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We present HepatoNet1, a manually curated large-scale metabolic network of the human hepatocyte that encompasses >2500 reactions in six intracellular and two extracellular compartments.Using constraint-based modeling techniques, the network has been validated to replicate numerous metabolic functions of hepatocytes corresponding to a reference set of diverse physiological liver functions.Taking the detoxification of ammonia and the formation of bile acids as examples, we show how these liver-specific metabolic objectives can be achieved by the variable interplay of various metabolic pathways under varying conditions of nutrients and oxygen availability.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.

The design of a portable Raman/SERS‐LFA reader with line illumination using a custom‐made fiber optic probe for rapid, quantitative, and ultrasensitive point‐of‐care testing (POCT) is presented. The pregnancy hormone human chorionic gonadotropin (hCG) is detectable in clinical samples within only 2–5 s down to approximately 1.6 mIU mL −1 . This acquisition time is several orders of magnitude shorter than those of existing approaches requiring expensive Raman instrumentation, and the method is 15‐times more sensitive than a commercially available lateral flow assay (LFA) as the gold standard. The SERS‐LFA technology paves the way for affordable, quantitative, and ultrasensitive POCT with multiplexing potential in real‐world applications, ranging from clinical chemistry to food and environmental analysis as well as drug and biowarfare agent testing.