Doctor of Philosophy, Hebrew University Of Jerusalem (2010)
Tobias Meyer, Postdoctoral Faculty Sponsor
View details for DOI 10.1038/nmeth.2729
The regulation of cellular protein levels is a complex process involving many regulatory mechanisms, each introducing stochastic events, leading to variability of protein levels between isogenic cells. Previous studies have shown that perturbing genes involved in transcription regulation affects the amount of cell-to-cell variability in protein levels, but to date there has been no systematic characterization of variability in expression as a phenotype. In this research, we use single-cell expression levels of two fluorescent reporters driven by two different promoters under a wide range of genetic perturbations in Saccharomyces cerevisiae, to identify proteins that affect variability in the expression of these reporters. We introduce computational methodology to determine the variability caused by each perturbation and distinguish between global variability, which affects both reporters in a coordinated manner (e.g., due to cell size variability), and local variability, which affects the individual reporters independently (e.g., due to stochastic events in transcription initiation). Classifying genes by their variability phenotype (the effect of their deletion on reporter variability) identifies functionally coherent groups, which broadly correlate with the different stages of transcriptional regulation. Specifically, we find that most processes whose perturbation affects global variability are related to protein synthesis, protein transport, and cell morphology, whereas most processes whose perturbations affect local variability are related to DNA maintenance, chromatin regulation, and RNA synthesis. Moreover, we demonstrate that the variability phenotypes of different protein complexes provide insights into their cellular functions. Our results establish the utility of variability phenotype for dissecting the regulatory mechanisms involved in gene expression.
View details for DOI 10.1073/pnas.1013148108
View details for Web of Science ID 000289413600077
View details for PubMedID 21444810
The activity in the living cell is carried out by a myriad network of interactions between macromolecules. These include interactions between proteins that form a functional complex, a protein modifying another protein in a transient interaction, a transcription factor that binds a specific DNA locus triggering a change in chromatin or transcription, and so on. Characterization of these interactions in terms of timing, context, and function is crucial for understanding how cells carry out basic biological processes. The recent years have led to the introduction of many assays for probing these interactions in a systematic and large-scale manner. However, there is a large gap between assay results and understanding of biological systems. The challenge for computational methods is to bridge this gap by combining results of different assays and introducing statistical methodologies. In this review we discuss recent advances in approaches dealing with these challenges, and key directions for the future.
View details for DOI 10.1016/j.copbio.2010.10.017
View details for Web of Science ID 000287840700013
View details for PubMedID 21109421
Genetic interactions between genes reflect functional relationships caused by a wide range of molecular mechanisms. Large-scale genetic interaction assays lead to a wealth of information about the functional relations between genes. However, the vast number of observed interactions, along with experimental noise, makes the interpretation of such assays a major challenge.Here, we introduce a computational approach to organize genetic interactions and show that the bulk of observed interactions can be organized in a hierarchy of modules. Revealing this organization enables insights into the function of cellular machineries and highlights global properties of interaction maps. To gain further insight into the nature of these interactions, we integrated data from genetic screens under a wide range of conditions to reveal that more than a third of observed aggravating (i.e. synthetic sick/lethal) interactions are unidirectional, where one gene can buffer the effects of perturbing another gene but not vice versa. Furthermore, most modules of genes that have multiple aggravating interactions were found to be involved in such unidirectional interactions. We demonstrate that the identification of external stimuli that mimic the effect of specific gene knockouts provides insights into the role of individual modules in maintaining cellular integrity.We designed a freely accessible web tool that includes all our findings, and is specifically intended to allow effective browsing of our results (http://compbio.cs.huji.ac.il/GIAnalysis).Supplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btq197
View details for Web of Science ID 000278689000028
View details for PubMedID 20529911
The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5-10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions. We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.
View details for DOI 10.1093/bioinformatics/btn151
View details for Web of Science ID 000257169700038
View details for PubMedID 18586706
Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.
View details for Web of Science ID 000236954700003
View details for PubMedID 16597232