A temporal logic approach to modular design of synthetic biological circuits

We present a new approach for the design of a synthetic biological circuit whose behaviour is specified in terms of signal temporal logic (STL) formulae. We first show how to characterise with STL formulae the input/output behaviour of biological modules miming the classical logical gates (AND, NOT, OR). Hence, we provide the regions of the parameter space for which these specifications are satisfied. Given a STL specification of the target circuit to be designed and the networks of its constituent components, we propose a methodology to constrain the behaviour of each module, then identifying the subset of the parameter space in which those constraints are satisfied, providing also a measure of the robustness for the target circuit design. This approach, which leverages recent results on the quantitative semantics of Signal Temporal Logic, is illustrated by synthesising a biological implementation of an half-adder

Encountering the World: Kawai Tsugunosuke's 1859 Journey to Yokohama and Nagasaki

Describes Kawai Tsugunosuke's journey in 1859 via his travel diaries, the people he encountered, his intellectual growth and especially his eagerness to make contact with foreigners trading with Japan.Early Modern Japan Networ

Specifying and Monitoring Properties of Stochastic Spatio-Temporal Systems in Signal Temporal Logic

We present an extension of the linear time, time-bounded, Signal Temporal Logic to describe spatio-temporal properties. We consider a discrete location/ patch-based representation of space, with a population of interacting agents evolving in each location and with agents migrating from one patch to another one. We provide both a boolean and a quantitative semantics to this logic. We then present monitoring algorithms to check the validity of a formula, or to compute its satisfaction (robustness) score, over a spatio-temporal trace, exploiting these routines to do statistical model checking of stochastic models. We illustrate the logic at work on an epidemic example, looking at the diffusion of a cholera infection among communities living along a river

WebMonitor: Verification of Web User Interfaces

Application development for the modern Web involves sophisticated engineering workflows which include user interface aspects. Those involve Web elements typically created with HTML/CSS markup and JavaScript-like languages, yielding Web documents. WebMonitor leverages requirements formally specified in a logic able to capture both the layout of visual components as well as how they change over time, as a user interacts with them. Then, requirements are verified upon arbitrary web pages, allowing for automated support for a wide set of use cases in interaction testing and simulation. We position WebMonitor within a developer workflow, where in case of a negative result, a visual counterexample is returned. The monitoring framework we present follows a black-box approach, and as such is independent of the underlying technologies a Web application may be developed with, as well as the browser and operating system used. WebMonitoris available as open source software: https://github.com/ennioVisco/webmonitor Video demonstration of WebMonitor: https://youtu.be/hqVw0JU3k9

One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems

Cyber-Physical Systems (CPS) are prevalent in critical infrastructures and a prime target for cyber-attacks. Multivariate time series data generated by sensors and actuators of a CPS can be monitored for detecting cyber-attacks that introduce anomalies in those data. We use Signal Temporal Logic (STL) formulas to tightly describe the normal behavior of a CPS, identifying data instances that do not satisfy the formulas as anomalies. We learn an ensemble of STL formulas based on observed data, without any specific knowledge of the CPS being monitored. We propose an algorithm based on Grammar-Guided Genetic Programming (G3P) that learns the ensemble automatically in a single evolutionary run. We test the effectiveness of our data-driven proposal on two real-world datasets, finding that the proposed one-shot algorithm provides good detection performance

Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes

We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and computationally efficient tool. The corresponding kernel trick allows us to circumvent the complicated process of feature extraction, i.e. the (typically manual) effort to identify the decisive properties of formulae so that learning can be applied. We demonstrate this consequence and its advantages on the task of predicting (quantitative) satisfaction of STL formulae on stochastic processes: Using our kernel and the kernel trick, we learn (i) computationally efficiently (ii) a practically precise predictor of satisfaction, (iii) avoiding the difficult task of finding a way to explicitly turn formulae into vectors of numbers in a sensible way. We back the high precision we have achieved in the experiments by a theoretically sound PAC guarantee, ensuring our procedure efficiently delivers a close-to-optimal predictor

On the robustness of temporal properties for stochastic models

Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic Hybrid Automata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling is the model checking problem. i.e. calculate the probability that a behaviour, expressed for instance in terms of a certain temporal logic formula, may occur in a given stochastic process. However, one may not only be interested in the notion of satisfiability, but also in the capacity of a system to mantain a particular emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic noise, or by possible small changes in the model parameters. To address this issue, researchers from the verification community have recently proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. The contributions of this paper are twofold. First, we extend the notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness scores. By discussing two examples, we show how to approximate the distribution of the robustness score and its key indicators: the average robustness and the conditional average robustness. Secondly, we show how to combine these indicators with the satisfaction probability to address the system design problem, where the goal is to optimize some control parameters of a stochastic model in order to best maximize robustness of the desired specifications