@misc{LarhlimiDavidSelbigetal.2012, author = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander}, title = {F2C2}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {921}, issn = {1866-8372}, doi = {10.25932/publishup-43243}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-432431}, pages = {11}, year = {2012}, abstract = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.}, language = {en} } @misc{KaminskiSchaubSiegeletal.2013, author = {Kaminski, Roland and Schaub, Torsten H. and Siegel, Anne and Videla, Santiago}, title = {Minimal intervention strategies in logical signaling networks with ASP}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, number = {4-5}, issn = {1866-8372}, doi = {10.25932/publishup-41570}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-415704}, pages = {675 -- 690}, year = {2013}, abstract = {Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.}, language = {en} }