Abstract
Building a dynamic model of a complete biological cell is one of the great challenges of the 21st century. While this objective could appear unrealistic until recently, considerable improvements in high-throughput data collection techniques, computational performance, data integration, and modeling approaches now allow us to consider it within reach in the near future. In this chapter, we review recent developments that pave the way toward the construction of genome-scale dynamic models. We first describe methodologies for the integration of heterogeneous “omics” datasets, which enable the interpretation of cellular activity at the genome scale and in fluctuating conditions, providing the necessary input to models. We subsequently discuss principles of such models and describe a series of approaches that open perspectives toward the construction of genome-scale dynamic models.
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Schwartz, JM., Gaugain, C. (2011). Genome-Scale Integrative Data Analysis and Modeling of Dynamic Processes in Yeast. In: Castrillo, J., Oliver, S. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 759. Humana Press. https://doi.org/10.1007/978-1-61779-173-4_24
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DOI: https://doi.org/10.1007/978-1-61779-173-4_24
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