Parag Mallick
Academic Appointments
- Assistant Professor (Research), Radiology - Diagnostic Radiology
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- Academic Offices
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Professional Overview
Professional Education
| PostDoc: | Institute for Systems Biology, Proteomics & Systems Biology |
| Ph.D.: | University of California, Los Angeles, Chemistry & Biochemistry |
| B.S.: | Washington University in St. Louis, Computer Science & Biochemistry |
Graduate & Fellowship Program Affiliations
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Industry Relationships
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Scientific Focus
Current Research Interests
Our general hope is to apply systems biology's complimentary computational and experimental methods in hopes that experimental results motivate large-scale computational studies, which initiate new experimental explorations. We hope this synergistic combination will provide insight into the relationship between molecular phenomena and organismic phenomena.
Organismic states, such as healthy and diseased, are hypothesized to arise from the alteration of a systems normal cell-network structures through a combination of endogenous genetic modifications and exogenous environmental agents. Whole-cell and whole-organism analyses of gene expression, protein expression and related differential analyses have been widely applied to study biological processes and disease states. These techniques have permitted the examination of cellular processes and their relationship to physiologic effects in a greater detail than previously possible, enabling better characterizations of pathologic states, such as cancer.
As our understanding of cellular processes has developed, so has our understanding that cancer, even in a single patient, is not one disease, but instead hundreds of heterogeneous diseases unified by the single common gross phenotype of de-regulation of cell-growth. Consequently, diagnosis becomes a complex challenge it is insufficient to merely determine that someone has cancer; instead a clinician must have the ability to concurrently determine what the likely behavior of that cancer will be and answer questions regarding its aggressivity, likely outcome, response to therapy, and evolution.
One area of inquiry specifically missing from the canon of marker discovery background is a formal description of the fundamental propagation of signals from molecular and cellular length scales to tumor and organismic length scales. For example, when a cell undergoes initiation and transformation what signals is it relaying to its nearby environment and how do those signals affect the broader system? Furthemore, it is often assumed that a linear increase in a protein's abundance in a tumor will lead to an equivalent linear increase in a protein's abundance in the circulation (and this appears to be true for some proteins). This general assumption neglects important effects such as a linear increase in a protein in a tumor leading nearby tissues to exponentially decrease their production of that protein for a net loss of protein in the circulation. Lastly, fundamental mechanical transport phenomena of how proteins traverse tumors into the circulation as a function of geometry and proximity to vasculature have yet to be well characterized. These sorts of basic questions are a critical foundation for all cancer marker discovery questions and something our group has been investigating.
Our group has also been leading the development of ProteoWizard, an open source set of libraries and tools to simplify the process of developing proteomics tools. They read and write the HUPO-PSI mzML standard and have been incorporated into the ISB's transproteomicpipeline!
Publications
- A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012; (10): 918-20
- Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition. Mol Cancer Ther. 2012; (5): 1071-81
- Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer. Mol Pharm. 2011; (6): 2069-79
- Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma. PLoS One. 2011; (7): e23090
- Proteomics: a pragmatic perspective. Nat Biotechnol. 2010; (7): 695-709
- Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol. 2007; (1): 125-31
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