- Biological Engineering
- Metabolic Engineering
- Soil Nitrification Processes
- Chaplen, F.W.R., Perez, J., Bottomley, P., Buchanan, A., Murthy, G.S., Chang, J.H., Sayavedra-Soto, L. 2014. (Accepted) Elucidating the coupled dynamics of the nitrifying bacteria Nitrosomonas europaea and Nitrobacter winogradskyi grown in chemostat co-culture. ACS Natl. Mtg., Dallas, TX, March 2014.
- Perez, J., Buchanan, A., Ferrell, R., Chang, J.H., Chaplen, F.W.R., Bottomley, P., Arp, D., and Sayavedra-Soto, L. 2013. (Accepted) Global transcriptomic analysis and modeling of Nitrosomonas europaea and Nitrobacter winogradskyi grown singly and in co-culture. ICoN3, Tokyo, Japan, September, 2013.
- Chaplen, F. W.R., Buchanan, A., Chang, J. H., & Sayavedra-Soto, L. (2013, April). Constraints-based modeling of the nitrifying bacteria Nitrosomonas europaea and Nitrobacter hamburgensis. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY (Vol. 245). 1155 16TH ST, NW, WASHINGTON, DC 20036 USA: AMER CHEMICAL SOC.
- Javad Azimi, Xiaoli Fern, Alan Fern, Elizabeth Burrows, Frank Chaplen, Yanzhen Fan, Hong Liu, Jun Jiao, Rebecca Schaller. (2010) Myopic Policies for Budgeted Optimization with Constrained Experiments. AAAI Conference on Artificial Intelligence (AAAI-10).
- Burrows, E.H., Wong, W.K., Fern, X., Chaplen, F.W.R. and Ely, R.L. (2009) Optimization of pH and Nitrogen for Enhanced Hydrogen Production by Synechocystis sp. PCC 6803 via Statistical and Machine Learning Methods. Biotech. Prog. 25(4):1009-1017.
Soil Nitrification Processes. Given that most of Earth’s terrestrial environments are being perturbed by humans, it is essential that we gain a better understanding carbon (C) and nitrogen (N) cycling to help ameliorate potential negative effects. The dynamics of coupled microbiological/ physical/chemical systems in the environment may be an important factor in elucidating the biogeochemical anthropogenic impact. Nitrifying bacteria are key elements of the Nitrogen (N) Cycle initiating the movement of fixed inorganic N (ammonia, NH3) to nitrate (NO3‑) in a coupled two-step process. Projects in this area focus on integration of constraints-based models of soil nitrifiers and associated heterotrophic bacteria with dynamic models of the environment combined with transcriptomics and 13C labeling studies. These integrated models can then be used to elucidate the individual and coupled dynamics of selected bacteria to environmental perturbations. Our work suggests that growth in the environment may be driven by complex biochemical responses to relatively simple chemical interactions. This work is funded through the National Science Foundation (CBET 1239870; PIs F. Chaplen and L. Sayavedra-Soto) and the Department of Energy (ER65192; PIs D. Arp, L. Sayavedra-Soto, J. Chang, and P. Bottomley).
Further funding is being sought from the National Science Foundation.
This material is based upon work supported by the National Science Foundation under Grant No. CBET 1239870 and Department of Energy under Grant No. ER65192.
Experimental Planning for Optimization and Experimental Design
(Description adapted from NSF Proposal Abstract, IIS 1320943, A. Fern and X. Fern, PIs) Many engineering and scientific projects require planning experimental activities in order to optimize an objective. Such planning involves jointly reasoning about both the budget-limited resource constraints among activities along with the utility of potential information that they may provide. Unfortunately, for many real-world planning problems, with rich structure among potential activities, tools from classic experimental design are not directly applicable due to their simplifying assumptions and poor scalability. This project aims to transform the practice of experimental planning by developing new algorithms that account for the complexities exhibited in a wide range of domains. A key activity of this project is working with bioengineers to assess and improve the usability of experimental planning tools and producing benchmark problems based on real and simulated bioengineering data. This work is funded through the National Science Foundation (IIS 1320943; PIs A. Fern and X. Fern). This work is also being done in conjunction with H. Liu.
Other work more in the domain of traditional experimental design has focused on the application of machine learning methods for optimizing hydrogen production in the cyanobacterium Synechocystis PCC. 6803. This work was done in conjunction with R. Ely, W.K. Wong and X. Fern.
This material is based upon work supported by the National Science Foundation under Grant No. IIS 1320943, with funds from the Oregon Nanoscience and Microtechnologies Institute (ONAMI)/Department of Defense (ARL-DOD Cooperative Agreement# W911NF-07-2-0083), and by the Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-05ER64130.
Microbial/Surface Interactions and Biofilm Formation
Interactions between microorganisms and materials surfaces can be important components of microbial function. Projects in this area involve the use of nanomodified materials for studying biofilm formation using a variety of model microbial species either singly or in consortia. Key aspects of this work are the use of tools from systems biology and metabolic engineering to elucidate the impact of surface modification through nanotechnology on the mechanisms of biofilm formation. Past funding was through the ARL-ONAMI Center for Nanoarchitectures for Enhanced Performance (PIs F. Chaplen, H. Liu, J. Jiao, and A. Fern).
Future funding for this area is being sought from the National Science Foundation and the Department of Energy.
1. Xu, S., Fan, Y., Schaller, R., Jiao, J., Liu, H. and Chaplen, F.W.R. (2012) Enhanced performance and study of microbial electrolysis cells using Fe nanoparticle-decorated anodes. Applied Microbiology and Biotechnology. 93:871-880.
2. Fan, Y., Xu, S., Schaller, R., Jiao, J., Chaplen, F.W.R., Fern, A. and Liu, H. (2011) Nanoparticle decorated anodes for enhanced generation in microbial electrochemical cells. Biosensors and Bioelectronics.26:1908-1912.
This material is based upon work supported by funds from the Oregon Nanoscience and Microtechnologies Institute (ONAMI)/Department of Defense (ARL-DOD Cooperative Agreement# W911NF-07-2-0083).