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A metabolic simulator is essential to reduce adverse effects of clinical studies. Moreover, it reduces monetary costs when developing new drugs and control algorithms for new devices. Diabetes is one of the diseases, where the metabolic simulation has established. Such a simulator mimics real metabolism to provide simulated responses to external events such as meal ingestion, physical activity, drugs, and pump-delivered insulin. This paper proposes a novel approach, which addresses limits of the current metabolic simulators. First, a simulator is usually based on a set of differential equations, which are de facto black boxes for physicians. The proposed approach, Sirael, provides a metabolic language, which describes chemical reactions and bodily fluid equilibration in a manner lectured at medical faculties. Second, the current metabolic simulators are authority accepted with a single-day scenario and few meals only. The proposed framework targets multiple weeks. Third, Sirael can model other diseases, drugs, and treatments, because its language is not bound to diabetes only. Fourth, Sirael required readily available equipment only, when applied to Diabetes Type 1. We created 18 digital twins from real patients, with 37 identified initial states, average relative error of 8.7%, and standard deviation of 8.0%. This provides free, open-source, and statistically significant base for conducting Diabetes Type 1 in silico studies