Article
Computer Science, Artificial Intelligence
Hong-Xiang Hu, Changyun Wen, Guanghui Wen
Summary: This article investigates a swarming behavior problem for heterogeneous uncertain agents with cooperation-competition interactions, proposing a distributed Lyapunov-based redesign approach for achieving distributed stabilization and output bipartite consensus in structurally unbalanced and balanced networks, respectively. The concept of coherent networks is introduced for structurally unbalanced directed networks to facilitate the design of distributed controllers, with four illustrative examples demonstrating the effectiveness of the designed distributed controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Yewen Deng, Na Li, Nan Kong, Zhibin Jiang, Xiaoqing Xie
Summary: Shortage of healthcare capacity and rapid urbanization pose challenges to healthcare access in many countries. Expanding total capacity and redistributing capacity spatially can address these challenges. This research focuses on locating new hospitals and upgrading existing hospitals in a two-tier outpatient care system. The study formulates the problem with a discrete location optimization model and uses multinomial logit model and queueing network model to parameterize the optimization model. A multi-fidelity optimization approach is proposed to solve the hard nonlinear combinatorial optimization problem. Real-world case studies in Shanghai demonstrate the changes in the care network and assess the impacts on network design by considering various factors.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Zoubeir Mlika, Soumaya Cherkaoui
Summary: The Internet of Vehicles (IoV) in the future 5G wireless ecosystem requires key technologies such as Mobile Edge Computing (MEC) and Network Slicing to optimize resource allocation. This article introduces a model-free approach based on deep reinforcement learning (DRL) to address resource allocation in MEC-enabled IoV networks, using non-orthogonal multiple access (NOMA) for better utilization of channel resources. The study shows that the proposed approach is robust and effective compared to traditional methods under various network conditions.
Article
Green & Sustainable Science & Technology
Branislav Boskovic, Mirjana Bugarinovic, Gordana Savic, Djuricic Ratko
Summary: This study aims to address the issues existing in the design of track access charges for European railways by balancing the requirements of stakeholders to ensure continuity in the redesign of the model, and proposes a method for producing a sustainable long-term model. The results of the study highlight the importance of continual enhancement in TAC model development as a key precursor for harmonizing stakeholders’ requirements.
Article
Management
Shaonan Liu, Nan Kong, Pratik Parikh, Mingzheng Wang
Summary: Trauma is a significant global health problem, with trauma centers mainly concentrated in urban areas and lacking in rural areas. The uneven distribution of trauma centers leads to system-related mistriage errors. To address this issue, a bilevel integer programming model is proposed to investigate the subsidized trauma care network redesign problem, where the government is the leader and the hospital group is the follower. By utilizing a branching idea, additional infeasible and suboptimal solutions are excluded, speeding up the branch-and-bound algorithm. In a case study, a trauma care network in the midwestern area of the U.S. is redesigned based on closed-form approximate functions of system-related mistriage errors, resulting in a slight reduction in the number of trauma centers and an overall improvement of about 11% compared to the original network.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Biotechnology & Applied Microbiology
Maciek R. Antoniewicz
Summary: Metabolic engineering aims to improve biological production by designing and optimizing metabolic pathways, quantifying metabolic fluxes, and analyzing metabolic network models; essential concepts include metabolic network models and metabolic fluxes; in recent years, advancements in technology and methods have allowed for a deeper understanding of the capabilities of biological systems.
