Differential network expression during drug and stress response.
BACKGROUND
The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organism's ability to react to its environment.


RESULTS
We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future.


BACKGROUND
chris@lanl.gov


BACKGROUND
Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW.
Cabusora
Lawrence
L
Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA.
Sutton
Electra
E
Fulmer
Andy
A
Forst
Christian V
CV
D001426:Bacterial Proteins / Q000378:metabolism*; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000187:drug effects / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods; D015964:Gene Expression Regulation, Bacterial / Q000187:drug effects / Q000502:physiology*; D008954:Models, Biological; D009169:Mycobacterium tuberculosis / Q000187:drug effects / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D018384:Oxidative Stress / Q000187:drug effects / Q000502:physiology*; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D050794:STAT1 Transcription Factor; D015534:Trans-Activators / Q000187:drug effects / Q000502:physiology*
D001426:Bacterial Proteins; D004268:DNA-Binding Proteins; D004364:Pharmaceutical Preparations; D050794:STAT1 Transcription Factor; D015534:Trans-Activators