(591e) Can We Predict Combined Stress Response from Individual Responses? | AIChE

(591e) Can We Predict Combined Stress Response from Individual Responses?

Authors 

Dutta, B. - Presenter, University of Maryland
Klapa, M. I. - Presenter, University of Maryland
Kanani, H. H. - Presenter, University of Maryland
Quackenbush, J. - Presenter, Dana-Farber Cancer Institute


Systems biology, which uses systems engineering approach to solve biological problem, requires the system to be perturbed in multiple ways and study it's response in a dynamic fashion to elucidate the relationship between different system variables. Multiple perturbations applied in a systematic way are essential as it allow us to study the system's response from different perspectives, unavailable from individual stresses. However, if the stresses applied individually are applied in combination do we still get the same amount of information?

This question led us to a unique experimental design where our model system, Arabidopsis thaliana liquid cultures, was systematically perturbed in several ways. This is a first effort, to the best of our knowledge, where multiple and combination of perturbations were applied to a eukaryotic system and the dynamic transcriptional response was studied using full genome cDNA microarrays. Specifically, A. thaliana liquid cultures grown for 12 days under constant light and temperature were subjected to step input in (1) CO2 level in their growth environment (2) Osmotic stress through addition of NaCl in growth media (3) CO2 and osmotic stress in combination. Liquid cultures were harvested over a period of 30 hours after initiation of the treatment to monitor the dynamic short-term response of the stresses. The obtained measurements was used to (a) study the effect of individual as well as combined perturbations (b) compare the effect of combined perturbation with that of the individual perturbations using statistical testing methods (c) develop methodologies to upgrade the information content of the gene expression data by using additional constraints based on previous biological knowledge and (d) get valuable insight about gene regulation. The results were further validated in the context of the known A. thaliana physiology.