1
|
Nausch H, Baldan M, Teichert K, Lutz J, Claussen C, Bortz M, Buyel JF. Simulation and optimization of nutrient uptake and biomass formation using a multi-parameter Monod-type model of tobacco BY-2 cell suspension cultures in a stirred-tank bioreactor. Front Plant Sci 2023; 14:1183254. [PMID: 38126010 PMCID: PMC10731461 DOI: 10.3389/fpls.2023.1183254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/27/2023] [Indexed: 12/23/2023]
Abstract
Introduction Tobacco (Nicotiana tabacum) cv Bright Yellow-2 (BY-2) cell suspension cultures enable the rapid production of complex protein-based biopharmaceuticals but currently achieve low volumetric productivity due to slow biomass formation. The biomass yield can be improved with tailored media, which can be designed either by laborious trial-and-error experiments or systematic, rational design using mechanistic models, linking nutrient consumption and biomass formation. Methods Here we developed an iterative experiment-modeling-optimization workflow to gradually refine such a model and its predictions, based on collected data concerning BY-2 cell macronutrient consumption (sucrose, ammonium, nitrate and phosphate) and biomass formation. Results and discussion The biomass formation was well predicted by an unstructured segregated mechanistic Monod-type model as long as the nutrient concentrations did not approach zero (we omitted phosphate, which was completely depleted). Multi-criteria optimization for sucrose and biomass formation indicated the best tradeoff (in a Paretian sense) between maximum biomass yield and minimum process time by reducing the initial sucrose concentration, whereas the inoculation biomass could be increased to maximize the biomass yield or minimize the process time, which we confirmed in calibration experiments. The model became inaccurate at biomass densities > 8 g L-1 dry mass when sucrose was almost depleted. We compensated for this limitation by including glucose and fructose as sucrose hydrolysis products in the model. The remaining offset between the simulation and experimental data might be resolved by including intracellular pools of sucrose, ammonium, nitrate and phosphate. Overall, we demonstrated that iterative models can be used to systematically optimize conditions for bioreactor-based processes.
Collapse
Affiliation(s)
- Henrik Nausch
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - Marco Baldan
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Katrin Teichert
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Jannik Lutz
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - Carsten Claussen
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Hamburg, Germany
| | - Michael Bortz
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Johannes Felix Buyel
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
- Institute for Molecular Biotechnology, RWTH Aachen University, Aachen, Germany
- Institute of Bioprocess Science and Engineering (IBSE), University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
| |
Collapse
|
2
|
Sawik B, Tobis S, Baum E, Suwalska A, Kropińska S, Stachnik K, Pérez-Bernabeu E, Cildoz M, Agustin A, Wieczorowska-Tobis K. Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study. Healthcare (Basel) 2023; 11:healthcare11091286. [PMID: 37174828 PMCID: PMC10178192 DOI: 10.3390/healthcare11091286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
This paper focuses on three areas: the first is a review of current knowledge about social and service robots for elderly care. The second is an optimization conceptual model aimed at maximizing the efficiency of assigning robots to serve the elderly. The proposed multi-criteria optimization model is the first one proposed in the area of optimization for robot assignment for the elderly with robot utilization level and caregiver stress level. The third is the findings of studies on the needs, requirements, and adoption of technology in elderly care. We consider the use of robots as a part of the ENRICHME project for long-term interaction and monitoring of older persons with mild cognitive impairment, to optimize their independence. Additionally, we performed focus group discussions (FGD) to collect opinions about robot-related requirements of the elderly and their caregivers. Four FDGs of six persons were organized: two comprising older adults, and two of the other formal and informal caregivers, based on a detailed script. The statements of older participants and their caregivers were consistent in several areas. The analysis revealed user characteristics, robot-related issues, functionality, and barriers to overcome before the deployment of the robot. An introduction of the robot must be thoroughly planned, include comprehensive pre-training, and take the ethical and practical issues into account. The involvement of future users in the customization of the robot is essential.
