• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

论目标在生物医学治疗房室模型优化中的作用

On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies.

作者信息

Ledzewicz Urszula, Schättler Heinz

机构信息

Lodz University of Technology, 90-924 Lodz, Poland.

Southern Illinois University Edwardsville, Edwardsville, IL 62026-1653 USA.

出版信息

J Optim Theory Appl. 2020;187(2):305-335. doi: 10.1007/s10957-020-01754-2. Epub 2020 Sep 30.

DOI:10.1007/s10957-020-01754-2
PMID:33012845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7525767/
Abstract

We review and discuss results obtained through an application of tools of nonlinear optimal control to biomedical problems. We discuss various aspects of the modeling of the dynamics (such as growth and interaction terms), modeling of treatment (including pharmacometrics of the drugs), and give special attention to the choice of the objective functional to be minimized. Indeed, many properties of optimal solutions are predestined by this choice which often is only made casually using some simple ad hoc heuristics. We discuss means to improve this choice by taking into account the underlying biology of the problem.

摘要

我们回顾并讨论了通过将非线性最优控制工具应用于生物医学问题所获得的结果。我们讨论了动力学建模的各个方面(如生长和相互作用项)、治疗建模(包括药物的药代动力学),并特别关注要最小化的目标函数的选择。实际上,最优解的许多性质由这种选择预先决定,而这种选择通常只是随意地使用一些简单的临时启发式方法做出的。我们讨论了通过考虑问题的基础生物学来改进这种选择的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/6af179545a73/10957_2020_1754_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/5c60b778a752/10957_2020_1754_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/7838e810c5ca/10957_2020_1754_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/0e747ba5e0de/10957_2020_1754_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/00d096f5f1f4/10957_2020_1754_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/08ac6b0d3a8a/10957_2020_1754_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/3408765ef8c2/10957_2020_1754_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/b670574c556d/10957_2020_1754_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/6501cbe29195/10957_2020_1754_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/61272f518b60/10957_2020_1754_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/6af179545a73/10957_2020_1754_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/5c60b778a752/10957_2020_1754_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/7838e810c5ca/10957_2020_1754_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/0e747ba5e0de/10957_2020_1754_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/00d096f5f1f4/10957_2020_1754_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/08ac6b0d3a8a/10957_2020_1754_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/3408765ef8c2/10957_2020_1754_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/b670574c556d/10957_2020_1754_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/6501cbe29195/10957_2020_1754_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/61272f518b60/10957_2020_1754_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eb2/7525767/6af179545a73/10957_2020_1754_Fig10_HTML.jpg

相似文献

1
On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies.论目标在生物医学治疗房室模型优化中的作用
J Optim Theory Appl. 2020;187(2):305-335. doi: 10.1007/s10957-020-01754-2. Epub 2020 Sep 30.
2
Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).大分子拥挤现象:化学与物理邂逅生物学(瑞士阿斯科纳,2012年6月10日至14日)
Phys Biol. 2013 Aug;10(4):040301. doi: 10.1088/1478-3975/10/4/040301. Epub 2013 Aug 2.
3
Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force.约束优化方法在卫生服务研究中的应用:ISPOR 优化方法新兴良好实践工作组报告 2。
Value Health. 2018 Sep;21(9):1019-1028. doi: 10.1016/j.jval.2018.05.003.
4
Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment.确定 COVID-19 大流行治疗中疫苗接种的最佳控制策略。
Comput Methods Programs Biomed. 2020 Nov;196:105664. doi: 10.1016/j.cmpb.2020.105664. Epub 2020 Jul 19.
5
Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer.药物代谢动力学和定量系统药理学在癌症治疗中的应用:以 luminal A 型乳腺癌为例。
Pharmacol Res. 2017 Oct;124:20-33. doi: 10.1016/j.phrs.2017.07.015. Epub 2017 Jul 19.
6
Pharmacometrics in Pediatrics.儿科药物计量学
Ther Innov Regul Sci. 2019 Sep;53(5):579-583. doi: 10.1177/2168479019851793. Epub 2019 May 28.
7
Dynamic Optimization with Particle Swarms (DOPS): a meta-heuristic for parameter estimation in biochemical models.基于粒子群的动态优化(DOPS):一种用于生化模型参数估计的元启发式算法。
BMC Syst Biol. 2018 Oct 12;12(1):87. doi: 10.1186/s12918-018-0610-x.
8
A multiobjective optimization model and an orthogonal design-based hybrid heuristic algorithm for regional urban mining management problems.一种用于区域城市采矿管理问题的多目标优化模型及基于正交设计的混合启发式算法。
J Air Waste Manag Assoc. 2018 Feb;68(2):146-169. doi: 10.1080/10962247.2017.1386141. Epub 2018 Jan 16.
9
Optimization and Control of Agent-Based Models in Biology: A Perspective.生物学中基于主体模型的优化与控制:一种视角
Bull Math Biol. 2017 Jan;79(1):63-87. doi: 10.1007/s11538-016-0225-6. Epub 2016 Nov 8.
10
[Mathematical modeling of life history evolution: a brief history and main trends].[生活史进化的数学建模:简史与主要趋势]
Zh Obshch Biol. 2010 Jul-Aug;71(4):275-86.

