The advent of DNA-microarrays spurred a vigorous effort to reverse engineer biological networks. Recently, these efforts have been reinvigorated by the availability of RNA-seq data from perturbed and unperturbed single cells. I will discuss the opportunities and limitations of using such data for inferring networks of direct causal interactions, with emphasis on the distinctions between models based on direct and indirect interactions. This discussion motivates the need to model proteins since most biological interactions involve proteins. Then I will introduce key ideas and technological capabilities of high-throughput single-cell proteomics methods that we have developed and will focus on the opportunities of using such data for inferring direct causal mechanisms in biological systems.