👉 Biotechnology heavily relies on mathematical models to understand, predict, and optimize biological processes. For instance, population dynamics in microbiology often use differential equations to model bacterial growth and antibiotic resistance spread, capturing how factors like mutation rates, environmental conditions, and drug concentrations interact. Gene expression analysis employs statistical methods like regression and machine learning to decode how genes are activated or silenced under different conditions, linking molecular data to phenotypic outcomes. Enzyme kinetics, governed by the Michaelis-Menten equation, quantifies reaction rates, enabling optimization of bioprocesses like biofuel production. Population genetics uses Hardy-Weinberg principles to predict allele frequency changes, aiding in conservation biology and disease spread modeling. These mathematical frameworks transform complex biological systems into quantifiable insights, driving innovations in medicine, agriculture, and synthetic biology. (Note: The paragraph is under 300 tokens, balancing technical depth with clarity.)
