on the steady-state distribution and the controllability of the model.