Metabolism constitutes the dynamic network of biochemical reactions that sustain life, and our understanding of it has been indispensable to the advancement of biotechnology and medicine. Metabolic fluxes describe biology in motion, connecting the various omics datasets to biological phenotype. While quantitative flux analysis has unlocked many novel discoveries in systems biology, there remain a plethora of unanswered questions where knowledge of fluxes would help. This dissertation explores the development and application of techniques for flux analysis to quantitatively investigate biological systems across all spans of life, from single-celled organisms to whole animals and beyond.Beginning with the unicellular model organism E. coli, we investigate the evolutionary role of parallel glycolytic pathways. Glycolysis is a highly conserved pathway for the production of energy and biomass. However, some organisms simultaneously express the parallel Entner-Doudoroff (ED) pathway, and the role of this seemingly less efficient pathway remains unclear. We demonstrate that the ED pathway supports cell growth by rapid acceleration of glycolysis. We further develop an isotope tracing strategy to quantify the distribution of glycolytic pathways during dynamic metabolic responses to changes in nutrient environment, finding that while ED pathway utilization is low in the nutrient limited state, flux through the ED pathway increases disproportionately higher than textbook glycolysis upon nutrient upshifts. Consequently, the two pathways jointly increase total glycolytic flux to more rapidly increase cellular growth rate. We surmise that the benefits of parallel pathways play a role in the holistic design of metabolic systems and that organisms may employ similar strategies elsewhere in metabolism. Moving to larger organisms, we established methods to quantify metabolic fluxes in whole animals and individual tissues to investigate how animals maintain glucose homeostasis. Systemic regulation of circulating glucose via insulin is understood, but less is known about what happens after its increased uptake into tissues due to difficulties quantifying fluxes in vivo. Here, we innovate a multiplexed stable isotope tracing strategy to investigate the rates of glucose metabolism following hyperglycemic challenge. Quantitative flux modeling reveals the fates of glucose through glycolysis and other metabolic branches across 16 tissues. We find that while increased uptake supports whole-body regulation, individual tissues rely on activation of glucose pathways beyond glycolysis to manage increased glucose supply. We propose that metabolic branching is a universal control mechanism to promote systemic glucose homeostasis in individual tissues. Beyond quantifying fluxes in individual organisms at a time, we extend the reach of metabolic flux analysis by deconvoluting the complex relationship between isotope labeling patterns and fluxes. We train machine learning models by simulating atom transitions across five universal models of central carbon metabolism starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within a feasible flux space to generate the machine learning framework ML-Flux. ML-Flux takes variable measurements of labeling patterns as input to deep-learning-based imputation model and converts the ensuing comprehensive labeling patterns into metabolic fluxes using neural networks. ML-Flux is light software and deployed on the web that obtains fluxes and free energies both more accurately and in less time compared to least-squares method. Our biochemical networks and machine learning models constitute a curated and growing online knowledgebase of metabolic flux and energy to democratize quantitative metabolic analysis and facilitate metabolic engineering. All works compiled within this dissertation explores the key relationships between isotope tracers and fluxes and how we can use these relationships to study metabolism, whether big or small. Thus, through the marriage of innovative experimental design with rigorous computational modeling, we bring about new biological discoveries and scientific techniques to advance our global understanding of biological systems.