(194ad) Application of the Genome-Scale Modeling Approach to Exoelectrogenic Microorganisms in Microbial Fuel Cells

Microbial fuel cells (MFCs) use microogranisms to oxidize a substrate and produce electrical current. They are of growing interest as sources of renewable energy and for wastewater remediation. However, the application of MFCs is limited due to low power output and high material and designs costs. Research efforts to address these limitations have been primarily focused on experimental work, but there has been increasing interest in modeling and simulation [1]. MFCs have been modeled using kinetic models and genome-scale metabolic models. Kinetic models are used to model the dynamic changes and are based on the reaction kinetics and mass balance equations of the system. The resulting ordinary or partial differential equations are solved using numerical methods with boundary conditions based on the inputs to the system and the history [2]. There are numerous examples of kinetic models in the literature and a comprehensive review of kinetic models has been recently performed [1].

Genome-scale metabolic models are used to model the fluxes through the metabolic reactions in a system. A metabolic model is based on the genome annotation of the microorganism and the construction of the metabolic network. Metabolic models are analyzed using flux balance analysis based on the reaction stoichiometry [3]. In this work, a comprehensive review of the current state of genome-scale metabolic models is performed. Several different exoelectrogenic microorganisms have been identified. In particular, Geobacteraceae are most commonly studied and are well-suited for use in MFCs [4]. Two recent works have developed genome-scale models based on the microorganisms Geobacter sulfurreducens and Geobacter metallireducens [5][6]. Both works have further advanced previously developed models based on updates to the genome annotations for the microorganisms and the authors present results. The 2013 G. sulfurreducens model predicts the maximum theoretical power output to be 2.716 W/gDW [5]. The 2014 G. metallireducens model does not directly compute power output, instead it calculates electron donor and acceptor uptake rates, which are related to power output. In addition, the 2014 model identified a new culturing condition with formate as the electron donor, which was subsequently verified experimentally.

This new study has two objectives. First, a comprehensive review was performed of metabolic models from earlier works to the present with a focus on the two recent G. sulfurreducens and G. metallireducens models. The general process by which genome-scale metabolic models are developed is summarized and the processes by which the two recent models were developed based on new genome annotations for G. sulfurreducens and G. metallireducens are reviewed. Second, this study further analyzed the two recent models by running additional simulations in MATLAB to further evaluate two key questions: 1) what are the culturing conditions to maximize current output in MFCs based on the two microorganisms and 2) what are the best metabolic strategies to maximize current output. To evaluate the first question, the critical nutrients were identified for each microorganism based on the models. Phase diagrams have been generated for growth rates and fluxes through reactions related to current output as a function of critical nutrients. For example, for the G. sulfurreducens model, growth rate and fluxes through key reactions are shown to be linearly proportional to acetate uptake rate. To evaluate the second question, gene knockout analysis has been performed. For example, for the G. metallireducens model, 390 essential genes were identified and strategies for perturbing the metabolism, such as targeting genes for overexpression, were evaluated to optimize growth rate and fluxes through key reactions. Finally, a detailed comparison of the 2013 G. sulfurreducens model and the 2014 G. metallireducens model was conducted.

References:

[1] V.M. Ortiz-Martinez, M.J. Salar-Garcia, A.P. de los Rios, F.J. Hernandez-Fernandez, J.A. Egea, L.J. Lozano, "Developments in Microbial Fuel Cell Modeling," Chemical Engineering Journal, vol. 271, pp. 50-60, 2015. [2] J. Almquist, M. Cvijovic, V. Hatzimanikatis, J. Nielsen, M. Jirstrand, "Kinetic Models in Industrial Biotechnology - Improving Cell Factory Performance," Metabolic Engineering, vol. 24, pp. 38-60, 2014.

[3] J. Orth, I. Thiele, B. Palsson, "What is Flux Balance Analysis," Nature Biotechnology, vol. 28, no. 3, pp. 245-248, March 2010.

[4] D. Lovley, "The Microbe Electric: Conversion of Organic Matter to Electricity," Current Opinion in Biotechnology, vol. 19, pp. 1-8, 2008.

[5] L. Mao, W. Verwoerd, "Model-Driven Elucidation of the Inherent Capacity of the Geobacter Sulfurreducens for Electricity Generation," Journal of Biological Engineering, vol. 7, no. 14, pp. 1-21, 2013.

[6] A. Feist, H. Nagarajan, A. Rotaru, P. Tremblay, T. Zhang, K. Nevin, D. Lovley, K. Zenger, "Constraint-Based Modeling of Carbon Fixation of Electron Transfer in Geobacter Metallireducens," PLOS Computational Biology, vol. 10, issue 4, pp. 1-10, April 2014