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Del Pino Herrera A, Ferrall-Fairbanks MC. A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies. Front Genet 2024; 15:1383676. [PMID: 38873108 PMCID: PMC11169904 DOI: 10.3389/fgene.2024.1383676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
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Affiliation(s)
- Adriana Del Pino Herrera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- University of Florida Health Cancer Center, University of Florida, Gainesville, FL, United States
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Sun T, Feng Q. Evolutionary game of environmental investment under national environmental regulation in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:53432-53443. [PMID: 34031832 DOI: 10.1007/s11356-021-14548-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/19/2021] [Indexed: 06/12/2023]
Abstract
With the rapid development of social economy, the deterioration of environment has become more and more serious; it is urgent to find a balance between economic development and environmental protection. Therefore, enterprises are appealed to invest in environmental protection, and local municipalities are appealed to supervise the environmental behaviors of enterprises, while central government plays a role of regulation. In order to study the preconditions for a better environmental strategy combination of local municipalities and enterprises, an evolutionary game theory was first constructed in this paper, then the evolutionary stable strategy (ESS) of local municipalities and enterprises under different scenarios was examined, and finally a simulation was used to test the results of the analysis. The results indicate that central government's regulation has influence on the strategies chosen by local municipalities and enterprises, the larger the degree of central government's incentives are, the more possible local municipalities and enterprises would choose environment-friendly strategies; and the intensity of central government's regulation, the cost and benefits of local municipalities' supervision, and the cost and benefits of enterprises' environmental investment are the key influence factors.
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Affiliation(s)
- Tao Sun
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China.
| | - Qiang Feng
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Liu W, Yang J, Bi K. Factors Influencing Private Hospitals' Participation in the Innovation of Biomedical Engineering Industry: A Perspective of Evolutionary Game Theory. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E7442. [PMID: 33066099 PMCID: PMC7600621 DOI: 10.3390/ijerph17207442] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/29/2020] [Accepted: 10/06/2020] [Indexed: 12/28/2022]
Abstract
The innovation of the biomedical engineering (BME) industry is inseparable from its cooperation with medical institutions. China has considerable medical institutions. Although private hospitals account for more than half of Chinese medical institutions, they rarely participate in biomedical engineering industry innovation. This paper analyzed the collaborative relationship among biomedical engineering enterprises, universities, research institutes, public hospitals and private hospitals through evolutionary game theory and discussed the influence of different factors on the collaborative innovation among them. A tripartite evolutionary game model is established which regards private hospitals as a stakeholder. The results show that (1) the good credit of private hospitals has a positive effect on their participation in collaborative innovation; (2) it is helpful for BME collaborative innovation to enhance the collaborative innovation ability of partners; (3) the novelty of innovation projects has an impact on BME collaborative innovation. The specific impacts depend on the revenue, cost and risk allocation ratio of innovation partners; (4) the higher the practicability of innovation projects, the more conducive to collaborative innovation.
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Affiliation(s)
- Weiwei Liu
- School of Economics and Management, Harbin Engineering University, Harbin 150001, China; (J.Y.); (K.B.)
- Management School, Queen’s University Belfast, Belfast BT9 5EE, UK
| | - Jianing Yang
- School of Economics and Management, Harbin Engineering University, Harbin 150001, China; (J.Y.); (K.B.)
| | - Kexin Bi
- School of Economics and Management, Harbin Engineering University, Harbin 150001, China; (J.Y.); (K.B.)
- School of Management, Harbin University of Science and Technology, Harbin 150080, China
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105443. [PMID: 32311510 DOI: 10.1016/j.cmpb.2020.105443] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/17/2020] [Accepted: 03/08/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients. METHODS The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule. RESULTS Computational results confirm that the optimal chemotherapy schedule depends on some patient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size. CONCLUSION The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - Madjid Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1969764499, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Khazaei B, Sartakhti JS, Manshaei MH, Zhu Q, Sadeghi M, Mousavi SR. HIV-1-infected T-cells dynamics and prognosis: An evolutionary game model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 152:1-14. [PMID: 29054249 DOI: 10.1016/j.cmpb.2017.08.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 07/01/2017] [Accepted: 08/21/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection. METHODS We propose a novel structure-based game-theoretic model for the evolution of HIV-1-Infected CD4+T-cells and invasion of the immune system. We discuss the theoretical basis of the stable equilibrium states of the evolutionary dynamics of four T-cells types as well as its significant results to understand and control HIV infection. The results include the importance of genetic variations and the process of establishing evolutionary dynamics of the virus quasispecies. RESULTS Our results show that there is a direct dependency between some parameters such as mutation rates and the stability of equilibrium states in the HIV infection. This is an interesting result because these parameters can be changed by some pharmacotherapies and alternative treatments. Our model indicates that in an appropriate treatment the relative frequency of the wild type of virus quasispecies can be decreased in the population. Consequently, this can cause delaying the emergence of the AIDS phase. To assess the model, we investigate two new treatments for HIV. The results show that our model can predict the treatment results. CONCLUSIONS The paper shows that a structured-based evolutionary game theory can model the evolutionary dynamics of the infected T-cells and virus quasispecies. The model predicts certain aspects of the HIV infection process under several treatments.
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Affiliation(s)
- Bahareh Khazaei
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | | | - Mohammad Hossein Manshaei
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Quanyan Zhu
- Department of Electrical and Computer Engineering, Polytechnic School of Engineering, New York University, NY, USA
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology and the School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Seyed Rasoul Mousavi
- Computer Engineering Department, Amirkabir University of Technology and the Institute for Research in Fundamental Sciences, Tehran, Iran
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Zangooei MH, Habibi J. Hybrid multiscale modeling and prediction of cancer cell behavior. PLoS One 2017; 12:e0183810. [PMID: 28846712 PMCID: PMC5573302 DOI: 10.1371/journal.pone.0183810] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 08/13/2017] [Indexed: 12/03/2022] Open
Abstract
Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
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Affiliation(s)
| | - Jafar Habibi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- * E-mail:
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Tellez-Gabriel M, Charrier C, Brounais-Le Royer B, Mullard M, Brown HK, Verrecchia F, Heymann D. Analysis of gap junctional intercellular communications using a dielectrophoresis-based microchip. Eur J Cell Biol 2017; 96:110-118. [DOI: 10.1016/j.ejcb.2017.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 07/29/2016] [Accepted: 01/09/2017] [Indexed: 01/20/2023] Open
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