@article{OmranianEloundouMbebiMuellerRoeberetal.2016,
  author    = {Omranian, Nooshin and Eloundou-Mbebi, Jeanne Marie Onana and M{\"u}ller-R{\"o}ber, Bernd and Nikoloski, Zoran},
  title     = {Gene regulatory network inference using fused LASSO on multiple data sets},
  series = {Scientific reports},
  volume    = {6},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/srep20533},
  pages     = {14},
  year      = {2016},
  abstract  = {Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.},
  language  = {en}
}
@article{SedaghatmehrMuellerRoeberBalazadeh2016,
  author    = {Sedaghatmehr, Mastoureh and M{\"u}ller-R{\"o}ber, Bernd and Balazadeh, Salma},
  title     = {The plastid metalloprotease FtsH6 and small heat shock protein HSP21 jointly regulate thermomemory in Arabidopsis},
  series = {Nature Communications},
  volume    = {7},
  journal   = {Nature Communications},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/ncomms12439},
  pages     = {14},
  year      = {2016},
  abstract  = {Acquired tolerance to heat stress is an increased resistance to elevated temperature following a prior exposure to heat. The maintenance of acquired thermotolerance in the absence of intervening stress is called 'thermomemory' but the mechanistic basis for this memory is not well defined. Here we show that Arabidopsis HSP21, a plastidial small heat shock protein that rapidly accumulates after heat stress and remains abundant during the thermomemory phase, is a crucial component of thermomemory. Sustained memory requires that HSP21 levels remain high. Through pharmacological interrogation and transcriptome profiling, we show that the plastid-localized metalloprotease FtsH6 regulates HSP21 abundance. Lack of a functional FtsH6 protein promotes HSP21 accumulation during the later stages of thermomemory and increases thermomemory capacity. Our results thus reveal the presence of a plastidial FtsH6-HSP21 control module for thermomemory in plants.},
  language  = {en}
}