Research highlights: microfluidics meets big data.

In this issue we highlight a collection of recent work in which microfluidic parallelization and automation have been employed to address the increasing need for large amounts of quantitative data concerning cellular function--from correlating microRNA levels to protein expression, increasing the throughput and reducing the noise when studying protein dynamics in single-cells, and understanding how signal dynamics encodes information. The painstaking dissection of cellular pathways one protein at a time appears to be coming to an end, leading to more rapid discoveries which will inevitably translate to better cellular control--in producing useful gene products and treating disease at the individual cell level. From these studies it is also clear that development of large scale mutant or fusion libraries, automation of microscopy, image analysis, and data extraction will be key components as microfluidics contributes its strengths to aid systems biology moving forward.