引用本文的文献

1
Optimal control of multiple myeloma assuming drug resistance and off-target effects.考虑耐药性和脱靶效应的多发性骨髓瘤的最优控制
PLoS Comput Biol. 2025 Aug 11;21(8):e1012225. doi: 10.1371/journal.pcbi.1012225. eCollection 2025 Aug.
2
Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models.从新冠疫情中学习:数学疫苗优先排序模型的系统综述
Infect Dis Model. 2024 May 15;9(4):1057-1080. doi: 10.1016/j.idm.2024.05.005. eCollection 2024 Dec.
3
Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent.

本文引用的文献

1
Optimization of combination therapy for chronic myeloid leukemia with dosing constraints.慢性髓性白血病联合治疗的剂量限制优化
J Math Biol. 2018 Nov;77(5):1533-1561. doi: 10.1007/s00285-018-1262-6. Epub 2018 Jul 10.
2
On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth.具有广义 logistic 生长的肿瘤-免疫系统相互作用模型的最优化疗与强靶向药物。
Math Biosci Eng. 2013 Jun;10(3):787-802. doi: 10.3934/mbe.2013.10.787.
3
Optimal and suboptimal protocols for a mathematical model for tumor anti-angiogenesis in combination with chemotherapy.
小鼠实验模型表明,乳腺癌的最佳抗激素治疗依赖于饮食。
Bull Math Biol. 2024 Mar 18;86(4):42. doi: 10.1007/s11538-023-01253-1.
4
Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models.从新冠疫情中学习:数学疫苗优先级模型的系统综述
medRxiv. 2024 Mar 7:2024.03.04.24303726. doi: 10.1101/2024.03.04.24303726.
5
Treatment of evolving cancers will require dynamic decision support.不断演变的癌症的治疗将需要动态的决策支持。
Ann Oncol. 2023 Oct;34(10):867-884. doi: 10.1016/j.annonc.2023.08.008.
6
Optimal quarantine-related strategies for COVID-19 control models.用于新冠疫情控制模型的最佳检疫相关策略。
Stud Appl Math. 2021 Aug;147(2):622-649. doi: 10.1111/sapm.12393. Epub 2021 May 25.
肿瘤抗血管生成联合化疗的数学模型的最优和次优方案。
Math Biosci Eng. 2011 Apr;8(2):307-23. doi: 10.3934/mbe.2011.8.307.
4
Optimal response to chemotherapy for a mathematical model of tumor-immune dynamics.肿瘤-免疫动力学数学模型对化疗的最佳反应
J Math Biol. 2012 Feb;64(3):557-77. doi: 10.1007/s00285-011-0424-6. Epub 2011 May 8.
5
On optimal delivery of combination therapy for tumors.关于肿瘤联合治疗的最优化输送。
Math Biosci. 2009 Nov;222(1):13-26. doi: 10.1016/j.mbs.2009.08.004. Epub 2009 Aug 23.
6
Optimal and suboptimal protocols for a class of mathematical models of tumor anti-angiogenesis.一类肿瘤抗血管生成数学模型的最优与次优方案
J Theor Biol. 2008 May 21;252(2):295-312. doi: 10.1016/j.jtbi.2008.02.014. Epub 2008 Feb 16.
7
Tumour eradication by antiangiogenic therapy: analysis and extensions of the model by Hahnfeldt et al. (1999).抗血管生成疗法根除肿瘤:对哈恩费尔特等人(1999年)模型的分析与扩展
Math Biosci. 2004 Oct;191(2):159-84. doi: 10.1016/j.mbs.2004.06.003.
8
The three Es of cancer immunoediting.癌症免疫编辑的三个E
Annu Rev Immunol. 2004;22:329-60. doi: 10.1146/annurev.immunol.22.012703.104803.
9
Dynamic response of cancer under the influence of immunological activity and therapy.免疫活性和治疗影响下癌症的动态反应
J Theor Biol. 2004 Apr 7;227(3):335-48. doi: 10.1016/j.jtbi.2003.11.012.
10
Normalizing tumor vasculature with anti-angiogenic therapy: a new paradigm for combination therapy.用抗血管生成疗法使肿瘤血管正常化:联合治疗的新范例。
Nat Med. 2001 Sep;7(9):987-9. doi: 10.1038/nm0901-987.