Combinatorial influence of environmental parameters on transcription factor activity.
BACKGROUND
Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference.


RESULTS
We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression.


BACKGROUND
The Matlab code is available from the authors upon request.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Knijnenburg
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TA
Information and Communication Theory Group, Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. t.a.knijnenburg@tudelft.nl
Wessels
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LF
Reinders
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MJ
D018929:Cell Culture Techniques / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004777:Environment; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D014157:Transcription Factors / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors