Advances in bioinformatics and genome research have generated a rapid expansion in the availability of information at all levels of biological investigation. This informational """"""""revolution"""""""" in biotechnology has resulted in-part from high-throughput technologies to sequence genes and proteins, and to determine their expression patterns under a variety of environmental conditions. Of equal importance to these genetic-based studies is the investigation of cell physiology and metabolism, as metabolic function leading to cell growth is the ultimate output of genetic regulation. However, historically these areas have received comparatively less attention from the perspective of developing methods for rapid data gathering and subsequent analysis and interpretation. This proposal is aimed at developing a combined experimental/in silico simulation platform, using metabolic flux data derived from 13C-label tracing experiments and a constraints-based metabolic model, to analyze and interpret cell physiology at a systems level. We will perform 13C-labeling experiments with chemostat-grown E. coil cultures, and develop the necessary algorithms to calculate intracellular fluxes. We will then compare these measured values with predictions made by existing metabolic models, and finally explore the feasibility of utilizing this data to improve the predictive power of the models. The construction of an integrated experimental/modeling platform for E. coil marks the first step towards extending these strategies to generate predictive models of human cell lines for use in disease research and drug development. In subsequent phases of the project, we intend to generalize these procedures so that they can be applied to any organism, and automate the analysis procedures to provide a software package for commercial use.
| Suthers, Patrick F; Burgard, Anthony P; Dasika, Madhukar S et al. (2007) Metabolic flux elucidation for large-scale models using 13C labeled isotopes. Metab Eng 9:387-405 |
