In current cellular biology, we often assume that cells operate at a near steady state. Thisassumption implies that each individual cell performs the same processes at any particular moment. However, this assumption proves to be difficult to reconcile with cellular processes that are dynamic and asynchronous, such as the cell cycle, in which each cell has its own clock, or ey signaling processes such as the MAPK and AKT pathways, which are heterogeneous and dynamic from cell to cell. These heterogeneities play an essential role in cell fate decisions, including proliferation and differentiation. Cell signaling information also ‘spreads’ to other pathways and thus creates complex changes in cellular states, including in metabolic flux and gene expression. The connection between cell signaling and cell metabolism raises the question of whether cell metabolism is heterogeneous too. Furthermore, metabolic states could influence how cells respond to growth signaling cues. In the first part of this dissertation, I explore the question of whether cellular metabolism is heterogeneous in cell populations. I utilize a fluorescence-based FRET biosensor to probe AMPK activity when cellular oxidative phosphorylation (OXPHOS) is inhibited. I show that, in fact, at a single-cell level, cells do not utilize OXPHOS equally, and cellular adaptation after OXPHOS inhibition never reaches a steady state. In the second part, I expand the idea of single-cell metabolism and ask how cell metabolism regulates growth signals at the single-cell level. I developed transposase-based transfection systems that would allow expression of up to three fluorescence biosensors in one transfection to achieve this goal. I also developed an unsupervised clustering technique for multidimensional time-series data to analyze more than 300,000 single cell traces. I showed that the signaling activity under metabolic conditions is heterogeneous even in the same type of metabolic stress. However, the signaling landscape is not infinite since there are only about 30 modes ofviii responses. This study characterizes the complex interaction between cell metabolism and cellular growth signals.