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Nisar KS, Kulachi MO, Ahmad A, Farman M, Saqib M, Saleem MU. Fractional order cancer model infection in human with CD8+ T cells and anti-PD-L1 therapy: simulations and control strategy. Sci Rep 2024; 14:16257. [PMID: 39009619 PMCID: PMC11251283 DOI: 10.1038/s41598-024-66593-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/02/2024] [Indexed: 07/17/2024] Open
Abstract
In order to comprehend the dynamics of disease propagation within a society, mathematical formulations are essential. The purpose of this work is to investigate the diagnosis and treatment of lung cancer in persons with weakened immune systems by introducing cytokines ( I L 2 & I L 12 ) and anti-PD-L1 inhibitors. To find the stable position of a recently built system TCD I L 2 I L 12 Z, a qualitative and quantitative analysis are taken under sensitive parameters. Reliable bounded findings are ensured by examining the generated system's boundedness, positivity, uniqueness, and local stability analysis, which are the crucial characteristics of epidemic models. The positive solutions with linear growth are shown to be verified by the global derivative, and the rate of impact across every sub-compartment is determined using Lipschitz criteria. Using Lyapunov functions with first derivative, the system's global stability is examined in order to evaluate the combined effects of cytokines and anti-PD-L1 inhibitors on people with weakened immune systems. Reliability is achieved by employing the Mittag-Leffler kernel in conjunction with a fractal-fractional operator because FFO provide continuous monitoring of lung cancer in multidimensional way. The symptomatic and asymptomatic effects of lung cancer sickness are investigated using simulations in order to validate the relationship between anti-PD-L1 inhibitors, cytokines, and the immune system. Also, identify the actual state of lung cancer control with early diagnosis and therapy by introducing cytokines and anti-PD-L1 inhibitors, which aid in the patients' production of anti-cancer cells. Investigating the transmission of illness and creating control methods based on our validated results will both benefit from this kind of research.
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Affiliation(s)
- Kottakkaran Sooppy Nisar
- Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia
- Saveetha School of Engineering, SIMATS, Chennai, India
| | | | - Aqeel Ahmad
- Department of Mathematics, Ghazi University, Dera Ghazi Khan, 32200, Pakistan
| | - Muhammad Farman
- Department of Mathematics, Mathematics Research Center, Near East University, Near East Boulevard, 99138, Nicosia, North Cyprus, Turkey.
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
| | - Muhammad Saqib
- Institute of Mathematics, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan
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2
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Garzón-Alvarado DA, Duque-Daza CA, Vaca-González JJ, Boucetta A, Linero DL, de Boer G, Das R, Ramtani S. Part II: A new perspective for modeling the bone remodeling process: Biology, mechanics, and pathologies. J Theor Biol 2024; 593:111894. [PMID: 38992463 DOI: 10.1016/j.jtbi.2024.111894] [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: 03/31/2024] [Revised: 06/13/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
In this paper, we explore the effects of biological (pathological) and mechanical damage on bone tissue within a benchmark model. Using the Finite Element Methodology, we analyze and numerically test the model's components, capabilities, and performance under physiologically and pathologically relevant conditions. Our findings demonstrate the model's effectiveness in simulating bone remodeling processes and self-repair mechanisms for micro-damage induced by biological internal conditions and mechanical external ones within bone tissue. This article is the second part of a series, where the first part presented the mathematical model and the biological and physical significance of the terms used in a simplified benchmark model. It explored the bone remodeling model's application, implementation, and results under physiological conditions.
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Affiliation(s)
- Diego A Garzón-Alvarado
- Biotechnology Institute, Universidad Nacional de Colombia, Colombia; GNUM, Universidad Nacional de Colombia, Colombia.
| | | | | | - Abdelkader Boucetta
- Laboratoire CSPBAT, equipe LBPS, CNRS (UMR 7244), Universit e Sorbonne Paris Nord, France
| | - Dorian L Linero
- Civil and Agricultural Department, Universidad Nacional de Colombia, Colombia.
| | - Gregory de Boer
- School of Mechanical Engineering, University of Leeds, United Kingdom
| | - Raj Das
- School of Engineering, RMIT University, Australia
| | - Salah Ramtani
- Laboratoire CSPBAT, equipe LBPS, CNRS (UMR 7244), Universit e Sorbonne Paris Nord, France.
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3
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Tavakoli N, Fong EJ, Coleman A, Huang YK, Bigger M, Doche ME, Kim S, Lenz HJ, Graham NA, Macklin P, Finley SD, Mumenthaler SM. Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595756. [PMID: 38826317 PMCID: PMC11142224 DOI: 10.1101/2024.05.24.595756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved high-throughput computational screening to investigate the effects of enzyme perturbations predicted by a computational model of CRC metabolism to understand system-wide effects efficiently. Our results highlighted hexokinase (HK) as one of the crucial targets, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF conditioned media exhibited increased sensitivity to HK inhibition. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.
