A requisite to understand the general principles underlying transcriptional regulation is to define its most important players (i.e. transcription factors and cofactors) quantitatively in absolute terms. Such global and quantitative knowledge is necessary to define the range of possibilities (i.e. the general context) within which TFs perform their functions from interacting to forming complexes, to being recruited to specific genes in specific nuclear environment. Furthermore, competition between TFs (for binding sites) is thought to be an important mechanism for driving cellular differentiation with changes in the stoichiometry between LS-TFs being considered as the central mechanism underlying cell fate choice.
 
We measure RNA-seq (transcriptomic) expression and SRM (proteomic) of a time series hematopietic stem cells differentiation into red blood cells. To study the data, first we perform a bioinformatics analysis of our samples to identify the most relevant genes. Second, we build a Gene Regulatory Network (GRN) of those genes based on previous knowledge and correlations in our time series. Third, we transform that GRN into a system of Ordinary Differential Equations to obtain a quantitative model. Fourth, we perform some knock-down experiments to validate our model. Our results show new interactions between genes and an accurate quantitative description of red blood cell development.