Deep reinforcement learning for the control of microbial co-cultures in bioreactors
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pcbi.1007783
Download full text from publisher
References listed on IDEAS
- Jason Karslake & Jeff Maltas & Peter Brumm & Kevin B Wood, 2016. "Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-21, October.
- Jeff Maltas & Kevin B Wood, 2019. "Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-34, October.
- Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
- M. Omar Din & Tal Danino & Arthur Prindle & Matt Skalak & Jangir Selimkhanov & Kaitlin Allen & Ellixis Julio & Eta Atolia & Lev S. Tsimring & Sangeeta N. Bhatia & Jeff Hasty, 2016. "Synchronized cycles of bacterial lysis for in vivo delivery," Nature, Nature, vol. 536(7614), pages 81-85, August.
- Stefan A Hoffmann & Christian Wohltat & Kristian M Müller & Katja M Arndt, 2017. "A user-friendly, low-cost turbidostat with versatile growth rate estimation based on an extended Kalman filter," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Héctor Rodríguez-Rángel & Dulce María Arias & Luis Alberto Morales-Rosales & Victor Gonzalez-Huitron & Mario Valenzuela Partida & Joan García, 2022. "Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems," Energies, MDPI, vol. 15(7), pages 1-18, March.
- Ajaykumar Unagar & Yuan Tian & Manuel Arias Chao & Olga Fink, 2021. "Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning," Energies, MDPI, vol. 14(5), pages 1-12, March.
- Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
- Hua Zheng & Wei Xie & Ilya O. Ryzhov & Dongming Xie, 2023. "Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 66-82, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
- Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
- Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
- Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018.
"Cooperating with machines,"
Nature Communications, Nature, vol. 9(1), pages 1-12, December.
- Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
- Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
- Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
- Yuhao Chen & Meng Du & Zhen Yuan & Zhiyi Chen & Fei Yan, 2022. "Spatiotemporal control of engineered bacteria to express interferon-γ by focused ultrasound for tumor immunotherapy," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
- Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
- Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
- Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
- Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
- Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
- Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
- Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
- Zichen Lu & Ying Yan, 2024. "Temperature Control of Fuel Cell Based on PEI-DDPG," Energies, MDPI, vol. 17(7), pages 1-19, April.
- Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
- Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
- Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
- Parvez Farazi, Nahid & Zou, Bo & Tulabandhula, Theja, 2022. "Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
- Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
- Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
- Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007783. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.