This study was designed to examine whether resveratrol exerts the protective effects on LPS and cigarette smoke (LC)-induced COPD in a murine model. In lung histopathological studies, H&E, Masson's trichrome, and AB-PAS staining were performed. The cytokines (IL-6, IL-17, TGF-β, and TNF-α) and inflammatory cells in BALF were determined. The Beclin1 level in the lungs of mouse was analyzed. Compared with the LC-induced mouse, the level of inflammatory cytokines (IL-17, IL-6, TNF-α, and TGF-β) of the BALF in the resveratrol + cigarette smoke-treated mouse had obviously decreased. Histological examination of the lung tissue revealed that the resveratrol treatment attenuated the fibrotic response and mucus hypersecretion. In addition, resveratrol inhibited the expression of the Beclin1 protein in mouse lungs. The presented findings collectively suggest that resveratrol has a therapeutic effect on mouse LC-induced COPD, and its mechanism of action might be related to reducing the production of the Beclin1 protein.

Unambiguous experiment descriptions are increasingly required for model publication, as they contain information important for reproducing simulation results. In the context of model composition, this information can be used to generate experiments for the composed model. If the original experiment descriptions specify which model property they refer to, we can then execute the generated experiments and assess the validity of the composed model by evaluating their results. Thereby, we move the attention to describing properties of a model's behavior and the conditions under which these hold, i.e., its semantics. We illuminate the potential of this concept by considering the composition of Lotka-Volterra models. In a first prototype realized for JAMES II, we use ML-Rules to describe and execute the Lotka-Volterra models and SESSL for specifying the original experiments. Model properties are described in continuous stochastic logic, and we use statistical model checking for their evaluation. Based on this, experiments to check whether these properties hold for the composed model are automatically generated and executed.

With the increasing size and complexity of models, developing models by composing existing ones becomes more important. We exploit the idea of reusing simulation experiments of individual models for composition to automatically generate experiments for the composed model. First, we illustrate the process of modeling based on composition and discuss the role simulation experiments can play in this process. Our focus is on semantic validation of the composed model. We explicitly specify simulation experiments in simulation experiment specification via a Scala layer, including the desired model behavioral properties and their required experiment setups. Models are annotated with experiment specifications, and upon composition, these specifications are adapted and automatically executed for the composed model. The approach is applied in a case study of developing a Wnt/b-catenin signaling pathway model by successively composing three individual models, where we exploit metric interval temporal logic to describe model behavioral properties and check averages of stochastic simulation results against these properties.

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