Collapse
Affiliation(s)
- Bartosz Sawik
- Department of Business Informatics and Engineering Management, AGH University of Science and Technology, 30-059 Krakow, Poland
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
- Haas School of Business, University of California at Berkeley, Berkeley, CA 94720, USA
| | - Sławomir Tobis
- Occupational Therapy Unit, Chair of Geriatric Medicine and Gerontology, Poznan University of Medical Sciences, ul. Swiecickiego 6, 60-781 Poznan, Poland
| | - Ewa Baum
- Department of Social Sciences and the Humanities, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Aleksandra Suwalska
- Department of Mental Health, Chair of Psychiatry, Poznan University of Medical Sciences, ul. Szpitalna 27/33, 60-572 Poznan, Poland
| | - Sylwia Kropińska
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
| | - Katarzyna Stachnik
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
| | - Elena Pérez-Bernabeu
- Department of Applied Statistics and Operations Research, Universitat Politècnica de València, Plaza Ferrandiz y Carbonell, sn, 03801 Alcoy, Spain
| | - Marta Cildoz
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
| | - Alba Agustin
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
| | - Katarzyna Wieczorowska-Tobis
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
| |
Collapse
|
3
|
Bouchez A, Vauchel P, Périno S, Dimitrov K. Multi-Criteria Optimization including Environmental Impacts of a Microwave-Assisted Extraction of Polyphenols and Comparison with an Ultrasound-Assisted Extraction Process. Foods 2023; 12:foods12091750. [PMID: 37174289 PMCID: PMC10177992 DOI: 10.3390/foods12091750] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Valorization of wastes and by-products using environmentally friendly technologies with an optimal cost-benefit relationship is a current major issue in agri-food industries. An original tool was recently developed for multi-criteria optimization of an ultrasound-assisted extraction (UAE) process including the assessment of environmental impacts using Life Cycle Assessment. In the present work, this methodology was adapted and applied to another green extraction process, microwave-assisted extraction (MAE), with the same case study, valorization of antioxidant polyphenols from downgraded beet seeds. Once built, the obtained multi-criteria optimization tool was used to investigate performances of the MAE process regarding productivity criteria (polyphenol concentration and antioxidant activity of the extracts), energy consumption and environmental impacts as functions of operating parameters (time, solvent composition, microwave power density, and liquid-solid ratio). The MAE process was optimized under different constraints and compared to the UAE process. For the studied conditions and different investigated scenarios, MAE enabled obtaining extracts with higher polyphenol concentrations and antioxidant activity (approximately 33% and 23% enhancements, respectively), and to strongly reduce extraction duration (by a factor up to 6), whereas UAE enabled reducing the energy consumption (up to 3.6 fold) and the environmental impacts (up to 12% for climate change).
Collapse
Affiliation(s)
- Alice Bouchez
- UMR-T 1158, BioEcoAgro Univ. Lille, INRAE, Univ. Artois, Univ. Littoral Côte d'Opale, JUNIA, Univ. Liège, Univ. Picardie Jules Verne, Institut Charles Viollette, F-59000 Lille, France
| | - Peggy Vauchel
- UMR-T 1158, BioEcoAgro Univ. Lille, INRAE, Univ. Artois, Univ. Littoral Côte d'Opale, JUNIA, Univ. Liège, Univ. Picardie Jules Verne, Institut Charles Viollette, F-59000 Lille, France
| | - Sandrine Périno
- GREEN Extraction Team, UMR 408, Avignon University, INRAE, F-84000 Avignon, France
| | - Krasimir Dimitrov
- UMR-T 1158, BioEcoAgro Univ. Lille, INRAE, Univ. Artois, Univ. Littoral Côte d'Opale, JUNIA, Univ. Liège, Univ. Picardie Jules Verne, Institut Charles Viollette, F-59000 Lille, France
| |
Collapse
|
4
|
Jayarathna S, Shen X, Chen RC, Li HH, Guida K. The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT. J Appl Clin Med Phys 2023:e13940. [PMID: 36827178 DOI: 10.1002/acm2.13940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/21/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.
