Mathematical models play a vital role in understanding complex biological systems. However, these systems are often characterized by indirectly and partially observed, noisy, and high-dimensional dynamics and data. Current models often fail to adequately account for the various sources of uncertainty within the data, leading to inaccurate or incomplete models with limited explanatory power. The field of uncertainty quantification (UQ) offers approaches to address these challenges.