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Affiliation(s)
- Niki Tavakoli
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Emma J. Fong
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Abigail Coleman
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Yu-Kai Huang
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Mathias Bigger
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | | | - Seungil Kim
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Heinz-Josef Lenz
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nicholas A. Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Shannon M. Mumenthaler
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
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4
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Ahmad A, Kulachi MO, Farman M, Junjua MUD, Bilal Riaz M, Riaz S. Mathematical modeling and control of lung cancer with IL2 cytokine and anti-PD-L1 inhibitor effects for low immune individuals. PLoS One 2024; 19:e0299560. [PMID: 38483931 PMCID: PMC10939278 DOI: 10.1371/journal.pone.0299560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
Mathematical formulations are crucial in understanding the dynamics of disease spread within a community. The aim of this work is to examine that the Lung Cancer detection and treatment by introducing IL2 and anti-PD-L1 inhibitor for low immune individuals. Mathematical model is developed with the created hypothesis to increase immune system by antibody cell's and Fractal-Fractional operator (FFO) is used to turn the model into a fractional order model. A newly developed system TCDIL2Z is examined both qualitatively and quantitatively in order to determine its stable position. The boundedness, positivity and uniqueness of the developed system are examined to ensure reliable bounded findings, which are essential properties of epidemic models. The global derivative is demonstrated to verify the positivity with linear growth and Lipschitz conditions are employed to identify the rate of effects in each sub-compartment. The system is investigated for global stability using Lyapunov first derivative functions to assess the overall impact of IL2 and anti-PD-L1 inhibitor for low immune individuals. Fractal fractional operator is used to derive reliable solution using Mittag-Leffler kernel. In fractal-fractional operators, fractal represents the dimensions of the spread of the disease and fractional represents the fractional ordered derivative operator. We use combine operators to see real behavior of spread as well as control of lung cancer with different dimensions and continuous monitoring. Simulations are conducted to observe the symptomatic and asymptomatic effects of Lung Cancer disease to verify the relationship of IL2, anti-PD-L1 inhibitor and immune system. Also identify the real situation of the control for lung cancer disease after detection and treatment by introducing IL2 cytokine and anti-PD-L1 inhibitor which helps to generate anti-cancer cells of the patients. Such type of investigation will be useful to investigate the spread of disease as well as helpful in developing control strategies from our justified outcomes.
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Affiliation(s)
- Aqeel Ahmad
- Department of Mathematics, Ghazi University, D G Khan, Pakistan
| | | | - Muhammad Farman
- Department of Mathematics, Faculty of Arts and Sciences, Near East University, Northern Cyprus, Turkey
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Moin-ud-Din Junjua
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Muhammad Bilal Riaz
- IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic
| | - Sidra Riaz
- Mathematical Research Center, Faculty of Arts and Sciences, Near East University, Northern Cyprus, Turkey
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5
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Miller HA, Miller DM, van Berkel VH, Frieboes HB. Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling. Ann Biomed Eng 2023; 51:820-832. [PMID: 36224485 PMCID: PMC10023290 DOI: 10.1007/s10439-022-03096-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/02/2022] [Indexed: 11/28/2022]
Abstract
The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
| | - Donald M Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
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6
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Ramtani S, Sánchez JF, Boucetta A, Kraft R, Vaca-González JJ, Garzón-Alvarado DA. A coupled mathematical model between bone remodeling and tumors: a study of different scenarios using Komarova's model. Biomech Model Mechanobiol 2023; 22:925-945. [PMID: 36922421 PMCID: PMC10167202 DOI: 10.1007/s10237-023-01689-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/05/2023] [Indexed: 03/17/2023]
Abstract
This paper aims to construct a general framework of coupling tumor-bone remodeling processes in order to produce plausible outcomes of the effects of tumors on the number of osteoclasts, osteoblasts, and the frequency of the bone turnover cycle. In this document, Komarova's model has been extended to include the effect of tumors on the bone remodeling processes. Thus, we explored three alternatives for coupling tumor presence into Komarova's model: first, using a "damage" parameter that depends on the tumor cell concentration. A second model follows the original structure of Komarova, including the tumor presence in those equations powered up to a new parameter, called the paracrine effect of the tumor on osteoclasts and osteoblasts; the last model is replicated from Ayati and collaborators in which the impact of the tumor is included into the paracrine parameters. Through the models, we studied their stability and considered some examples that can reproduce the tumor effects seen in clinic and experimentally. Therefore, this paper has three parts: the exposition of the three models, the results and discussion (where we explore some aspects and examples of the solution of the models), and the conclusion.
