@article{540, abstract = {RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/-mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.}, author = {Khamina, Kseniya and Lercher, Alexander and Caldera, Michael and Schliehe, Christopher and Vilagos, Bojan and Sahin, Mehmet and Kosack, Lindsay and Bhattacharya, Anannya and Májek, Peter and Stukalov, Alexey and Sacco, Roberto and James, Leo and Pinschewer, Daniel and Bennett, Keiryn and Menche, Jörg and Bergthaler, Andreas}, issn = {15537366}, journal = {PLoS Pathogens}, number = {12}, publisher = {Public Library of Science}, title = {{Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein}}, doi = {10.1371/journal.ppat.1006758}, volume = {13}, year = {2017}, } @article{466, abstract = {We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. There exist two different views: (i) the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector. We consider optimization with respect to both objectives at once, thus unifying the existing semantics. Precisely, the goal is to optimize the expectation while ensuring the satisfaction constraint. Our problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., ensure certain probabilistic guarantee). Our main results are as follows: First, we present algorithms for the decision problems which are always polynomial in the size of the MDP. We also show that an approximation of the Pareto-curve can be computed in time polynomial in the size of the MDP, and the approximation factor, but exponential in the number of dimensions. Second, we present a complete characterization of the strategy complexity (in terms of memory bounds and randomization) required to solve our problem. }, author = {Chatterjee, Krishnendu and Křetínská, Zuzana and Kretinsky, Jan}, issn = {18605974}, journal = {Logical Methods in Computer Science}, number = {2}, publisher = {International Federation of Computational Logic}, title = {{Unifying two views on multiple mean-payoff objectives in Markov decision processes}}, doi = {10.23638/LMCS-13(2:15)2017}, volume = {13}, year = {2017}, } @article{467, abstract = {Recently there has been a significant effort to handle quantitative properties in formal verification and synthesis. While weighted automata over finite and infinite words provide a natural and flexible framework to express quantitative properties, perhaps surprisingly, some basic system properties such as average response time cannot be expressed using weighted automata or in any other known decidable formalism. In this work, we introduce nested weighted automata as a natural extension of weighted automata, which makes it possible to express important quantitative properties such as average response time. In nested weighted automata, a master automaton spins off and collects results from weighted slave automata, each of which computes a quantity along a finite portion of an infinite word. Nested weighted automata can be viewed as the quantitative analogue of monitor automata, which are used in runtime verification. We establish an almost-complete decidability picture for the basic decision problems about nested weighted automata and illustrate their applicability in several domains. In particular, nested weighted automata can be used to decide average response time properties.}, author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan}, issn = {15293785}, journal = {ACM Transactions on Computational Logic (TOCL)}, number = {4}, publisher = {ACM}, title = {{Nested weighted automata}}, doi = {10.1145/3152769}, volume = {18}, year = {2017}, } @article{465, abstract = {The edit distance between two words w 1 , w 2 is the minimal number of word operations (letter insertions, deletions, and substitutions) necessary to transform w 1 to w 2 . The edit distance generalizes to languages L 1 , L 2 , where the edit distance from L 1 to L 2 is the minimal number k such that for every word from L 1 there exists a word in L 2 with edit distance at most k . We study the edit distance computation problem between pushdown automata and their subclasses. The problem of computing edit distance to a pushdown automaton is undecidable, and in practice, the interesting question is to compute the edit distance from a pushdown automaton (the implementation, a standard model for programs with recursion) to a regular language (the specification). In this work, we present a complete picture of decidability and complexity for the following problems: (1) deciding whether, for a given threshold k , the edit distance from a pushdown automaton to a finite automaton is at most k , and (2) deciding whether the edit distance from a pushdown automaton to a finite automaton is finite. }, author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Ibsen-Jensen, Rasmus and Otop, Jan}, issn = {18605974}, journal = {Logical Methods in Computer Science}, number = {3}, publisher = {International Federation of Computational Logic}, title = {{Edit distance for pushdown automata}}, doi = {10.23638/LMCS-13(3:23)2017}, volume = {13}, year = {2017}, } @article{512, abstract = {The fixation probability is the probability that a new mutant introduced in a homogeneous population eventually takes over the entire population. The fixation probability is a fundamental quantity of natural selection, and known to depend on the population structure. Amplifiers of natural selection are population structures which increase the fixation probability of advantageous mutants, as compared to the baseline case of well-mixed populations. In this work we focus on symmetric population structures represented as undirected graphs. In the regime of undirected graphs, the strongest amplifier known has been the Star graph, and the existence of undirected graphs with stronger amplification properties has remained open for over a decade. In this work we present the Comet and Comet-swarm families of undirected graphs. We show that for a range of fitness values of the mutants, the Comet and Cometswarm graphs have fixation probability strictly larger than the fixation probability of the Star graph, for fixed population size and at the limit of large populations, respectively. }, author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin}, issn = {20452322}, journal = {Scientific Reports}, number = {1}, publisher = {Nature Publishing Group}, title = {{Amplification on undirected population structures: Comets beat stars}}, doi = {10.1038/s41598-017-00107-w}, volume = {7}, year = {2017}, }