Biological Network Inference
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, IL2 stimulated T cell
regulatory response, and LPS stimulated macrophages. More information
can be found in the following papers:
- Martin
S., Zhang Z., Martino A., Faulon J.L.
Boolean
Dynamics of Genetic Regulatory Networks Inferred from Microarray Time
Series
Data, Bioinformatics, 23, 866-74, 2007 [PMID:
17267426] (link
to journal)
- 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).
Signal transduction
The NF-κB signaling
network plays an important role in many
different compartments of the immune system during immune activation.
Using a
computational model of the NF-κB signaling
network involving two negative regulators,
IκBα and A20, we performed sensitivity analyses with three different
sampling
methods and present a ranking of the kinetic rate variables by the
strength of
their influence on the NF-κB signaling
response. We also present a classification
of temporal response profiles of nuclear NF-κB concentration into six
clusters,
which can be regrouped to three biologically relevant clusters. More
information can be found in the following paper:
- Joo J.,
Plimpton S., Martin S., Swiler L., Slepoy A., Faulon J.L., Sensitivity analysis
of computational model of the
NF-κB-IκB-A20 signal transduction network, Annals
of NY Academy of Sciences, in
press 2007 [PMID: 17934057] (link to journal)
Send reprint request or comments to:
jfaulon@sandia.gov