METABOLIC ENGINEERING
(2021)
Article
Engineering, Environmental
Xiao-Gang Wang, Xing-Chao Lin, Xing-Song Sun
Summary: In this paper, an upper bound limit analysis method for 3D slope stability analysis based on rigid block structure is proposed. The method offers a solution to the difficulty of solving the highly nonlinear optimization model with a large number of degrees of freedom and has been validated through numerical examples.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Mohammad Moshref-Javadi, Ahmad Hemmati, Matthias Winkenbach
Summary: This paper conducts a comparative analysis of three synchronized truck-and-drone delivery models for package delivery, demonstrating that higher levels of synchronization considerably reduce customer waiting times. The results from case studies show more than 60% reduction in customer waiting time compared to a truck-only scenario.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Biotechnology & Applied Microbiology
Eva-H. Dulf, Dan C. Vodnar, Alex Danku, Adrian Gheorghe Martau, Bernadette-Emoke Teleky, Francisc V. Dulf, Mohamed Fawzy Ramadan, Ovidiu Crisan
Summary: This study aims to optimize the fermentation process of soy and wheat flour mixtures using single cultures and co-cultures of Lactobacillus plantarum and Lactobacillus casei. Mathematical modeling, including multiple regression and artificial neural networks, is used to track the growth kinetics and viability of the lactic acid bacteria, with the objective of finding a model with optimal performances. The obtained models are validated by comparing the simulation results with experimental data.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Operations Research & Management Science
Are Denstad, Einar Ulsund, Marielle Christiansen, Lars Magnus Hvattum, Gregorio Tirado
Summary: The banking industry is facing various challenges due to regulatory changes and technological advancements; the redesign of ATM networks to adapt to the increased use of electronic payment methods is crucial for cost reduction and network performance.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Chemistry, Applied
Botian Hao, Donghai Xu, Ya Wei, Yunfei Diao, Le Yang, Liangliang Fan, Yang Guo
Summary: Hydrothermal liquefaction offers a direct method for utilizing biomass resources, while mathematical modeling has proven to be an effective tool for research, development, and optimization of the process technology. This paper systematically describes four mathematical models (empirical, response surface, kinetic, and machine learning) and summarizes their construction and research development history in hydrothermal liquefaction. Particularly, machine learning has recently been introduced and shows great potential in biomass hydrothermal liquefaction. The provided information can assist in optimizing biocrude yield and quality, aid in reactor design, and facilitate the industrialization and commercialization of hydrothermal liquefaction technology at a low research cost.
FUEL PROCESSING TECHNOLOGY
(2023)
Article
Thermodynamics
Yujian Gong, Zuo Wang, Zeyu Lai, Minlin Jiang
Summary: The time-varying acceleration coefficients particle swarm optimization method is used to extract the physical parameters of the single diode PV model, which shows good accuracy in most ranges. The introduction of the time-varying mechanism helps mitigate premature convergence issues and establish a proper balance between exploratory and exploitative capabilities.
Review
Biochemistry & Molecular Biology
Yang Cheng, Xinyu Bi, Yameng Xu, Yanfeng Liu, Jianghua Li, Guocheng Du, Xueqin Lv, Long Liu
Summary: Optimizing metabolic pathways in microbial cell factories is crucial for efficient biotechnological production processes. Machine learning has been used to build data-driven models and accelerate development in areas such as genetic engineering and pathway optimization. This review discusses recent applications of machine learning in genome-scale metabolic model construction, enzyme engineering, and gene regulatory element design, as well as the limitations and potential solutions to these methods.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Management
Mehrdad Mohammadi, Milad Dehghan, Amir Pirayesh, Alexandre Dolgui
Summary: This paper develops an approach to optimize the design of a vaccine distribution network with the objective of minimizing the total expected number of deaths and distribution cost. The study finds that current vaccination strategies are not optimal and the prioritization and equity of vaccine distribution depend on factors beyond health policymakers' considerations.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
Kashif Iqbal, Wei Jiang, Rui Ma, Chun Deng
Summary: Previous industrial water networks lack clarity on freshwater sources and neglect seasonal flow rate fluctuations. This study proposes a mathematical model to optimize the synthesis of an integrated water network, including multiple water resources and pretreatment technologies. Through implementation in a coastal oil refinery, the model successfully reduces freshwater demand and lowers the total annual cost for certain water resources.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Plant Sciences
Niaz Bahar Chowdhury, Wheaton L. Schroeder, Debolina Sarkar, Nardjis Amiour, Isabelle Quillere, Bertrand Hirel, Costas D. Maranas, Rajib Saha
Summary: The genome-scale metabolic model for maize roots accurately predicts metabolic reprogramming under nitrogen stress conditions and identifies key metabolites regulating root biomass growth. Additionally, it reveals specific phosphatidylcholine and phosphatidylglycerol metabolites playing a crucial role in increased biomass production under nitrogen-deficient conditions. This integrated model shows promise as a tool for analyzing stress conditions in maize roots and engineering stress-tolerant maize genotypes.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Biotechnology & Applied Microbiology
Teun Kuil, Shuen Hon, Johannes Yayo, Charles Foster, Giulia Ravagnan, Costas D. Maranas, Lee R. Lynd, Daniel G. Olson, Antonius J. A. van Maris
Summary: This study found that membrane-bound pyrophosphatase, glycogen cycling, Ppdk-malate shunt cycle, and acetate cycling are not significant sources of PPi supply in the atypical glycolysis of C. thermocellum. However, the true sources of PPi or alternative phosphorylating mechanisms that drive glycolysis in C. thermocellum remain elusive.
