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Kurian V, Gee M, Farrington S, Yang E, Okossi A, Chen L, Beris AN. Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease Part II: Extension for Variable Metabolic Rates. ACS OMEGA 2024; 9:494-508. [PMID: 38222577 PMCID: PMC10785060 DOI: 10.1021/acsomega.3c05953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 11/16/2023] [Accepted: 11/23/2023] [Indexed: 01/16/2024]
Abstract
Recently, we developed a systems engineering model of the human cardiorespiratory system [Kurian et al. ACS Omega2023, 8 (23), 20524-20535. DOI: 10.1021/acsomega.3c00854] based on existing models of physiological processes and adapted it for chronic obstructive pulmonary disease (COPD)-an inflammatory lung disease with multiple manifestations and one of the leading causes of death in the world. This control engineering-based model is extended here to allow for variable metabolic rates established at different levels of physical activity. This required several changes to the original model: the model of the controller was enhanced to include the feedforward loop that is responsible for cardiorespiratory control under varying metabolic rates (activity level, characterized as metabolic equivalent of the task-Rm-and normalized to one at rest). In addition, a few refinements were made to the cardiorespiratory mechanics, primarily to introduce physiological processes that were not modeled earlier but became important at high metabolic rates. The extended model is verified by analyzing the impact of exercise (Rm > 1) on the cardiorespiratory system of healthy individuals. We further formally justify our previously proposed adaptation of the model for COPD patients through sensitivity analysis and refine the parameter tuning through the use of a parallel tempering stochastic global optimization method. The extended model successfully replicates experimentally observed abnormalities in COPD-the drop in arterial oxygen tension and dynamic hyperinflation under high metabolic rates-without being explicitly trained on any related data. It also supports the prospects of remote patient monitoring in COPD.
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Affiliation(s)
- Varghese Kurian
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Michelle Gee
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Daniel
Baugh Institute of Functional Genomics/Computational Biology, Department
of Pathology and Genomic Medicine, Thomas
Jefferson University, Philadelphia, Pennsylvania 19107, United States
| | - Sean Farrington
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Entao Yang
- American
Air Liquide Inc., Innovation
Campus Delaware, Newark, Delaware 19702, United States
| | - Alphonse Okossi
- American
Air Liquide Inc., Innovation
Campus Delaware, Newark, Delaware 19702, United States
| | - Lucy Chen
- American
Air Liquide Inc., Innovation
Campus Delaware, Newark, Delaware 19702, United States
| | - Antony N. Beris
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
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Wu HJ, Singla A, Weatherston JD. Nanocube-Based Fluidic Glycan Array. Methods Mol Biol 2022; 2460:45-63. [PMID: 34972930 DOI: 10.1007/978-1-0716-2148-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The nature of cell membrane fluidity permits glycans, which are attached to membrane proteins and lipids, to freely diffuse on cell surfaces. Through such two-dimensional motion, some weakly binding glycans can participate in lectin binding processes, eventually changing lectin binding behaviors. This chapter discusses a plasmonic nanocube sensor that allows users to detect lectin binding kinetics in a cell membrane mimicking environment. This assay only requires standard laboratory spectrometers, including microplate readers. We describe the basics of the technology in detail, including sensor fabrication, sensor calibration, data processing, a general protocol for detecting lectin-glycan interactions, and a troubleshooting guide.
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Affiliation(s)
- Hung-Jen Wu
- Department of Chemical Engineering, Texas A&M University, College Station, TX, USA.
| | - Akshi Singla
- Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Joshua D Weatherston
- Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
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3
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Lee D, Green A, Wu H, Kwon JS. Hybrid
PDE‐kMC
modeling approach to simulate multivalent lectin‐glycan binding process. AIChE J 2021. [DOI: 10.1002/aic.17453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Dongheon Lee
- Department of Biomedical Engineering Duke University Durham North Carolina USA
| | - Aaron Green
- Artie McFerrin Department of Chemical Engineering Texas A&M University Texas USA
| | - Hung‐Jen Wu
- Artie McFerrin Department of Chemical Engineering Texas A&M University Texas USA
| | - Joseph Sang‐Il Kwon
- Artie McFerrin Department of Chemical Engineering Texas A&M University Texas USA
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4
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A Framework for Economically Optimal Operation of Explosive Waste Incineration Process to Reduce NOx Emission Concentration. MATHEMATICS 2021. [DOI: 10.3390/math9172174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste.
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Lee D, Jayaraman A, Kwon JS. Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling. PLoS Comput Biol 2020; 16:e1008472. [PMID: 33315899 PMCID: PMC7769624 DOI: 10.1371/journal.pcbi.1008472] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/28/2020] [Accepted: 10/26/2020] [Indexed: 12/30/2022] Open
Abstract
Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively. An intracellular signaling pathway is often represented by a set of nonlinear ordinary differential equations, which translate our current knowledge about the signaling pathway into a testable mathematical model. However, predictions from such models are often subject to high uncertainty since many signaling pathways are only partially known beforehand. In this study, we propose a systematic approach to develop a hybrid model to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN.
