Combining Zhegalkin Polynomials and SAT Solving for Context-specific Boolean Modeling of Biological Systems.

Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biological phenomena at multiple scales. Yet, when a model is required to understand or predict the behavior of a biological system in given conditions, existing information often does not completely match this context.

Profiling of metastatic small intestine neuroendocrine tumors reveals characteristic miRNAs detectable in plasma.

Background: Current diagnostic and prognostic blood-based biomarkers for neuroendocrine tumors are limited. MiRNAs have tumor-specific expression patterns, are relatively stable, and can be measured in patient blood specimens. We performed a multi-stage study to identify and validate characteristic circulating miRNAs in patients with metastatic small intestine neuroendocrine tumors, and to assess associations between miRNA levels and survival.

Characterizing short stature by insulin-like growth factor axis status and genetic associations: results from the prospective, cross-sectional, epidemiogenetic EPIGROW study.

Context: Serum IGF-I levels are often low in patients with short stature (SS) without defined etiology. Hence, genetic investigations have focused on the GH-IGF-I axis.

Objective: Our objectives were to characterize IGF-I axis status and search for a broader range of genetic associations in children with SS and normal GH.