Transcriptional regulatory network response to extreme conditions.
Extremophiles thrive in deep-sea hydrothermal vents under high pressure and temperature, saturated salt lakes, and polar icecaps. Many of these organisms are members of the third domain of life, the archaea. Although archaea contribute substantially to global carbon and energy cycles, they remain understudied because they are difficult to culture and manipulate genetically. How do these microorganisms cope with an extreme and changing environment? How do they alter their genetic programs and metabolic pathways to adapt and survive changes in their unique habitats? To answer these questions, the long-term goal of ongoing research is to understand how organisms maintain homeostasis in the face of fluctuating environmental conditions. Central to this process are gene regulatory networks (GRNs) composed of groups of regulatory proteins that switch genes on and off in response to environmental stimuli. Upon sensing a change in the environment, the GRN increases the production of genes encoding proteins that repair damage, restore the cell to a healthy state and prepare for future stress. The organism studied in the current research, Halobacterium salinarum, thrives in high salt environments. Because it is easy to culture and manipulate genetically, Halobacterium is a good model system for studying archaea. In addition, this organism is a stress response specialist, surviving in the Great Salt Lake during strong daily fluctuations in light, heat, oxygen, and nutrients at ~4.0 M salt. In response, the organism shifts its metabolism between four light- and oxygen-dependent energy-generating modes. The aim of the current work is to determine how the organism uses its GRN, or gene circuitry, during dynamic alterations in light, oxygen, and other nutrients (e.g. carbon sources and trace metals) to ensure survival in its extremely salty habitat. We use systems biology approaches, which combine cutting-edge high throughput experimental techniques with computational or statistical modeling.
Data integration and network modeling.
This project aims to build a predictive computational model which integrates and overlays transcriptional and posttranscriptional events with metabolic output.