Article
Engineering, Environmental
Thomas Krumpolc, D. W. Trahan, D. A. Hickman, L. T. Biegler
Summary: Applications of fixed-effects models for kinetic parameter estimation assume independence among batches, but biased residuals often exist in multiple longitudinal batch experiments with time series data. Nonlinear mixed-effects models provide an alternative approach to address the two types of random experimental variation resulting from longitudinal experiments: measurement error for each data point and random batch-to-batch variation. In our case study, implementing a mixed-effects model using nonlinear programming for a batch reactor system yields parameter estimates with less bias compared to a fixed-effects model. Additionally, the Bayesian notion of probability shares is applied to discriminate between several candidate mixed-effects models, demonstrating the ability to elucidate additional model information when fixed-effects models are inappropriate.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Food Science & Technology
Jesus Miguel Zamudio Lara, Laurent Dewasme, Hector Hernandez Escoto, Alain Vande Wouwer
Summary: Two dynamic models of beer fermentation are proposed in this study, and their parameters are estimated using experimental data. The structural identifiability of the measurement configuration and kinetic model structure is analyzed, and the predictive capability of the model is investigated. The model can be used for monitoring and controlling the beer fermentation process.
Review
Biochemical Research Methods
Veronica Porubsky, Lucian Smith, Herbert M. Sauro
Summary: The article highlights the importance of publishing repeatable and reproducible computational models in computational biology, specifically focusing on issues in the systems biology field. It discusses the current landscape in terms of software tools, model repositories, standards, and best practices, as well as potential future remedies to improve reproducibility in scientific research.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Carlos Sequeiros, Irene Otero-Muras, Carlos Vazquez, Julio R. Banga
Summary: Mechanistic dynamic models are important for understanding biomolecular networks and biological systems. Stochastic dynamic models should be used when dealing with low copy numbers and biochemical stochasticity. This article presents a novel strategy for parameter estimation in stochastic dynamic models, employing global optimization and stochastic simulation techniques.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Review
Biochemical Research Methods
Charles J. Foster, Lin Wang, Hoang Dinh, Patrick F. Suthers, Costas D. Maranas
Summary: Kinetic formalisms of metabolism provide a mechanistic link across heterogeneous omics datasets to inform metabolic engineering strategies. Despite challenges in identifying physiologically relevant values for parameters, recent progress in computational power, gene annotation coverage, and formalism standardization have enabled significant advancements. Careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges that need to be addressed.
CURRENT OPINION IN BIOTECHNOLOGY
(2021)
Article
Optics
Xuan Cong, Hongxin Zeng, Shiqi Wang, Qiwu Shi, Shixiong Liang, Jiandong Sun, Sen Gong, Feng Lan, Ziqiang Yang, Yaxin Zhang
Summary: This work demonstrates a functionally decoupled terahertz metasurface that can incorporate any two functions into one metasurface and switch dynamically through external excitation. The metasurface operates in two modes with independent phase modulation, allowing for dynamic switching between dual functions.
PHOTONICS RESEARCH
(2022)
Article
Engineering, Chemical
Kaveh Abdi, Benoit Celse, Kimberley B. B. McAuley
Summary: Error-in-variables model (EVM) methods are used to estimate parameters when independent variables are uncertain. This study proposes a method to estimate output measurement variances based on pseudo-replicate data for multivariate EVM estimation problems. Additionally, a bootstrap technique is proposed to quantify uncertainties in resulting parameter estimates and model predictions. A case study on n-hexane hydroisomerization is presented to illustrate the methods, showing the influence of input uncertainties on parameter estimates, model predictions, and confidence intervals.
Article
Engineering, Electrical & Electronic
Georgios A. Barzegkar-Ntovom, Theofilos A. Papadopoulos, Eleftherios O. Kontis
Summary: This paper presents a measurement-based online framework for deriving dynamic equivalent models, which includes automatic detection of suitable events/disturbances, signal processing techniques, fine-tuning of signal window length, and parameter estimation through nonlinear least-squares optimization. The methodology's performance is tested using artificially created signals, simulation results, and measurements from a laboratory-scale active distribution network.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Shuhui Wang, Yongguang Yu, Wei Hu
Summary: Photovoltaic modeling research is gaining interest, with a focus on accuracy for system design and control. A novel hybrid optimization algorithm HROA is proposed for parameter extraction, offering efficiency and no need for parameter tuning.
JOURNAL OF CLEANER PRODUCTION
(2021)
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
Engineering, Electrical & Electronic
Ning Mao, Jingshu Li, Hongyang He, Jiangning Xu
Summary: The paper introduces a new coarse attitude estimation method for a dynamic base with the decoupling of the gravity vector, which does not require the participation of the gravity vector and effectively improves the errors caused by inaccurate gravity vector in SINS attitude estimation.
