(314g) Comprehensive Network-Based Dynamic Metabolic Model for Shewanella Oneidensis

Authors: 
Song, H., Purdue University
Geng, J., Purdue University
Ramkrishna, D., Purdue University
Pinchuk, G. E., Pacific Northwest National Laboratory
Beliaev, A. S., Pacific Northwest National Laboratory
Konopka, A. E., Pacific Northwest National Laboratory

Shewanella oneidensis draws attention as a potential solution for bioremediation due to its ability to convert metals to an altered state which can break up in the environment. In their growth on carbon substrates such as lactate, pyruvate and acetate, species of the genus Shewanella utilize a wide range of electron acceptors including O2, fumarate, Fe(III), Mn(VI), etc. Towards a comprehensive understanding of metabolism of S. oneidensis MR-1, we develop a dynamic metabolic model for its aerobic growth. The formulated model is based on a detailed metabolic network within the lumped hybrid cybernetic modeling (L-HCM) framework (Song and Ramkrishna 2010, 2011). The L-HCM views the total uptake flux as distributed in a regulated way among lumped elementary modes (L-EMs) so as to maximize a prescribed metabolic objective such as growth or uptake rate. L-EM is computed as a weighted average of EMs where the weights are basically related to the yields of vital products (i.e., biomass and ATP) associated with individual EMs, but can be further tuned using experimental yield data. The model shows excellent agreement with dynamic experimental data collected at the Pacific Northwest National Laboratory (PNNL), as well as data available in the literature (Tang et al., 2007).  The effectiveness of L-HCM lies in the notably smaller number of parameters, which is attributable to the rational lumping of EMs and its accounting for dynamic regulation among L-EMs through the cybernetic control laws. The present work is being extended to further explore anaerobic and oxygen-limited growth with particular interest in the metabolic shift between anaerobic and aerobic growth.

References:

Song HS, Ramkrishna D. 2010. Prediction of Metabolic Function From Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and Bioengineering 106(2):271-284.

Song HS, Ramkrishna D. 2011. Cybernetic Models Based on Lumped Elementary Modes Accurately Predict Strain-Specific Metabolic Function. Biotechnology and Bioengineering 108(1):127-140.

Tang YJJ, Meadows AL, Keasling JD. 2007. A kinetic model describing Shewanella oneidensis MR-1 growth, substrate consumption, and product secretion. Biotechnology and Bioengineering 96(1):125-133.