Collapse
Affiliation(s)
- Sandun Jayarathna
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinglei Shen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Ronald C Chen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - H Harold Li
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Kenny Guida
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| |
Collapse
|
5
|
Wuyckens S, Zhao L, Saint-Guillain M, Janssens G, Sterpin E, Souris K, Ding X, Lee JA. Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy. Phys Med Biol 2022; 67. [PMID: 36541505 DOI: 10.1088/1361-6560/aca5e9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 11/24/2022] [Indexed: 11/27/2022]
Abstract
Objective. Proton arc therapy (PAT) is a new delivery technique that exploits the continuous rotation of the gantry to distribute the therapeutic dose over many angular windows instead of using a few static fields, as in conventional (intensity-modulated) proton therapy. Although coming along with many potential clinical and dosimetric benefits, PAT has also raised a new optimization challenge. In addition to the dosimetric goals, the beam delivery time (BDT) needs to be considered in the objective function. Considering this bi-objective formulation, the task of finding a good compromise with appropriate weighting factors can turn out to be cumbersome.Approach. We have computed Pareto-optimal plans for three disease sites: a brain, a lung, and a liver, following a method of iteratively choosing weight vectors to approximate the Pareto front with few points. Mixed-integer programming (MIP) was selected to state the bi-criteria PAT problem and to find Pareto optimal points with a suited solver.Main results. The trade-offs between plan quality and beam irradiation time (staticBDT) are investigated by inspecting three plans from the Pareto front. The latter are carefully picked to demonstrate significant differences in dose distribution and delivery time depending on their location on the frontier. The results were benchmarked against IMPT and SPArc plans showing the strength of degrees of freedom coming along with MIP optimization.Significance. This paper presents for the first time the application of bi-criteria optimization to the PAT problem, which eventually permits the planners to select the best treatment strategy according to the patient conditions and clinical resources available.
Collapse
Affiliation(s)
- Sophie Wuyckens
- UCLouvain, Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Lewei Zhao
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | | | | | - Edmond Sterpin
- UCLouvain, Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.,KULeuven, Department of Oncology, Leuven, Belgium
| | - Kevin Souris
- UCLouvain, Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.,Ion Beam Applications SA, Louvain-La-Neuve, Belgium
| | - Xuanfeng Ding
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | - John A Lee
- UCLouvain, Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| |
Collapse
|
6
|
Dong B, Ikonnikova I, Rogulin R, Sakulyeva T, Mikhaylov A. Environmental-economic approach to optimization of transport communication in megacities. J Environ Sci Health A Tox Hazard Subst Environ Eng 2021; 56:660-666. [PMID: 34162313 DOI: 10.1080/10934529.2021.1913928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 06/13/2023]
Abstract
The paper presents the results of the study on the transport systems in metropolises by stating the interdisciplinary problems and prospects of applying the environmental and economic approach to the optimization of transport communication in megacities. The main problems of transport communications in large cities have been considered. The world experience in managing transport systems is analyzed. Priority directions for the implementation of environmental and economic tasks of transport industry management using the economic and mathematical approach have been established. It has been established that the performance indicators of the functioning of the megalopolis transport system depend on the work of the service provider. The most effective solution is to ensure that vehicles are working properly, that the vehicles are fully loaded and that they are on schedule. Performance indicators of the functioning of the megalopolis transport system on the part of the consumer of services: the coefficient of determination was 0.9493, the static probability p = 0.2067 standard deviation was 7.9428, the variance did not exceed 0.1015. The correlation of actual and planned results exceeded r2>0.7051; and was described by the equation y = 1.6746 + 0.844x. Logistic models for performance improvement of the transportation system were offered.
