Biological Network Inference
Protein-Protein interactions (PPIs)
We have developed a new technique to predict PPIs using
the signature descriptor described in the QSAR pages.
The signature of a protein sequence (or DNA sequence) is a vector of occurence
numbers of the sequence k-mers (or k-words). To predict protein binding
pairs a new type of QSAR was developed where the signagture is a product
of the signagtures of both the bait (target) and the prey (ligand).
The QSAR, which is trained using SVM compares well in term of accuracy/precision/sensitivity
with other existing techniques. Our method is not specific to a particular
type of experimental dataset, does not require knowledge of protein domains
or physico-chemical parameters, it just uses as input a list of sequences
and binding pairs. More information can be found in the following
papers:
- Brown W. M., Martin S., Chabarek J.P., Strauss C,
Faulon J.L. Prediction of β-Strand Packing Interactions
using the Signature Product, Journal of Molecular Modeling,
in press. [PMID: 16365772]
(link to journal)
- Martin S., Roe D., Faulon J.L. Predicting Protein-Protein
Interactions using Signature Products, Bioinformatics, 21,
218-26, 2005. [PMID: 15319262]. (.pdf manuscript).
Protein-Protein
Interaction Predictor Downloads
Regulatory interactions
Regulatory interactions are probed using a combinatorial
technique that enumerates all the possible regulators of a given gene/protein
from hight throughput expression profiles. The technique takes as input
time series expression profiles under experimental perturbations (knock-out
for instance), and outputs all the possible networks, where relationships
between genes/proteins are represented by activation/inhibition rules. The
activation/inhibition rules are implemented by Boolean functions, which
allows one to study the steady state dynamics of the infered networks. The
computational efficiency of the technique relies of the assumption that the
number of regulator per gene/protein is bounded by some constant. Our
combinatorial enumeration has been applied to infer gene regulatory networks
for Yeast cell cycle, and IL2 stimulated T cell regulatory response. More
information can be found in the following papers:
- Faulon J.L.,, Zhang Z., Martino A., Timlin J.A., Haaland
D.M.,, Martin S., Davidson G., May E., Slepoy A. Reverse Engineering
Biological Networks: T-cell response to IL-2 stimulation. SANDIA Report
2005- 5238379, Sandia National Laboratories, Albuquerque, NM.
(.pdf manuscript).
- Martin S, Davidson G, May E, Werner-Washburne M.,
Faulon J.L. Inferring Genetic Networks from Microarray Data.
Proceedings IEEE CSB2004, 3, 566-569, 2004. (.pdf manuscript).
- Faulon J.L., Martin S., Carr RD. Dynamical
Robustness in Gene Regulatory Networks. Proceedings IEEE CSB2004,
3, 626-627, 2004. (.pdf manuscript).
Send reprint request or comments
to:
jfaulon@sandia.gov