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Bahar, I.

Publications and source records attributed to Bahar, I..

2 recordsLinked to original sources

Quantitative Assessment Of Cell Fate Decision Between Autophagy And Apoptosis

Autophagy and apoptosis regulate cell survival and death, and are implicated in the pathogenesis of many diseases. The same type of stress signals can induce either process, but it is unclear how cells assess cellular damage and make a life or death decision by activating autophagy or apoptosis. A computational model of coupled apoptosis and autophagy is built here to study the systems-level dynamics of the underlying signaling network. The model explains the differential dynamics of autophagy and apoptosis in response to various experimental stress signals. Autophagic response dominates at low-to-moderate stress; whereas the response shifts from autophagy (graded activation) to apoptosis (switch-like activation) with increasing intensity of stress. The model reveals that this dynamic cell fate decision is conferred by a core regulatory network involving cytoplasmic Ca2+ as a rheostat that fine-tunes autophagic and apoptotic responses. A G-protein signaling-mediated feedback loop maintains cytoplasmic Ca2+ level, which in turn governs autophagic response through an AMP-activated protein kinase (AMPK)-mediated feedforward loop. The model identified Ca2+/calmodulin-dependent kinase kinase {beta} (CaMKK{beta}) as a determinant of the opposite roles of cytoplasmic Ca2+ in autophagy regulation. The results also demonstrated that the model could contribute to the development of pharmacological strategies modulate cell fate decisions.

systems biology

Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

Understanding the three-dimensional (3D) architecture of the chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to have a glimpse into the 3D genome organization and identify topologically associated domains (TADs), but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (> 50 megabases) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to the task of modeling chromatin dynamics using Hi-C contact data. We show that the GNM can identify structural dynamics at multiple scales: it can quantify the fluctuations in the positions of gene loci, find large genomic compartments and smaller TADs that undergo en-bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with DNase-seq and ATAC-seq measurements on accessibility, the previously identified A and B compartments of chromatin structure, and pairs of interacting loci identified by ChIA-PET. We describe a method to use the GNM to identify novel cross-correlated distal domains (CCDDs) representing regions of long-range dynamic coupling and show that CCDDs are often associated with increased gene coexpression using a large-scale analysis of 212 expression experiments. Together, these results show that GNM provides a mathematically well-founded unified framework for assessing chromatin dynamics and the structural basis of genome-wide observations.

genomics