APPLIED AND ENVIRONMENTAL MICROBIOLOGY
(2022)
Review
Chemistry, Multidisciplinary
Lee R. Lynd, Gregg T. Beckham, Adam M. Guss, Lahiru N. Jayakody, Eric M. Karp, Costas Maranas, Robert L. McCormick, Daniel Amador-Noguez, Yannick J. Bomble, Brian H. Davison, Charles Foster, Michael E. Himmel, Evert K. Holwerda, Mark S. Laser, Chiam Yu Ng, Daniel G. Olson, Yuriy Roman-Leshkov, Cong T. Trinh, Gerald A. Tuskan, Vikas Upadhayay, Derek R. Vardon, Lin Wang, Charles E. Wyman
Summary: This article identifies the technology challenges and opportunities for developing economically viable, scalable, and sustainable technologies for converting lignocellulosic polysaccharides to liquid fuels. The overview of feedstocks, processes, and products highlights the potential of anaerobic processing for fuel production and the distinctive challenges associated with microbial processing of cellulosic biomass. The article also discusses opportunities to increase product tolerance and decrease the cost of product recovery, as well as pathways for converting anaerobic fermentation products to larger fuel molecules using chemo-catalysis.
ENERGY & ENVIRONMENTAL SCIENCE
(2022)
Article
Biotechnology & Applied Microbiology
Charles Foster, Veda Sheersh Boorla, Satyakam Dash, Saratram Gopalakrishnan, Tyler B. Jacobson, Daniel G. Olson, Daniel Amador-Noguez, Lee R. Lynd, Costas D. Maranas
Summary: This study aims to reveal unknown factors in the metabolism of Clostridium thermocellum by analyzing metabolic fluxes and constructing a kinetic model. The findings provide valuable insights for improving ethanol yield and titer.
METABOLIC ENGINEERING
(2022)
Article
Biotechnology & Applied Microbiology
Hoang Dinh, Debolina Sarkar, Costas D. Maranas
Summary: Flux balance analysis (FBA) is surprisingly robust in predicting cellular phenotypes, with two important assumptions at its core: biomass precursors and energy requirements do not change, and metabolite production and consumption rates are always equal. The study confirms the importance of enforcing these assumptions in predicting cellular phenotypes and explains the robustness of FBA biomass yield predictions.
METABOLIC ENGINEERING
(2022)
Article
Biochemistry & Molecular Biology
Veda Sheersh Boorla, Ratul Chowdhury, Ranjani Ramasubramanian, Brandon Ameglio, Rahel Frick, Jeffrey J. Gray, Costas D. Maranas
Summary: This study focuses on the computational design of neutralizing antibodies for emerging SARS-CoV-2 variants. By recombining VDJ genes and optimizing amino acid substitutions, high-affinity antibody variable regions were designed. Computational evaluation identified a promising candidate for experimental testing.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Mengqi Hu, Hoang Dinh, Yihui Shen, Patrick F. Suthers, Catherine M. Call, Xuanjia Ye, Jimmy Pratas, Zia Fatma, Huimin Zhao, Joshua D. Rabinowitz, Costas D. Maranas
Summary: The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and genetic perturbations. It remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. In this study, two separate large-scale kinetic models were parameterized using K-FIT for different strains of Saccharomyces cerevisiae, and the results showed strain-specific differences in key metabolic pathways.