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Affiliation(s)
- Salah Ramtani
- Laboratoire CSPBAT, equipe LBPS, CNRS (UMR 7244), Universit e Sorbonne Paris Nord, Paris, France
| | | | - Abdelkader Boucetta
- Laboratoire CSPBAT, equipe LBPS, CNRS (UMR 7244), Universit e Sorbonne Paris Nord, Paris, France
| | - Reuben Kraft
- Department of Mechanical Engineering, Penn State University, University Park, USA
| | - Juan Jairo Vaca-González
- Escuela de Pregrado - Direccion Académica, Universidad Nacional de Colombia, Sede de La Paz, Cesar, Colombia
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7
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Comparison of Predator-Prey Model and Hawk-Dove Game for Modelling Leukemia. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9957514. [PMID: 36188674 PMCID: PMC9522487 DOI: 10.1155/2022/9957514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Game theory is an excellent mathematical tool to describe the interaction between the immune system and cancerous leukocytes (c.leu). The feature of cancerous leukocytes to differentiate and mutate to give rise to leukemia is in the domain of ecological models as well. In this work, the dynamic of leukemia is described and compared by two models: firstly by a simple probabilistic mathematical model using the zero-sum two player game of Hawk and Dove, and secondly by Leslie Predator Prey model of ecology. The main goal of this study is to compare the results of both models and then discuss the treatment of leukemia i.e., Hematopoietic Stem cell transplant with the best model among them. Hawk and Dove model also describes the cell to cell interaction of cancerous leukocytes and healthy leukocytes (leu) after diagnoses and the condition of the patient before and after treatments. In this work, Hematopoietic Stem cell transplant is discussed by using concepts of a zero-sum three player game. Also, both models will be characterized by determining the stability properties, identifying basins of attraction, and locating the equilibrium points to see, at what extent the patient's survival is possible with leukemia in its body. Results for both models will be presented graphically.
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8
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.,Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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9
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Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data. Ann Biomed Eng 2022; 50:314-329. [PMID: 35083584 PMCID: PMC9743982 DOI: 10.1007/s10439-022-02904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/01/2022] [Indexed: 12/15/2022]
Abstract
Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.
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10
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Mehrpooya A, Saberi-Movahed F, Azizizadeh N, Rezaei-Ravari M, Saberi-Movahed F, Eftekhari M, Tavassoly I. High dimensionality reduction by matrix factorization for systems pharmacology. Brief Bioinform 2022; 23:bbab410. [PMID: 34891155 PMCID: PMC8898012 DOI: 10.1093/bib/bbab410] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/20/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
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Affiliation(s)
- Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Najmeh Azizizadeh
- Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Iran
| | - Mohammad Rezaei-Ravari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA
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11
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Jia D, Park JH, Kaur H, Jung KH, Yang S, Tripathi S, Galbraith M, Deng Y, Jolly MK, Kaipparettu BA, Onuchic JN, Levine H. Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer. Br J Cancer 2021; 124:1902-1911. [PMID: 33859341 DOI: 10.1038/s41416-021-01385-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/17/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. A developmental programme known as epithelial-to-mesenchymal transition (EMT) plays a critical role during metastasis, promoting the loss of polarity and cell-cell adhesion and the acquisition of motile, stem-cell characteristics. Cells undergoing EMT or the reverse mesenchymal-to-epithelial transition (MET) are often associated with metabolic changes, as the change in phenotype often correlates with a different balance of proliferation versus energy-intensive migration. Extensive crosstalk occurs between metabolism and EMT, but how this crosstalk leads to coordinated physiological changes is still uncertain. The elusive connection between metabolism and EMT compromises the efficacy of metabolic therapies targeting metastasis. In this review, we aim to clarify the causation between metabolism and EMT on the basis of experimental studies, and propose integrated theoretical-experimental efforts to better understand the coupled decision-making of metabolism and EMT.
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Affiliation(s)
- Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.
| | - Jun Hyoung Park
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Harsimran Kaur
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka, India
| | - Kwang Hwa Jung
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sukjin Yang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Shubham Tripathi
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,PhD Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA.,Center for Theoretical Biological Physics and Department of Physics, Northeastern University, Boston, MA, USA
| | - Madeline Galbraith
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Department of Physics and Astronomy, Rice University, Houston, TX, USA
| | - Youyuan Deng
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.,Applied Physics Graduate Program, Rice University, Houston, TX, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka, India
| | - Benny Abraham Kaipparettu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. .,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA. .,Department of Physics and Astronomy, Rice University, Houston, TX, USA. .,Department of Chemistry, Rice University, Houston, TX, USA. .,Department of Biosciences, Rice University, Houston, TX, USA.
| | - Herbert Levine
- Center for Theoretical Biological Physics and Department of Physics, Northeastern University, Boston, MA, USA. .,Department of Bioengineering, Northeastern University, Boston, MA, USA.
