Abstract. One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rule-based language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, concentration and population; and one temporal logic language used for formalizing the biological properties of the system. In this paper, we show how the temporal logic language can be turned into a specification language. We describe two algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. Then, with an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler. 1

Abstract. The switch-like character of the dynamics of genetic regulatory networks has attracted much attention from mathematical biologists and researchers on hybrid systems alike. We extend our previous work on a method for the qualitative analysis of hybrid models of genetic regulatory networks, based on a class of piecewise-affine differential equation (PADE) models, in two directions. First, we present a refinement of the method using a discrete or qualitative abstraction that preserves stronger properties of the dynamics of the PA systems, in particular the sign patterns of the derivatives of the concentration variables. The discrete transition system resulting from the abstraction is a conservative approximation of the dynamics of the PA system and can be computed symbolically. Second, we apply the refined method to a regulatory system whose functioning is not yet well-understood by biologists, the nutritional stress response in the bacterium Escherichia coli. 1

Abstract. Recent studies have demonstrated the possibility to build genetic regulatory networks that confer a desired behavior to a living organism. However, the design of these networks is difficult, notably because of uncertainties on parameter values. In previous work, we proposed an approach to analyze genetic regulatory networks with parameter uncertainties. In this approach, the models are based on piecewise-multiaffine (PMA) differential equations, the specifications are expressed in temporal logic, and uncertain parameters are given by intervals. Abstractions are used to obtain finite discrete representations of the dynamics of the system, amenable to model checking. However, the abstraction process creates spurious behaviors along which time does not progress, called time-converging behaviors. Consequently, the verification of liveness properties, expressing that something will eventually happen, and implicitly assuming progress of time, often fails. In this work, we extend our previous approach to enforce progress of time. More precisely, we define transient regions as subsets of the state space left in finite time by every solution trajectory, show how they can be used to rule out timeconverging behaviors, and provide sufficient conditions for their identification in PMA systems. This approach is implemented in RoVerGeNe and applied to the analysis of a network built in the bacterium E. coli. 1

Abstract. Components, connectors and architectures have now made a breakthrough in software industry, leading to Component-Based Software Engineering (CBSE). In this paper, we argue for the pragmatic use level and to solve CBSE issues. We give some possible benefits of such an approach and list some of its open issues. 1

We formally verify hybrid safety properties of cooperation protocols in a fully parametric version of the European Train Control System (ETCS). We present a formal model using hybrid programs and verify correctness using our logic-based decomposition procedure. This procedure supports free parameters and parameter discovery, which is required to determine correct design choices for free parameters of ETCS.

Complex physical systems have several degrees of freedom. They only work correctly when their control parameters obey corresponding constraints. Based on the informal specification of the European Train Control System (ETCS), we design a controller for its cooperation protocol. For its free parameters, we successively identify constraints that are required to ensure collision freedom. We formally prove the parameter constraints to be sharp by characterizing them equivalently in terms of reachability properties of the hybrid system dynamics. Using our deductive verification tool KeYmaera, we formally verify controllability, safety, liveness, and reactivity properties of the ETCS protocol that entail collision freedom. We prove that the ETCS protocol remains correct even in the presence of perturbation by disturbances in the dynamics. We verify that safety is preserved when a PI controlled speed supervision is used.

The lack of precise numerical information for the values of biological parameters severely limits the development and analysis of models of genetic regulatory networks. To deal with this problem, we propose a method for the analysis of genetic regulatory networks under parameter uncertainty. We consider models based on piecewise-multiaffine differential equations, dynamical properties expressed in temporal logic, and intervals for the values of uncertain parameters. The problem is then either to guarantee that the system satisfies the expected properties for every possible parameter value – the corresponding parameter set is then called valid – or to find valid subsets of a given parameter set. The proposed method uses discrete abstractions and model checking, and allows for efficient search of the parameter space. However, the abstraction process creates spurious behaviors in the abstract systems, along which time does not progress. Consequently, the verification of liveness properties, expressing that something will eventually happen, and implicitly assuming progress of time, often fails. A solution to this second problem is proposed using the notion of transient regions. This approach has been implemented in a tool for robust verification of gene networks (RoVerGeNe) and applied to the tuning of a synthetic network built in E. coli.

Abstract. Temporal logics and model-checking techniques have proved successful to respectively express biological properties of complex biochemical systems, and automatically verify their satisfaction in both qualitative and quantitative models. In this paper, we propose a finite time horizon model-checking algorithm for the existential fragment of LTL with numerical constraints over the reals, with the ability to compute the range of values of the real variables occurring in a formula that makes it true in a model. We illustrate this approach for the analysis of biological data time series, provide a set of biologically relevant patterns of formulas, and evaluate them on models of the cell cycle control and MAPK signal transduction. 1

Temporal logics and model-checking have proved successful to respectively express biological properties of complex biochemical systems, and automatically verify their satisfaction in both qualitative and quantitative models. In this article, we go beyond model-checking and present a constraint solving algorithm for quantifier-free first-order temporal logic formulae with constraints over the reals. This algorithm computes the domain of the real valued variables occurring in a formula that makes it true in a model. We illustrate this approach for the automatic generation of temporal logic specification from biological data time series. We provide a set of biologically relevant patterns of formulae, and apply them on numerical data time series of models of the cell cycle control and MAPK signal transduction. We show on these examples that this approach succeeds in inferring automatically semiqualititave semi-quantitative information on concentration thresholds, amplitude of oscillations, stability properties, checkpoints and influences between species. Key words: model-checking, temporal logic, constraint solving, data time series, systems biology 1

Abstract. The dynamics of biological processes are often modeled as systems of nonlinear ordinary differential equations (ODE). An important feature of nonlinear ODEs is that seemingly minor changes in initial conditions or parameters can lead to radically different behaviors. This is problematic because in general it is never possible to know/measure the precise state of any biological system due to measurement errors. The parameter synthesis problem is to identify sets of parameters (including initial conditions) for which a given system of nonlinear ODEs does not reach a given set of undesirable states. We present an efficient algorithm for solving this problem that combines sensitivity analysis with an efficient search over initial conditions. It scales to high-dimensional models and is exact if the given model is affine. We demonstrate our method on a model of the acute inflammatory response to bacterial infection, and identify initial conditions consistent with 3 biologically relevant outcomes.