Additionally, predictions made using computational models facilitate directed experiments. Computational models confirm intricacies in cellular network behavior that are difficult to elucidate through observation alone. Additionally, the development of complex computational algorithms can dissect the dynamics and distribution of single-cell behavior found in the complicated and rich datasets produced by single-cell measurements. Observing cells at the single-cell level is now possible using better fluorescent biosensors and single-cell analysis techniques. However, single-cell studies continue to utilize the same overarching approach as traditional population level studies ( Figure 1). The ability to fully comprehend signal transduction at the single-cell level requires advancements in how we observe cells, model cellular behavior, and manipulate biological systems. The implications of cellular heterogeneity is a key area of research important to understanding fundamental issues such as variability of drug response at the cellular and organism level. Furthermore, single-cell information exposes the complexity of cellular response distribution. Single-cell studies have uncovered the importance of temporal dynamics in information transmission through cellular signaling networks. Similarly, single-cell studies of the NF-κB pathway show complex digital activation patterns in individual cells that are hidden by population level studies. Analysis based solely on population level readouts completely masked these insights. More recent work on this pathway elucidates the complex dynamic patterns of p53 activities that cells use to encode and transmit information. Instead, differences in pulse cycle between cells created a population average behavior showing damped oscillations. However, single-cell studies revealed that there are no damped oscillations in the individual cell. For example, earlier studies of the p53 signaling pathway provided support for damped oscillations to stimulus. These new studies often reveal that previous knowledge about signaling dynamics at the population level may not be the complete picture. Over the last few years, technological advancements in measuring, manipulating, and modeling signaling activities at single-cell resolution has enabled researchers to go beyond the limitations of population-averaged bulk assays. Furthermore, population averaging results in information loss regarding biological variability that often has important physiological implications. Although these studies have been useful in mapping signaling pathways and making qualitative predictions, population averaging can often mask the spatial and temporal dynamics important in physiological processes. Traditional biological approaches to study cellular signal transduction include bulk assays to measure cellular response at the population level. Defining cellular signaling pathways is important to understand many biological processes including tissue development, immune response, cancer development, cellular growth and migration, and more.

Signaling transduction flaticon