@misc{MayChristianKempaetal.2009,
  author    = {May, Patrick and Christian, Jan-Ole and Kempa, Stefan and Walther, Dirk},
  title     = {ChlamyCyc : an integrative systems biology database and web-portal for Chlamydomonas reinhardtii},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-44947},
  year      = {2009},
  abstract  = {Background: The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern highthroughput technologies there is an imperative need to integrate large-scale data sets from highthroughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. Results: In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. Conclusion: ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.},
  language  = {en}
}
@article{ChristianMayKempaetal.2009,
  author    = {Christian, Nils and May, Patrick and Kempa, Stefan and Handorf, Thomas and Ebenhoeh, Oliver},
  title     = {An integrative approach towards completing genome-scale metabolic networks},
  issn      = {1742-206X},
  doi       = {10.1039/B915913b},
  year      = {2009},
  abstract  = {Genome-scale metabolic networks which have been automatically derived through sequence comparison techniques are necessarily incomplete. We propose a strategy that incorporates genomic sequence data and metabolite profiles into modeling approaches to arrive at improved gene annotations and more complete genome-scale metabolic networks. The core of our strategy is an algorithm that computes minimal sets of reactions by which a draft network has to be extended in order to be consistent with experimental observations. A particular strength of our approach is that alternative possibilities are suggested and thus experimentally testable hypotheses are produced. We carefully evaluate our strategy on the well-studied metabolic network of Escherichia coli, demonstrating how the predictions can be improved by incorporating sequence data. Subsequently, we apply our method to the recently sequenced green alga Chlamydomonas reinhardtii. We suggest specific genes in the genome of Chlamydomonas which are the strongest candidates for coding the responsible enzymes.},
  language  = {en}
}