Genetics

This blog post examines how systems biology and mathematical modeling can improve biomarker discovery in multiple sclerosis by integrating molecular, cellular, clinical, and metabolic data. Drawing on the reviewed article, it highlights the progression from static biomarker identification to dynamic network analysis, differential-equation models, and Chemical Reaction Network Theory. Particular attention is given to metabolism as a mathematically tractable biological system in which enzyme kinetics, reaction networks, and multiple steady states can reveal disease mechanisms and potentially support patient stratification, prediction of treatment response, and more personalized therapeutic strategies.