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12
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Sánchez-Jiménez F, Medina MÁ, Villalobos-Rueda L, Urdiales JL. Polyamines in mammalian pathophysiology. Cell Mol Life Sci 2019; 76:3987-4008. [PMID: 31227845 PMCID: PMC11105599 DOI: 10.1007/s00018-019-03196-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/11/2019] [Accepted: 06/14/2019] [Indexed: 02/07/2023]
Abstract
Polyamines (PAs) are essential organic polycations for cell viability along the whole phylogenetic scale. In mammals, they are involved in the most important physiological processes: cell proliferation and viability, nutrition, fertility, as well as nervous and immune systems. Consequently, altered polyamine metabolism is involved in a series of pathologies. Due to their pathophysiological importance, PA metabolism has evolved to be a very robust metabolic module, interconnected with the other essential metabolic modules for gene expression and cell proliferation/differentiation. Two different PA sources exist for animals: PA coming from diet and endogenous synthesis. In the first section of this work, the molecular characteristics of PAs are presented as determinant of their roles in living organisms. In a second section, the metabolic specificities of mammalian PA metabolism are reviewed, as well as some obscure aspects on it. This second section includes information on mammalian cell/tissue-dependent PA-related gene expression and information on crosstalk with the other mammalian metabolic modules. The third section presents a synthesis of the physiological processes described as modulated by PAs in humans and/or experimental animal models, the molecular bases of these regulatory mechanisms known so far, as well as the most important gaps of information, which explain why knowledge around the specific roles of PAs in human physiology is still considered a "mysterious" subject. In spite of its robustness, PA metabolism can be altered under different exogenous and/or endogenous circumstances so leading to the loss of homeostasis and, therefore, to the promotion of a pathology. The available information will be summarized in the fourth section of this review. The different sections of this review also point out the lesser-known aspects of the topic. Finally, future prospects to advance on these still obscure gaps of knowledge on the roles on PAs on human physiopathology are discussed.
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Affiliation(s)
- Francisca Sánchez-Jiménez
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Andalucía Tech, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain
- UNIT 741, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain
| | - Miguel Ángel Medina
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Andalucía Tech, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain
- UNIT 741, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain
| | - Lorena Villalobos-Rueda
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Andalucía Tech, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain
| | - José Luis Urdiales
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, Andalucía Tech, and IBIMA (Biomedical Research Institute of Málaga), Málaga, Spain.
- UNIT 741, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain.
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Rockne RC, Hawkins-Daarud A, Swanson KR, Sluka JP, Glazier JA, Macklin P, Hormuth DA, Jarrett AM, Lima EABF, Tinsley Oden J, Biros G, Yankeelov TE, Curtius K, Al Bakir I, Wodarz D, Komarova N, Aparicio L, Bordyuh M, Rabadan R, Finley SD, Enderling H, Caudell J, Moros EG, Anderson ARA, Gatenby RA, Kaznatcheev A, Jeavons P, Krishnan N, Pelesko J, Wadhwa RR, Yoon N, Nichol D, Marusyk A, Hinczewski M, Scott JG. The 2019 mathematical oncology roadmap. Phys Biol 2019; 16:041005. [PMID: 30991381 PMCID: PMC6655440 DOI: 10.1088/1478-3975/ab1a09] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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Affiliation(s)
- Russell C Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA 91010, United States of America. Author to whom any correspondence should be addressed
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Stiehl T, Marciniak-Czochra A. How to Characterize Stem Cells? Contributions from Mathematical Modeling. CURRENT STEM CELL REPORTS 2019. [DOI: 10.1007/s40778-019-00155-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways. Proc Natl Acad Sci U S A 2019; 116:3909-3918. [PMID: 30733294 DOI: 10.1073/pnas.1816391116] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities. Previously, by modeling the gene regulation of cancer metabolism we have reported that cancer cells can acquire a stable hybrid metabolic state in which both glycolysis and OXPHOS can be used. Here, to comprehensively characterize cancer metabolic activity, we establish a theoretical framework by coupling gene regulation with metabolic pathways. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis, respectively, with the activities of three major metabolic pathways: glucose oxidation, glycolysis, and fatty acid oxidation. Our model further characterizes the hybrid metabolic state and a metabolically inactive state where cells have low activity of both glycolysis and OXPHOS. We verify the model prediction using metabolomics and transcriptomics data from paired tumor and adjacent benign tissue samples from a cohort of breast cancer patients and RNA-sequencing data from The Cancer Genome Atlas. We further validate the model prediction by in vitro studies of aggressive triple-negative breast cancer (TNBC) cells. The experimental results confirm that TNBC cells can maintain a hybrid metabolic phenotype and targeting both glycolysis and OXPHOS is necessary to eliminate their metabolic plasticity. In summary, our work serves as a platform to symmetrically study how tuning gene activity modulates metabolic pathway activity, and vice versa.
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