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Effect of the linear mapping on module size and enrichment
Linear mapping applied
No linear mapping
Disc. Expr. Pattern
For the modules that are most straightforwardly related to one of the the cultivation parameters (the four nutrient limitations and the oxygen availability) this table indicates the size of the respective module, the number of associated TFs, TF pairs and annotation categories; both with and without appliance of the linear mapping. (Note that when no linear mapping is applied the original continuous expression levels are discretized and no oxygen effect can be computed, resulting in a discretized expression pattern of length eight.)
See Table 2 .
Knijnenburg, Theo; de Winde, Johannes; Daran, Jean-Marc; Daran-Lapujade, Pascale; Pronk, Jack; Reinders, Marcel; Wessels, LodewykJournal: BMC Genomics
Issue 1DOI: 10.1186/1471-2164-8-25Published: 2007-01-22Institution(s):
Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands, Industrial Microbiology, Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands, Department of Molecular Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
Regulatory networks often employ the model that attributes changes in gene expression levels, as observed across different cellular conditions, to changes in the activity of transcription factors (TFs). Although the actual conditions that trigger a change in TF activity should form an integral part of the generated regulatory network, they are usually lacking. This is due to the fact that the large heterogeneity in the employed conditions and the continuous changes in environmental parameters in the often used shake-flask cultures, prevent the unambiguous modeling of the cultivation conditions within the computational framework.
We designed an experimental setup that allows us to explicitly model the cultivation conditions and use these to infer the activity of TFs. The yeast Saccharomyces cerevisiae was cultivated under four different nutrient limitations in both aerobic and anaerobic chemostat cultures. In the chemostats, environmental and growth parameters are accurately controlled. Consequently, the measured transcriptional response can be directly correlated with changes in the limited nutrient or oxygen concentration. We devised a tailor-made computational approach that exploits the systematic setup of the cultivation conditions in order to identify the individual and combined effects of nutrient limitations and oxygen availability on expression behavior and TF activity.
Incorporating the actual growth conditions when inferring regulatory relationships provides detailed insight in the functionality of the TFs that are triggered by changes in the employed cultivation conditions. For example, our results confirm the established role of TF Hap4 in both aerobic regulation and glucose derepression. Among the numerous inferred condition-specific regulatory associations between gene sets and TFs, also many novel putative regulatory mechanisms, such as the possible role of Tye7 in sulfur metabolism, were identified.
This table is from the article titled "Exploiting combinatorial cultivation conditions to infer transcriptional regulation"
(from BMC Genomics), which is copyrighted by Knijnenburg et al; licensee BioMed Central Ltd. For more information on the
copyright for this table, please refer to the full table caption and to the
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