@inproceedings{1421, abstract = {Hybridization methods enable the analysis of hybrid automata with complex, nonlinear dynamics through a sound abstraction process. Complex dynamics are converted to simpler ones with added noise, and then analysis is done using a reachability method for the simpler dynamics. Several such recent approaches advocate that only "dynamic" hybridization techniquesi.e., those where the dynamics are abstracted on-The-fly during a reachability computation are effective. In this paper, we demonstrate this is not the case, and create static hybridization methods that are more scalable than earlier approaches. The main insight in our approach is that quick, numeric simulations can be used to guide the process, eliminating the need for an exponential number of hybridization domains. Transitions between domains are generally timetriggered, avoiding accumulated error from geometric intersections. We enhance our static technique by combining time-Triggered transitions with occasional space-Triggered transitions, and demonstrate the benefits of the combined approach in what we call mixed-Triggered hybridization. Finally, error modes are inserted to confirm that the reachable states stay within the hybridized regions. The developed techniques can scale to higher dimensions than previous static approaches, while enabling the parallelization of the main performance bottleneck for many dynamic hybridization approaches: The nonlinear optimization required for sound dynamics abstraction. We implement our method as a model transformation pass in the HYST tool, and perform reachability analysis and evaluation using an unmodified version of SpaceEx on nonlinear models with up to six dimensions.}, author = {Bak, Stanley and Bogomolov, Sergiy and Henzinger, Thomas A and Johnson, Taylor and Prakash, Pradyot}, location = {Vienna, Austria}, pages = {155 -- 164}, publisher = {Springer}, title = {{Scalable static hybridization methods for analysis of nonlinear systems}}, doi = {10.1145/2883817.2883837}, year = {2016}, } @inproceedings{1439, abstract = {Fault-tolerant distributed algorithms play an important role in many critical/high-availability applications. These algorithms are notoriously difficult to implement correctly, due to asynchronous communication and the occurrence of faults, such as the network dropping messages or computers crashing. We introduce PSYNC, a domain specific language based on the Heard-Of model, which views asynchronous faulty systems as synchronous ones with an adversarial environment that simulates asynchrony and faults by dropping messages. We define a runtime system for PSYNC that efficiently executes on asynchronous networks. We formalize the relation between the runtime system and PSYNC in terms of observational refinement. The high-level lockstep abstraction introduced by PSYNC simplifies the design and implementation of fault-tolerant distributed algorithms and enables automated formal verification. We have implemented an embedding of PSYNC in the SCALA programming language with a runtime system for asynchronous networks. We show the applicability of PSYNC by implementing several important fault-tolerant distributed algorithms and we compare the implementation of consensus algorithms in PSYNC against implementations in other languages in terms of code size, runtime efficiency, and verification.}, author = {Dragoi, Cezara and Henzinger, Thomas A and Zufferey, Damien}, location = {St. Petersburg, FL, USA}, pages = {400 -- 415}, publisher = {ACM}, title = {{PSYNC: A partially synchronous language for fault-tolerant distributed algorithms}}, doi = {10.1145/2837614.2837650}, volume = {20-22}, year = {2016}, } @inproceedings{1524, abstract = {When designing genetic circuits, the typical primitives used in major existing modelling formalisms are gene interaction graphs, where edges between genes denote either an activation or inhibition relation. However, when designing experiments, it is important to be precise about the low-level mechanistic details as to how each such relation is implemented. The rule-based modelling language Kappa allows to unambiguously specify mechanistic details such as DNA binding sites, dimerisation of transcription factors, or co-operative interactions. Such a detailed description comes with complexity and computationally costly executions. We propose a general method for automatically transforming a rule-based program, by eliminating intermediate species and adjusting the rate constants accordingly. To the best of our knowledge, we show the first automated reduction of rule-based models based on equilibrium approximations. Our algorithm is an adaptation of an existing algorithm, which was designed for reducing reaction-based programs; our version of the algorithm scans the rule-based Kappa model in search for those interaction patterns known to be amenable to equilibrium approximations (e.g. Michaelis-Menten scheme). Additional checks are then performed in order to verify if the reduction is meaningful in the context of the full model. The reduced model is efficiently obtained by static inspection over the rule-set. The tool is tested on a detailed rule-based model of a λ-phage switch, which lists 92 rules and 13 agents. The reduced model has 11 rules and 5 agents, and provides a dramatic reduction in simulation time of several orders of magnitude.}, author = {Beica, Andreea and Guet, Calin C and Petrov, Tatjana}, location = {Madrid, Spain}, pages = {173 -- 191}, publisher = {Springer}, title = {{Efficient reduction of kappa models by static inspection of the rule-set}}, doi = {10.1007/978-3-319-26916-0_10}, volume = {9271}, year = {2016}, } @inproceedings{1526, abstract = {We present the first study of robustness of systems that are both timed as well as reactive (I/O). We study the behavior of such timed I/O systems in the presence of uncertain inputs and formalize their robustness using the analytic notion of Lipschitz continuity: a timed I/O system is K-(Lipschitz) robust if the perturbation in its output is at most K times the perturbation in its input. We quantify input and output perturbation using similarity functions over timed words such as the timed version of the Manhattan distance and the Skorokhod distance. We consider two models of timed I/O systems — timed transducers and asynchronous sequential circuits. We show that K-robustness of timed transducers can be decided in polynomial space under certain conditions. For asynchronous sequential circuits, we reduce K-robustness w.r.t. timed Manhattan distances to K-robustness of discrete letter-to-letter transducers and show PSpace-completeness of the problem.}, author = {Henzinger, Thomas A and Otop, Jan and Samanta, Roopsha}, location = {St. Petersburg, FL, USA}, pages = {250 -- 267}, publisher = {Springer}, title = {{Lipschitz robustness of timed I/O systems}}, doi = {10.1007/978-3-662-49122-5_12}, volume = {9583}, year = {2016}, } @article{1148, 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. © 2016 Elsevier Ireland Ltd}, author = {Schilling, Christian and Bogomolov, Sergiy and Henzinger, Thomas A and Podelski, Andreas and Ruess, Jakob}, journal = {Biosystems}, pages = {15 -- 25}, publisher = {Elsevier}, title = {{Adaptive moment closure for parameter inference of biochemical reaction networks}}, doi = {10.1016/j.biosystems.2016.07.005}, volume = {149}, year = {2016}, }