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Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling(More)
The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks(More)
VOLUME 33 NUMBER 4 APRIL 2015 NATURE BIOTECHNOLOGY node j have changed, but other indirectly affected nodes also change because the initial perturbation has propagated through the network. MRA has solved the problem of finding local, direct links between components through the global responses for networks of any size and complexity2. The development and(More)
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses(More)
Currently there is intense interest in using mesenchymal stem cells (MSC) for therapeutic interventions in many diseases and conditions. To accelerate the therapeutic use of stem cells we must understand how they sense their environment. Primary cilia are an extracellular sensory organelle present on most growth arrested cells that transduce information(More)
Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has(More)
Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for(More)
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian(More)
p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and(More)
One of the primary mechanisms of signal transduction in cells is protein phosphorylation. Upon ligand stimulation a series of phosphorylation events take place which eventually lead to transcription. Different sets of phosphorylation events take place due to different stimulating ligands in different types of cells. Knowledge of these phosphorylation events(More)