Collapse
Affiliation(s)
- Bo Dong
- School of Innovation and Entrepreneurship, Liaoning Unviersity, Shenyang, China
| | - Irina Ikonnikova
- Department of Medical Computer Science and Statistics, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rodion Rogulin
- Department of Mathematics and Modelling, Vladivostok State University of Economics and Service, Vladivostok, Russia
- Department of Applied Mathematics, Mechanics, Controlling and Software, Far Eastern Federal University, Vladivostok, Russia
| | - Tatyana Sakulyeva
- Department of Transportation Management, State University of Management, Moscow, Russia
| | - Alexey Mikhaylov
- Financial Research Institute of Ministry of Finance of the Russian Federation, Moscow, Russian Federation
| |
Collapse
|
7
|
Jensen PJ, Zhang J, Wu QJ. Technical note: Interpolated Pareto surface similarity metrics for multi-criteria optimization in radiation therapy. Med Phys 2020; 47:6450-6457. [PMID: 33058151 DOI: 10.1002/mp.14541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/07/2020] [Accepted: 08/04/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE There is a strong clinical need to evaluate different multi-criteria optimization (MCO) algorithms, including inverse optimization sampling algorithms and machine learning-based predictions. This study aims to develop and compare several interpolated Pareto surface similarity metrics. MATERIALS AND METHODS The first metric is the root-mean-square error (RMSE) evaluated between vertices on the interpolated surfaces, augmented by intra-simplex sampling of the barycentric coordinates of the surfaces' simplicial complexes. The second metric is the average projected distance (APD), which evaluates the displacements between the vertices and computes their projections along the mean displacement. The third metric is the average nearest-point distance (ANPD), which numerically integrates point-to-simplex distances over the sampled simplices of the interpolated surfaces. These metrics were compared by their convergence rates, the times required to achieve convergence, and their representation of the underlying surface interpolations. For analysis, several interpolated Pareto surface pairs were constructed abstractly, with one pair from a nasopharyngeal treatment planning case using MCO. RESULTS Convergence within 1% is typically achieved at approximately 50 and 80 samples per barycentric dimension for the RMSE and the ANPD, respectively. Calculation requires approximately 1 and 10 ms to achieve convergence for the RMSE and the ANPD in two dimensions, respectively, while the APD always requires < 1 ms. These time costs are much higher in higher dimensions for just the RMSE and ANPD. The APD values more closely approximated the ANPD limits than the RMSE limits. CONCLUSION The ANPD's formulation and generality make it likely more meaningful than the RMSE and APD for representing the similarity between the underlying interpolated surfaces rather than the sampling points on the surfaces. However, in situations requiring high-speed evaluations, the APD may be more desirable due to its speed, independence from a subjectively chosen sampling rate, and similarity to the ANPD limits.
Collapse
Affiliation(s)
- P James Jensen
- Department of Radiation Oncology, Duke Cancer Institute, Durham, NC, 27710, USA
| | - Jiahan Zhang
- Department of Radiation Oncology, Duke Cancer Institute, Durham, NC, 27710, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke Cancer Institute, Durham, NC, 27710, USA
| |
Collapse
|
8
|
Leite WDO, Campos Rubio JC, Mata Cabrera F, Carrasco A, Hanafi I. Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks. Polymers (Basel) 2018; 10:polym10020143. [PMID: 30966179 PMCID: PMC6415129 DOI: 10.3390/polym10020143] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 01/23/2018] [Accepted: 01/31/2018] [Indexed: 11/30/2022] Open
Abstract
In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks’ inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models’ predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.
Collapse
Affiliation(s)
- Wanderson De Oliveira Leite
- Departamento de Mecânica, Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerias-Campus Betim, Rua Itaguaçu, No. 595, São Caetano, 32677-780 Betim, Brazil.
| | - Juan Carlos Campos Rubio
- Escola de Engenharia, Departamento de Engenharia Mecânica, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, No. 6627, Pampulha, 31270-901 Belo Horizonte, Brazil.
| | - Francisco Mata Cabrera
- Escuela de Ingeniería Minera e Industrial de Almadén, Departamento Mecánica Aplicada e Ingeniería de Proyectos, Universidad de Castilla-La Mancha, Plaza Manuel Meca No. 1, 13400 Ciudad Real, Spain.
| | - Angeles Carrasco
- Escuela de Ingeniería Minera e Industrial de Almadén, Departamento de Filología Moderna, Universidad de Castilla-La Mancha, Plaza Manuel Meca No. 1, 13400 Ciudad Real, Spain.
