@article{VidelaGuziolowskiEduatietal.2015,
  author    = {Videla, Santiago and Guziolowski, Carito and Eduati, Federica and Thiele, Sven and Gebser, Martin and Nicolas, Jacques and Saez-Rodriguez, Julio and Schaub, Torsten H. and Siegel, Anne},
  title     = {Learning Boolean logic models of signaling networks with ASP},
  series = {Theoretical computer science},
  volume    = {599},
  journal   = {Theoretical computer science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3975},
  doi       = {10.1016/j.tcs.2014.06.022},
  pages     = {79 -- 101},
  year      = {2015},
  abstract  = {Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{UllnerAresMorellietal.2012,
  author    = {Ullner, E. and Ares, S. and Morelli, L. G. and Oates, A. C. and J{\"u}licher, F. and Nicola, E. and Heussen, R. and Whitmore, D. and Blyuss, K. and Fryett, M. and Zakharova, A. and Koseska, A. and Nene, N. R. and Zaikin, Alexei},
  title     = {Noise and oscillations in biological sysems multidisciplinary approach between experimental biology, theoretical modelling and synthetic biology},
  series = {International journal of modern physics : B, Condensed matter physics, statistical physics, applied physics},
  volume    = {26},
  journal   = {International journal of modern physics : B, Condensed matter physics, statistical physics, applied physics},
  number    = {25},
  publisher = {World Scientific},
  address   = {Singapore},
  issn      = {0217-9792},
  doi       = {10.1142/S0217979212460095},
  pages     = {12},
  year      = {2012},
  abstract  = {Rapid progress of experimental biology has provided a huge flow of quantitative data, which can be analyzed and understood only through the application of advanced techniques recently developed in theoretical sciences. On the other hand, synthetic biology enabled us to engineer biological models with reduced complexity. In this review we discuss that a multidisciplinary approach between this sciences can lead to deeper understanding of the underlying mechanisms behind complex processes in biology. Following the mini symposia "Noise and oscillations in biological systems" on Physcon 2011 we have collected different research examples from theoretical modeling, experimental and synthetic biology.},
  language  = {en}
}
@article{WinckRianoPachonSommeretal.2012,
  author    = {Winck, Flavia V. and Riano-Pachon, Diego M. and Sommer, Frederik and Rupprecht, Jens and M{\"u}ller-R{\"o}ber, Bernd},
  title     = {The nuclear proteome of the green alga Chlamydomonas reinhardtii},
  series = {Proteomics},
  volume    = {12},
  journal   = {Proteomics},
  number    = {1},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {1615-9853},
  doi       = {10.1002/pmic.201000782},
  pages     = {95 -- 100},
  year      = {2012},
  abstract  = {Nuclear proteins play a central role in regulating gene expression. Their identification is important for understanding how the nuclear repertoire changes over time under different conditions. Nuclear proteins are often underrepresented in proteomic studies due to the frequently low abundance of proteins involved in regulatory processes. So far, only few studies describing the nuclear proteome of plant species have been published. Recently, the genome sequence of the unicellular green alga Chlamydomonas reinhardtii has been obtained and annotated, allowing the development of further detailed studies for this organism. However, a detailed description of its nuclear proteome has not been reported so far. Here, we present an analysis of the nuclear proteome of the sequenced Chlamydomonas strain cc503. Using LC-MS/MS, we identified 672 proteins from nuclei isolates with a maximum 1\% peptide spectrum false discovery rate. Besides well-known proteins (e.g. histones), transcription factors and other transcriptional regulators (e.g. tubby and HMG) were identified. The presence of protein motifs in nuclear proteins was investigated by computational tools, and specific over-represented protein motifs were identified. This study provides new insights into the complexity of the nuclear environment and reveals novel putative protein targets for further studies of nuclear mechanisms.},
  language  = {en}
}