@phdthesis{Lohse2004,
  author    = {Lohse, Marc-Andr{\´e}},
  title     = {Funktionelle Analyse des CHoR-Proteins aus Arabidopsis thaliana},
  address   = {Potsdam},
  pages     = {145 Bl. : Ill., graph. Darst.},
  year      = {2004},
  language  = {de}
}
@article{RyngajlloChildsLohseetal.2011,
  author    = {Ryngajllo, Malgorzata and Childs, Liam H. and Lohse, Marc and Giorgi, Federico M. and Lude, Anja and Selbig, Joachim and Usadel, Bj{\"o}rn},
  title     = {SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data},
  series = {Frontiers in plant science},
  volume    = {2},
  journal   = {Frontiers in plant science},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2011.00043},
  pages     = {19},
  year      = {2011},
  abstract  = {Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.},
  language  = {en}
}
@article{MettlerMuehlhausHemmeetal.2014,
  author    = {Mettler, Tabea and M{\"u}hlhaus, Timo and Hemme, Dorothea and Sch{\"o}ttler, Mark Aurel and Rupprecht, Jens and Idoine, Adam and Veyel, Daniel and Pal, Sunil Kumar and Yaneva-Roder, Liliya and Winck, Flavia Vischi and Sommer, Frederik and Vosloh, Daniel and Seiwert, Bettina and Erban, Alexander and Burgos, Asdrubal and Arvidsson, Samuel Janne and Schoenfelder, Stephanie and Arnold, Anne and Guenther, Manuela and Krause, Ursula and Lohse, Marc and Kopka, Joachim and Nikoloski, Zoran and M{\"u}ller-R{\"o}ber, Bernd and Willmitzer, Lothar and Bock, Ralph and Schroda, Michael and Stitt, Mark},
  title     = {Systems analysis of the response of photosynthesis, metabolism, and growth to an increase in irradiance in the photosynthetic model organism chlamydomonas reinhardtii},
  series = {The plant cell},
  volume    = {26},
  journal   = {The plant cell},
  number    = {6},
  publisher = {American Society of Plant Physiologists},
  address   = {Rockville},
  issn      = {1040-4651},
  doi       = {10.1105/tpc.114.124537},
  pages     = {2310 -- 2350},
  year      = {2014},
  abstract  = {We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R-2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance.},
  language  = {en}
}