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Affiliation(s)
- Dongheon Lee
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas, USA
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
| | - Joseph S. Kwon
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas, USA
- * E-mail:
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6
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Chen W, Biegler LT. Reduced Hessian based parameter selection and estimation with simultaneous collocation approach. AIChE J 2020. [DOI: 10.1002/aic.16242] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Weifeng Chen
- College of Information EngineeringZhejiang University of Technology Hangzhou China
| | - Lorenz T. Biegler
- Center for Advanced Process Decision‐making, Department of Chemical EngineeringCarnegie Mellon University Pittsburgh Pennsylvania USA
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Lee D, Jayaraman A, Kwon JS. Identification of cell‐to‐cell heterogeneity through systems engineering approaches. AIChE J 2020. [DOI: 10.1002/aic.16925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Dongheon Lee
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
| | - Joseph S.‐I. Kwon
- Artie McFerrin Department of Chemical EngineeringTexas A&M University Texas
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Lee D, Jayaraman A, Sang-Il Kwon J. Identification of a time-varying intracellular signalling model through data clustering and parameter selection: application to NF-[inline-formula removed]B signalling pathway induced by LPS in the presence of BFA. IET Syst Biol 2019; 13:169-179. [PMID: 31318334 PMCID: PMC8687386 DOI: 10.1049/iet-syb.2018.5079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 01/02/2023] Open
Abstract
Developing a model for a signalling pathway requires several iterations of experimentation and model refinement to obtain an accurate model. However, the implementation of such an approach to model a signalling pathway induced by a poorly-known stimulus can become labour intensive because only limited information on the pathway is available beforehand to formulate an initial model. Therefore, a large number of iterations are required since the initial model is likely to be erroneous. In this work, a numerical scheme is proposed to construct a time-varying model for a signalling pathway induced by a poorly-known stimulus when its nominal model is available in the literature. Here, the nominal model refers to one that describes the signalling dynamics under a well-characterised stimulus. First, global sensitivity analysis is implemented on the nominal model to identify the most important parameters, which are assumed to be piecewise constants. Second, measurement data are clustered to determine temporal subdomains where the parameters take different values. Finally, a least-squares problem is solved to estimate the parameter values in each temporal subdomain. The effectiveness of this approach is illustrated by developing a time-varying model for NF-[inline-formula removed]B signalling dynamics induced by lipopolysaccharide in the presence of brefeldin A.
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Affiliation(s)
- Dongheon Lee
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Arul Jayaraman
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Joseph Sang-Il Kwon
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA.
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Amdoun R, Benyoussef EH, Benamghar A, Khelifi L. Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.01.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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10
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Worstell NC, Singla A, Wu HJ. Evaluation of hetero-multivalent lectin binding using a turbidity-based emulsion agglutination assay. Colloids Surf B Biointerfaces 2019; 175:84-90. [PMID: 30522011 PMCID: PMC10079213 DOI: 10.1016/j.colsurfb.2018.11.069] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/20/2018] [Accepted: 11/27/2018] [Indexed: 12/28/2022]
Abstract
Lectin hetero-multivalency, binding to two or more different types of ligands, has been demonstrated to play a role in case of both LecA (a Pseudomonas aeruginosa adhesin) and Cholera Toxin subunit B (a Vibrio cholerae toxin). In order to screen the ligand candidates that involve in hetero-multivalent binding from large molecular libraries, we present a turbidity-based emulsion agglutination (TEA) assay that can be conducted in a high-throughput format using the standard laboratory instruments and reagents. The benefit of this assay is that it relies on the use of emulsions that can be formed using ultrasonication, minimizing the bottleneck of substrate surface functionalization. By measuring the change in turbidity, we could quantify the lectin-induced aggregation rate of oil droplets to determine the relative binding strength between different ligand combinations. The TEA results are consistent with our prior binding results using a nanocube sensor. The developed TEA assay can serve as a high-throughput and customizable tool to screen the potential ligands involved in hetero-multivalent binding.
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Affiliation(s)
- Nolan C Worstell
- Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Akshi Singla
- Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Hung-Jen Wu
- Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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Lee D, Mohr A, Kwon JSI, Wu HJ. Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.08.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Kim JH, Lee JM. Successive complementary model-based experimental designs for parameter estimation of fed-batch bioreactors. Bioprocess Biosyst Eng 2018; 41:1767-1777. [PMID: 30099622 DOI: 10.1007/s00449-018-1999-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/07/2018] [Indexed: 10/28/2022]
Abstract
When a dynamic model is used for the description of (fed-)batch bioreactors, it is typical that the model parameters are highly correlated to each other. In this case, it is important to keep the parameter correlation as small as possible to obtain a reliable set of parameter estimates. In this study, we propose an anticorrelation parameter estimation scheme that can be best utilized when a number of different batch experiments are sequentially processed. The scheme iteratively performs parameter estimation and model-based design of experiment (MBDOE) at the beginning and between the batches. The important difference from the existing approaches is that the MBDOE objective is defined according to the system analysis performed a priori, so that each new batch supplements what is lacking from the previous batches combined, in terms of information. The use of the scheme is illustrated on a fed-batch bioreactor model.
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Affiliation(s)
- Jung Hun Kim
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Jong Min Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
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