The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?

Understanding the regulation of metabolism in time and space is critical for many biological problems, be it the growth of tumors or the adaptation of the gut microbiome to diet. However, the need for quantitative and dynamic understanding and the effort to gain the appropriate data usable in computational models diverge dramatically. Nowadays, metabolism on a genome scale is primarily studied with methods that refer to steady states and conclusions to dynamics and quantitative aspects are only made in an indirect way. There are theoretical concepts that could in principle deliver dynamic and quantitative descriptions, such as ordinary differential equation systems employing tailored rate laws for enzymatic reactions, but they dramatically lack information about the required parameter values and intracellular concentrations as well as computationally feasible parameterization methods.