Abstract
This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Publisher | IEEE |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop - Duration: 1 Jan 2009 → … |
Conference
| Conference | Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop |
|---|---|
| Period | 1/01/09 → … |
Keywords
- logic
- boolean network with perturbation
- bnp
Fingerprint Dive into the research topics of 'Inference of transition probabilities between the attractors in Boolean networks with perturbation'. Together they form a unique fingerprint.
Cite this
Watterson, S. (2009). Inference of transition probabilities between the attractors in Boolean networks with perturbation. In Unknown Host Publication IEEE. https://doi.org/10.1109/GENSIPS.2009.5174376