@inproceedings{1658, abstract = {Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.}, author = {Bogomolov, Sergiy and Henzinger, Thomas A and Podelski, Andreas and Ruess, Jakob and Schilling, Christian}, location = {Nantes, France}, pages = {77 -- 89}, publisher = {Springer}, title = {{Adaptive moment closure for parameter inference of biochemical reaction networks}}, doi = {10.1007/978-3-319-23401-4_8}, volume = {9308}, year = {2015}, } @inproceedings{1660, abstract = {We study the pattern frequency vector for runs in probabilistic Vector Addition Systems with States (pVASS). Intuitively, each configuration of a given pVASS is assigned one of finitely many patterns, and every run can thus be seen as an infinite sequence of these patterns. The pattern frequency vector assigns to each run the limit of pattern frequencies computed for longer and longer prefixes of the run. If the limit does not exist, then the vector is undefined. We show that for one-counter pVASS, the pattern frequency vector is defined and takes one of finitely many values for almost all runs. Further, these values and their associated probabilities can be approximated up to an arbitrarily small relative error in polynomial time. For stable two-counter pVASS, we show the same result, but we do not provide any upper complexity bound. As a byproduct of our study, we discover counterexamples falsifying some classical results about stochastic Petri nets published in the 80s.}, author = {Brázdil, Tomáš and Kiefer, Stefan and Kučera, Antonín and Novotny, Petr}, location = {Kyoto, Japan}, pages = {44 -- 55}, publisher = {IEEE}, title = {{Long-run average behaviour of probabilistic vector addition systems}}, doi = {10.1109/LICS.2015.15}, year = {2015}, } @article{1665, abstract = {Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. Here we identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukaemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized putative cancer drivers (RPS15, IKZF3), and collectively identify RNA processing and export, MYC activity, and MAPK signalling as central pathways involved in CLL. Clonality analysis of this large data set further enabled reconstruction of temporal relationships between driver events. Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution. Thus, large sequencing data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.}, author = {Landau, Dan and Tausch, Eugen and Taylor Weiner, Amaro and Stewart, Chip and Reiter, Johannes and Bahlo, Jasmin and Kluth, Sandra and Božić, Ivana and Lawrence, Michael and Böttcher, Sebastian and Carter, Scott and Cibulskis, Kristian and Mertens, Daniel and Sougnez, Carrie and Rosenberg, Mara and Hess, Julian and Edelmann, Jennifer and Kless, Sabrina and Kneba, Michael and Ritgen, Matthias and Fink, Anna and Fischer, Kirsten and Gabriel, Stacey and Lander, Eric and Nowak, Martin and Döhner, Hartmut and Hallek, Michael and Neuberg, Donna and Getz, Gad and Stilgenbauer, Stephan and Wu, Catherine}, journal = {Nature}, number = {7574}, pages = {525 -- 530}, publisher = {Nature Publishing Group}, title = {{Mutations driving CLL and their evolution in progression and relapse}}, doi = {10.1038/nature15395}, volume = {526}, year = {2015}, } @article{1663, abstract = {CREB-binding protein (CBP) and p300 are transcriptional coactivators involved in numerous biological processes that affect cell growth, transformation, differentiation, and development. In this study, we provide evidence of the involvement of homeodomain-interacting protein kinase 2 (HIPK2) in the regulation of CBP activity. We show that HIPK2 interacts with and phosphorylates several regions of CBP. We demonstrate that serines 2361, 2363, 2371, 2376, and 2381 are responsible for the HIPK2-induced mobility shift of CBP C-terminal activation domain. Moreover, we show that HIPK2 strongly potentiates the transcriptional activity of CBP. However, our data suggest that HIPK2 activates CBP mainly by counteracting the repressive action of cell cycle regulatory domain 1 (CRD1), located between amino acids 977 and 1076, independently of CBP phosphorylation. Our findings thus highlight a complex regulation of CBP activity by HIPK2, which might be relevant for the control of specific sets of target genes involved in cellular proliferation, differentiation and apoptosis.}, author = {Kovács, Krisztián and Steinmann, Myriam and Halfon, Olivier and Magistretti, Pierre and Cardinaux, Jean}, journal = {Cellular Signalling}, number = {11}, pages = {2252 -- 2260}, publisher = {Elsevier}, title = {{Complex regulation of CREB-binding protein by homeodomain-interacting protein kinase 2}}, doi = {10.1016/j.cellsig.2015.08.001}, volume = {27}, year = {2015}, } @inproceedings{1667, abstract = {We consider parametric version of fixed-delay continuoustime Markov chains (or equivalently deterministic and stochastic Petri nets, DSPN) where fixed-delay transitions are specified by parameters, rather than concrete values. Our goal is to synthesize values of these parameters that, for a given cost function, minimise expected total cost incurred before reaching a given set of target states. We show that under mild assumptions, optimal values of parameters can be effectively approximated using translation to a Markov decision process (MDP) whose actions correspond to discretized values of these parameters. To this end we identify and overcome several interesting phenomena arising in systems with fixed delays.}, author = {Brázdil, Tomáš and Korenčiak, L'Uboš and Krčál, Jan and Novotny, Petr and Řehák, Vojtěch}, location = {Madrid, Spain}, pages = {141 -- 159}, publisher = {Springer}, title = {{Optimizing performance of continuous-time stochastic systems using timeout synthesis}}, doi = {10.1007/978-3-319-22264-6_10}, volume = {9259}, year = {2015}, }