| | - Issam Hanafi
- Ecole Nationale des Sciences Appliquées d'Al Hoceima (ENSAH), Département of Civil and Environmental Engineering, 32000 Al Hoceima, Morocco.
| |
Collapse
|
9
|
Camacho-Cáceres KI, Acevedo-Díaz JC, Pérez-Marty LM, Ortiz M, Irizarry J, Cabrera-Ríos M, Isaza CE. Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge. Cancer Med 2015; 4:1884-900. [PMID: 26471143 PMCID: PMC4940807 DOI: 10.1002/cam4.540] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2015] [Revised: 07/30/2015] [Accepted: 07/14/2015] [Indexed: 12/14/2022] Open
Abstract
Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.
Collapse
Affiliation(s)
- Katia I Camacho-Cáceres
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Juan C Acevedo-Díaz
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Lynn M Pérez-Marty
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Michael Ortiz
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Juan Irizarry
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Mauricio Cabrera-Ríos
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico
| | - Clara E Isaza
- Bio IE Lab, The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico, Mayaguez, Puerto Rico.,Public Health Program, Ponce Health Sciences University, Ponce, Puerto Rico
| |
Collapse
|
10
|
Clark VH, Chen Y, Wilkens J, Alaly JR, Zakaryan K, Deasy JO. IMRT treatment planning for prostate cancer using prioritized prescription optimization and mean-tail-dose functions. Linear Algebra Appl 2008; 428:1345-1364. [PMID: 18974791 PMCID: PMC2574493 DOI: 10.1016/j.laa.2007.07.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Treatment planning for intensity modulated radiation therapy (IMRT) is challenging due to both the size of the computational problems (thousands of variables and constraints) and the multi-objective, imprecise nature of the goals. We apply hierarchical programming to IMRT treatment planning. In this formulation, treatment planning goals/objectives are ordered in an absolute hierarchy, and the problem is solved from the top-down such that more important goals are optimized in turn. After each objective is optimized, that objective function is converted into a constraint when optimizing lower-priority objectives. We also demonstrate the usefulness of a linear/quadratic formulation, including the use of mean-tail-dose (mean dose to the hottest fraction of a given structure), to facilitate computational efficiency. In contrast to the conventional use of dose-volume constraints (no more than x% volume of a structure should receive more than y dose), the mean-tail-dose formulation ensures convex feasibility spaces and convex objective functions. To widen the search space without seriously degrading higher priority goals, we allowed higher priority constraints to relax or 'slip' a clinically negligible amount during lower priority iterations. This method was developed and tuned for external beam prostate planning and subsequently tested using a suite of 10 patient datasets. In all cases, good dose distributions were generated without individual plan parameter adjustments. It was found that allowance for a small amount of 'slip,' especially in target dose homogeneity, often resulted in improved normal tissue dose burdens. Compared to the conventional IMRT treatment planning objective function formulation using a weighted linear sum of terms representing very different dosimetric goals, this method: (1) is completely automatic, requiring no user intervention, (2) ensures high-priority planning goals are not seriously degraded by lower-priority goals, and (3) ensures that lower priority, yet still important, normal tissue goals are separately pushed as far as possible without seriously impacting higher priority goals.
Collapse
Affiliation(s)
- V. H. Clark
- Department of Radiation Oncology, Washington University School of Medicine, and the Siteman Cancer Center, Saint Louis, MO, USA
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO, USA
| | - Y. Chen
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO, USA
| | - J. Wilkens
- Department of Radiation Oncology, Washington University School of Medicine, and the Siteman Cancer Center, Saint Louis, MO, USA
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - J. R. Alaly
- Department of Radiation Oncology, Washington University School of Medicine, and the Siteman Cancer Center, Saint Louis, MO, USA
| | - K. Zakaryan
- Department of Radiation Oncology, Washington University School of Medicine, and the Siteman Cancer Center, Saint Louis, MO, USA
| | - J. O. Deasy
- Department of Radiation Oncology, Washington University School of Medicine, and the Siteman Cancer Center, Saint Louis, MO, USA
| |
Collapse
|