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Yang J, Virostko J, Liu J, Jarrett AM, Hormuth DA, Yankeelov TE. Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation. Sci Rep 2023; 13:10387. [PMID: 37369672 DOI: 10.1038/s41598-023-37238-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
Glucose plays a central role in tumor metabolism and development and is a target for novel therapeutics. To characterize the response of cancer cells to blockade of glucose uptake, we collected time-resolved microscopy data to track the growth of MDA-MB-231 breast cancer cells. We then developed a mechanism-based, mathematical model to predict how a glucose transporter (GLUT1) inhibitor (Cytochalasin B) influences the growth of the MDA-MB-231 cells by limiting access to glucose. The model includes a parameter describing dose dependent inhibition to quantify both the total glucose level in the system and the glucose level accessible to the tumor cells. Four common machine learning models were also used to predict tumor cell growth. Both the mechanism-based and machine learning models were trained and validated, and the prediction error was evaluated by the coefficient of determination (R2). The random forest model provided the highest accuracy predicting cell dynamics (R2 = 0.92), followed by the decision tree (R2 = 0.89), k-nearest-neighbor regression (R2 = 0.84), mechanism-based (R2 = 0.77), and linear regression model (R2 = 0.69). Thus, the mechanism-based model has a predictive capability comparable to machine learning models with the added benefit of elucidating biological mechanisms.
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Affiliation(s)
- Jianchen Yang
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA
| | - Jack Virostko
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Junyan Liu
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA.
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA.
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA.
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA.
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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Bhatia R, Singh A, Narang RK. Fluorescence Resonance Energy Transfer (FRET) based Sensors: An Advanced Multifactorial Approach in Modern Analysis. Curr Pharm Des 2023; 29:2361-2365. [PMID: 37817653 DOI: 10.2174/0113816128255541231009092936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 10/12/2023]
Affiliation(s)
- Rohit Bhatia
- Department of Pharmaceutical Chemistry, Indo Soviet Friendship College of Pharmacy, GT Road, Ghall Kalan, Punjab, India
| | - Amandeep Singh
- Department of Pharmaceutics, Indo Soviet Friendship College of Pharmacy, GT Road, Ghall Kalan, Punjab, India
| | - Raj Kumar Narang
- Department of Pharmaceutics, Indo Soviet Friendship College of Pharmacy, GT Road, Ghall Kalan, Punjab, India
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Deepa Maheshvare M, Raha S, Pal D. A Graph-Based Framework for Multiscale Modeling of Physiological Transport. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 1:802881. [PMID: 36925576 PMCID: PMC10013063 DOI: 10.3389/fnetp.2021.802881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022]
Abstract
Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches in which it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here, we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective-dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element-based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro-in vivo studies and vascular topology from imaging studies for examining the structure-function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery.
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Affiliation(s)
| | | | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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Chen YI, Chang YJ, Liao SC, Nguyen TD, Yang J, Kuo YA, Hong S, Liu YL, Rylander HG, Santacruz SR, Yankeelov TE, Yeh HC. Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells. Commun Biol 2022; 5:18. [PMID: 35017629 PMCID: PMC8752789 DOI: 10.1038/s42003-021-02938-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.
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Affiliation(s)
- Yuan-I Chen
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yin-Jui Chang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Shih-Chu Liao
- ISS, Inc., 1602 Newton Drive, Champaign, IL, 61822, USA
| | - Trung Duc Nguyen
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Jianchen Yang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yu-An Kuo
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Soonwoo Hong
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yen-Liang Liu
- Master Program for Biomedical Engineering, China Medical University, Taichung, 406040, Taiwan
- Research Center for Cancer Biology, China Medical University, Taichung, 406040, Taiwan
| | - H Grady Rylander
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Samantha R Santacruz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hsin-Chih Yeh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, 78712, USA.
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