METABOLIC ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Hoang Dinh, Costas D. Maranas
Summary: In this study, a compact and tractable genome-scale resource balance analysis (RBA) model called scRBA was reconstructed to analyze metabolic fluxes and proteome allocation efficiently. The model helps to identify bottlenecks in biosynthetic pathways and estimates parameter values that vary with environmental conditions. It reveals the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency, and explains the Crabtree effect by specific limitations in mitochondrial proteome capacity.
METABOLIC ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Wheaton L. Schroeder, Teun Kuil, Antonius J. A. van Maris, Lee R. Lynd, Costas D. Maranas
Summary: Lignocellulosic biomass is a potential carbon source for chemical manufacturing, and the thermophilic anaerobe Clostridium thermocellum shows promise in bioprocessing this biomass. A new genome-scale model, iCTH669, was constructed and analyzed to understand pyrophosphate metabolism in C. thermocellum. The model showed no direct energetic advantage for substituting ATP with pyrophosphate in C. thermocellum, but identified possible sources of pyrophosphate production.
METABOLIC ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Zong-Yen Wu, Wan Sun, Yihui Shen, Jimmy Pratas, Patrick F. Suthers, Ping-Hung Hsieh, Sudharsan Dwaraknath, Joshua D. Rabinowitz, Costas D. Maranas, Zengyi Shao, Yasuo Yoshikuni
Summary: Through genetic engineering, Issatchenkia orientalis was modified to produce 2.0 g/L of citramalate, showcasing its potential as a promising strain for citramalate production due to its ability to survive at extremely low pH levels.
METABOLIC ENGINEERING COMMUNICATIONS
(2023)
Article
Plant Sciences
Soyeon Choi, Pradeep K. Prabhakar, Ratul Chowdhury, Thomas H. Pendergast IV, Breeanna R. Urbanowicz, Costas Maranas, Katrien M. Devos
Summary: We have identified a key gene that can control the flowering time and biomass accumulation in switchgrass, a bioenergy crop native to North America, by showing how a single variant can significantly alter protein functionality. In this study, we mapped a robust flowering time quantitative trait locus (QTL) on chromosome 4K and identified the transcription factor gene PvHd1 as the underlying causal gene. Protein modeling and in vitro experiments demonstrated that a substitution at position 35 in the PvHd1 protein greatly changed its structure and functionality.
JOURNAL OF EXPERIMENTAL BOTANY
(2023)
Article
Biotechnology & Applied Microbiology
Vikas Upadhyay, Veda Sheersh Boorla, Costas D. Maranas
Summary: Retro-biosynthetic approaches have been improved with the EnzRank algorithm, which uses a convolutional neural network to rank existing enzymes based on their suitability for directed evolution or de novo design of specific substrate activity. The algorithm is trained on enzyme-substrate pairs from the BRENDA database and achieves high accuracy in predicting enzyme-substrate activity. A web-based user interface has also been developed for easy access to EnzRank using SMILES strings of substrates and enzyme sequences.
METABOLIC ENGINEERING
(2023)
Article
Biotechnology & Applied Microbiology
Deepro Banerjee, Michael A. Jindra, Alec J. Linot, Brian F. Pfleger, Costas D. Maranas
Summary: In this study, the researchers proposed a machine learning-based enzyme classification method called EnZymClass, which aims to address the difficulties and challenges in predicting the function of plant acyl-ACP thioesterases (TEs). By applying EnZymClass, the researchers successfully classified TEs into different substrate specificity categories and identified two enzymes with previously uncharacterized activity in experimental testing.
CURRENT RESEARCH IN BIOTECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Nicolas Huber, Edgar Alberto Alcala-Orozco, Thomas Rexer, Udo Reichl, Steffen Klamt
Summary: Cell-free production systems are commonly used for synthesizing industrial chemicals and biopharmaceuticals. This study presents a model-based optimization framework for cell-free enzyme cascades, taking into account parameter uncertainties. The approach was exemplified using the synthesis of GDP-fucose, resulting in significant improvements in the process.
METABOLIC ENGINEERING
(2024)