IEEE SENSORS JOURNAL
(2021)
Article
Meteorology & Atmospheric Sciences
Yohei Sawada
Summary: A new method called HOOPE-PF is introduced to estimate time-varying parameters in relatively low dimensional models. It outperforms the original SIRPF in synthetic and real-data experiments, and is not greatly affected by the size of perturbations added to ensemble members.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Energy & Fuels
Xiaoyan Qiu, Hang Zhang, Yiwei Qiu, Yi Zhou, Tianlei Zang, Buxiang Zhou, Ruomei Qi, Jin Lin, Jiepeng Wang
Summary: Utility-scale hydrogen production via alkaline electrolysis is an effective way to reduce carbon emissions in various industries. The efficiency, flexibility, and safety of the alkaline electrolysis system are influenced by electrochemical, thermal, and mass transfer dynamics. However, the lack of a comprehensive parameter estimation method has hindered the accuracy and adaptability of existing models. To address this, a fast and accurate parameter estimation method based on Bayesian inference and Markov chain Monte Carlo is proposed. Experimental results demonstrate the effectiveness of this method in improving estimation accuracy and providing fault diagnosis and sensitivity analysis for alkaline electrolysis systems.
Article
Environmental Sciences
Jenny Kupzig, Robert Reinecke, Francesca Pianosi, Martina Floerke, Thorsten Wagener
Summary: Global hydrological models (GHMs) provide important information for simulating water cycles and supporting decision-making. However, inaccuracies in GHM simulations can hinder valuable decision support. In this study, we introduce a transparent and efficient method to understand parameter control in GHMs and improve parameter estimation using global sensitivity analysis (GSA). Our findings show that traditionally neglected model parameters have a significant influence on GHM simulations, and basin attributes explain the spatial variability of parameter importance better than climate zones. Overall, our results demonstrate the effectiveness of GSA in guiding parameter estimation and improving the accuracy of GHM simulations.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Optics
Yinghua Ji, Juju Hu
Summary: Decoherence is a significant obstacle in quantum precision measurement. This paper explores the impact of dynamical decoupling pulses on inhibiting decoherence and enhancing quantum parameter estimation precision through the dephasing model of system-environment with initial correlation. Results show that choosing the right PDD pulse frequency can effectively improve the precision of quantum parameter estimation, while the application of UDD sequence may not yield ideal results due to the peak structure in the decoherence function.
Review
Biochemical Research Methods
Sarantos Kyriakopoulos, Kok Siong Ang, Meiyappan Lakshmanan, Zhuangrong Huang, Seongkyu Yoon, Rudiyanto Gunawan, Dong-Yup Lee
BIOTECHNOLOGY JOURNAL
(2018)
Article
Biochemistry & Molecular Biology
Heeju Noh, Jason E. Shoemaker, Rudiyanto Gunawan
NUCLEIC ACIDS RESEARCH
(2018)
Article
Biophysics
Thierry Baasch, Peter Reichert, Stefan Lakaemper, Nadia Vertti-Quintero, Gamuret Hack, Xavier Casadevall i Solvas, Andrew deMello, Rudiyanto Gunawan, Juerg Dual
BIOPHYSICAL JOURNAL
(2018)
Article
Engineering, Chemical
Sandro Hutter, Moritz Wolf, Nan Papili Gao, Dario Lepori, Thea Schweigler, Massimo Morbidelli, Rudiyanto Gunawan
Editorial Material
Engineering, Chemical
Rudiyanto Gunawan, Neda Bagheri
Article
Multidisciplinary Sciences
Ce Zhang, Hsiung-Lin Tu, Gengjie Jia, Tanzila Mukhtar, Verdon Taylor, Andrey Rzhetsky, Savas Tay
Article
Multidisciplinary Sciences
Gengjie Jia, Yu Li, Hanxin Zhang, Ishanu Chattopadhyay, Anders Boeck Jensen, David R. Blair, Lea Davis, Peter N. Robinson, Torsten Dahlen, Soren Brunak, Mikael Benson, Gustaf Edgren, Nancy J. Cox, Xin Gao, Andrey Rzhetsky
NATURE COMMUNICATIONS
(2019)
Article
Genetics & Heredity
Xue Zhong, Zhijun Yin, Gengjie Jia, Dan Zhou, Qiang Wei, Annika Faucon, Patrick Evans, Eric R. Gamazon, Bingshan Li, Ran Tao, Andrey Rzhetsky, Lisa Bastarache, Nancy J. Cox
GENETICS IN MEDICINE
(2020)
Article
Multidisciplinary Sciences
Gengjie Jia, Xue Zhong, Hae Kyung Im, Nathan Schoettler, Milton Pividori, D. Kyle Hogarth, Anne Sperling, Steven R. White, Edward T. Naureckas, Christopher S. Lyttle, Chikashi Terao, Yoichiro Kamatani, Masato Akiyama, Koichi Matsuda, Michiaki Kubo, Nancy J. Cox, Carole Ober, Andrey Rzhetsky, Julian Solway
Summary: This study identifies different asthma endotypes based on comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes.
NATURE COMMUNICATIONS
(2022)
Proceedings Paper
Automation & Control Systems
Juergen Hahn, Matthew Chang, Michael Koepke, Neda Bagheri, Rudiyanto Gunawan
Article
Biochemical Research Methods
Nan Papili Gao, S. M. Minhaz Ud-Dean, Olivier Gandrillon, Rudiyanto Gunawan