Publications in Scientific Journals:

A. Oliveira, S. Dimopoulos, A. Busetto, S. Christen, R. Dechant, L. Falter, M. Chehreghani, S. Jozefczuk, C. Ludwig, F. Rudroff, J. Schulz, A. González, A. Soulard, D. Stracka, R. Aebersold, J. Buhmann, N. Hall, M. Peter, U. Sauer, J. Stelling:
"Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome";
Molecular Systems Biology, 4 (2015), 11; 802.

English abstract:
Cells react to nutritional cues in changing environments via the
integrated action of signaling, transcriptional, and metabolic
networks. Mechanistic insight into signaling processes is often
complicated because ubiquitous feedback loops obscure causal
relationships. Consequently, the endogenous inputs of many nutrient
signaling pathways remain unknown. Recent advances for
system-wide experimental data generation have facilitated the
quantification of signaling systems, but the integration of multilevel
dynamic data remains challenging. Here, we co-designed
dynamic experiments and a probabilistic, model-based method to
infer causal relationships between metabolism, signaling, and gene
regulation. We analyzed the dynamic regulation of nitrogen
metabolism by the target of rapamycin complex 1 (TORC1) pathway
in budding yeast. Dynamic transcriptomic, proteomic, and
metabolomic measurements along shifts in nitrogen quality
yielded a consistent dataset that demonstrated extensive re-wiring
of cellular networks during adaptation. Our inference method
identified putative downstream targets of TORC1 and putative
metabolic inputs of TORC1, including the hypothesized glutamine
signal. The work provides a basis for further mechanistic studies of
nitrogen metabolism and a general computational framework to
study cellular processes.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.