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Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by(More)
Cellular behavior in response to stimulatory cues is governed by information encoded within a complex intracellular signaling network. An understanding of how phenotype is determined requires the distributed characterization of signaling processes (e.g., phosphorylation states and kinase activities) in parallel with measures of resulting cell function. We(More)
  • Scott R Floyd, Michael E Pacold, Qiuying Huang, Scott M Clarke, Fred C Lam, Ian G Cannell +18 others
  • 2013
DNA damage activates a signalling network that blocks cell-cycle progression, recruits DNA repair factors and/or triggers senescence or programmed cell death. Alterations in chromatin structure are implicated in the initiation and propagation of the DNA damage response. Here we further investigate the role of chromatin structure in the DNA damage response(More)
Although human epidermal growth factor receptor 2 (HER2) overexpression is implicated in tumor progression for a variety of cancer types, how it dysregulates signaling networks governing cell behavioral functions is poorly understood. To address this problem, we use quantitative mass spectrometry to analyze dynamic effects of HER2 overexpression on(More)
Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms(More)
Systems biology, particularly of mammalian cells, is data starved. However, technologies are now in place to obtain rich data, in a form suitable for model construction and validation, that describes the activities, states and locations of cell-signalling molecules. The key is to use several measurement technologies simultaneously and, recognizing each of(More)
Posttranslational modification of peptide antigens has been shown to alter the ability of T cells to recognize major histocompatibility complex (MHC) class I-restricted peptides. However, the existence and origin of naturally processed phosphorylated peptides presented by MHC class I molecules have not been explored. By using mass spectrometry, significant(More)
Due to its common dysregulation in epithelial-based cancers and extensive characterization of its role in tumor growth, epidermal growth factor receptor (EGFR) is a highly validated target for anticancer therapies. There has been particular interest in the development of monoclonal antibodies (mAbs) targeting EGFR, resulting in two approved mAb-based drugs(More)
Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions.(More)
Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based(More)