S. cerevisiae has emerged as one of the most
widely used model organisms for understanding complex
transcriptional control systems. Pioneering high-throughput
techniques to elucidate regulatory and signaling
systems have been applied first and foremost in
S. cerevisiae. Furthermore, the computational infrastructure
for systems biology has been developed, tested,
and parameterized primarily using experiments on
S. cerevisiae. Consequently, availability of these
high-throughput datasets, coupled with major developments
in computational biology has catalyzed significant
insight into the mechanisms of pathway functionality.
While these efforts have increased our understanding
of pathways in S. cerevisiae, little is known about
how these pathways evolve in different organisms,
adapt to novel environments and change with speciation.
Yeast species are separated by enormous phenotypic
diversity accumulated over a billion years
of evolution. Coupled with detailed, data-driven
models of
pathways, a range of evolutionary distances makes yeasts a perfect model
to study evolution of complex systems and
pathways. The osmotic stress response
is a multifaceted pathway that activates a kinase cascade regulating multiple
transcription factors that act in different combinations at different promoters
to produce a specific transcriptional response. I am interested in combining
state-of-the-art computational tools with novel experimental data to build
a framework for understanding both the evolution of the signaling cascade
and the transcriptional response to osmotic
stress in far diverged yeast species.
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