(141e) The Human Platelet Reconstruction: Understanding Mechanistic Outcomes From Proteomics, Metabolomics, and Fluxomics

Systems biology catalogues knowledge in a framework which enables mechanistic insights to the causes and effects of resulting biological phenotypes, allowing for the contextualization of datasets which subsequently promote the predictive capabilities of biological models. Constraints based methods quantitatively model biochemical reactions while annotating associations which influence the intracellular state of fluxes through pathways, and have been applied to discover antibiotics, propose metabolic engineering strategies, and understand principles in basic biology. With the reconstruction of human metabolism [1, 2], constraints based methods have been applied to cell-specific models, however, properly modeling human metabolism generally requires a metabolically active cell-type and the annotation of complex, altruistic, non-metabolic processes such as signaling, transcriptional regulation, and cell-to-cell community interactions. Due to these complexities, modeling simpler, metabolically-focused human cell types presents an opportunity to build mass balance models that can incrementally incorporate other modeling paradigms. Blood platelets represent one such system. Platelets are enucleated, have simple cellular fates, and are metabolically active. Signaling pathways associated with thrombosis are shallow with known mechanistic outcomes. In addition, reconstructing the metabolism of these blood cells offer opportunities for applications to improve quality of blood transfusions. Therefore, since these processes have not been characterized at a cell-wide-level, a biochemical reaction network of platelet metabolism was constructed using evidence from 33 proteomic datasets and over 300 literature references. The flux balance analysis model was built using a modified model building algorithm [3] with map-driven gap filling. Functional verification of the in vivo energy pathways was performed with isotopomer labeling data [4]. The data were mapped onto the model employing a parsimonious sampling algorithm which samples possible media uptake and secretion choices to define a minimal media for in vivo energy sources. The effects of SNPs, drugs, and phosphorylation events on the biochemical state of the platelet were enumerated. In addition to characterizing intracellular flux states of platelets in vivo, this study puts forth a pipeline and set of computational methods to reconstruct and study human cells in the context of proteomics and isotopomer data.

[1] Duarte, Natalie C., et al. "Global reconstruction of the human metabolic network based on genomic and bibliomic data." Proceedings of the National Academy of Sciences 104.6 (2007): 1777-1782.

[2] Thiele, Ines, et al. "A community-driven global reconstruction of human metabolism." Nature biotechnology (2013).

[3] Livnat Jerby, Tomer Shlomi, and Eytan Ruppin. "Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism."Molecular systems biology 6.1 (2010).

[4] Guppy, Michael, et al. "Fuel choices by human platelets in human plasma."European Journal of Biochemistry 244.1 (1997): 161-167.