Biomedical subjects
Publications and source records attributed to Dale Muzzey.
Positive feedback is a ubiquitous feature of networks that establish and maintain cellular decisions. In this issue of Cell, Laslo et al. (2006) demonstrate how a feedback loop comprised of two mutual repressors regulates the differentiation of myeloid progenitors into either macrophages or neutrophils.
Fluctuations in protein numbers (noise) due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the population average. Recently, stochastic models have been used to predict distributions of steady-state protein levels within a population but not to predict the dynamic, presteady-state distributions. In the present work, we experimentally examine a system whose dynamics are heavily influenced by stochastic effects. We measure population distributions of protein numbers as a function of time in the Escherichia coli lactose uptake network (lac operon). We then introduce a dynamic stochastic model and show that prediction of dynamic distributions requires only a few noise parameters in addition to the rates that characterize a deterministic model. Whereas the deterministic model cannot fully capture the observed behavior, our stochastic model correctly predicts the experimental dynamics without any fit parameters. Our results provide a proof of principle for the possibility of faithfully predicting dynamic population distributions from deterministic models supplemented by a stochastic component that captures the major noise sources.
We developed a new DNA microarray-based technology, called protein binding microarrays (PBMs), that allows rapid, high-throughput characterization of the in vitro DNA binding-site sequence specificities of transcription factors in a single day. Using PBMs, we identified the DNA binding-site sequence specificities of the yeast transcription factors Abf1, Rap1 and Mig1. Comparison of these proteins' in vitro binding sites with their in vivo binding sites indicates that PBM-derived sequence specificities can accurately reflect in vivo DNA sequence specificities. In addition to previously identified targets, Abf1, Rap1 and Mig1 bound to 107, 90 and 75 putative new target intergenic regions, respectively, many of which were upstream of previously uncharacterized open reading frames. Comparative sequence analysis indicated that many of these newly identified sites are highly conserved across five sequenced sensu stricto yeast species and, therefore, are probably functional in vivo binding sites that may be used in a condition-specific manner. Similar PBM experiments should be useful in identifying new cis regulatory elements and transcriptional regulatory networks in various genomes.