The target of this proposal is to build a computational model that can simulate a complete life cycle of a single yeast cell, taking into account all of the annotated genes. This has been called ?the ultimate test of understanding a simple cell? and will revolutionize how we view biology, by allowing us to examine interactions between major cellular processes which have never been studied before. We will demonstrate this point, integrating computational modeling and experimental biology, in a detailed study of aging and lifespan control. Several intellectual challenges will have to be addressed to complete the proposed work successfully. For example, a major component of our effort to build a whole-cell model will depend on the development of new modeling approaches, applicable at a large scale. Additionally, methods for the integration of models for different types of biological processes, each of which may be best described using different representation, will be critical. Fortunately, the particular expertise of the P.I. is in modeling a variety of cellular processes, and in particular integrating different network models at the large scale. Our proposed work aims not only to pass the ?ultimate test? of building a whole-cell computer model, but to make it accessible to the scientific community and the general public through a user-friendly web interface. One deliverable of the project will be a web-based platform which will allow the user to track and perturb biological processes such as DNA replication, RNA transcription and regulation, protein synthesis, metabolism and cell division. For scientists, the platform will have advanced user options, so that other researchers can adapt our methods to whichever cell type(s) they prefer. We anticipate that this could lead to a broad implementation of research which uses computational modeling and simulation to guide experimental programs and biological discovery.
| Karr, Jonathan R; Williams, Alex H; Zucker, Jeremy D et al. (2015) Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS Comput Biol 11:e1004096 |
| Carrera, Javier; Covert, Markus W (2015) Why Build Whole-Cell Models? Trends Cell Biol 25:719-722 |
| Regot, Sergi; Hughey, Jacob J; Bajar, Bryce T et al. (2014) High-sensitivity measurements of multiple kinase activities in live single cells. Cell 157:1724-34 |
| Birch, Elsa W; Udell, Madeleine; Covert, Markus W (2014) Incorporation of flexible objectives and time-linked simulation with flux balance analysis. J Theor Biol 345:12-21 |
| Macklin, Derek N; Ruggero, Nicholas A; Covert, Markus W (2014) The future of whole-cell modeling. Curr Opin Biotechnol 28:111-5 |
| Karr, Jonathan R; Phillips, Nolan C; Covert, Markus W (2014) WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database (Oxford) 2014: |
| Karr, Jonathan R; Sanghvi, Jayodita C; Macklin, Derek N et al. (2013) WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res 41:D787-92 |
| Lee, Ruby; Karr, Jonathan R; Covert, Markus W (2013) WholeCellViz: data visualization for whole-cell models. BMC Bioinformatics 14:253 |
| Purcell, Oliver; Jain, Bonny; Karr, Jonathan R et al. (2013) Towards a whole-cell modeling approach for synthetic biology. Chaos 23:025112 |
| Sanghvi, Jayodita C; Regot, Sergi; Carrasco, Silvia et al. (2013) Accelerated discovery via a whole-cell model. Nat Methods 10:1192-5 |
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