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Fusion-Inducing Liposomes for Efficient Intracellular Delivery: Continuum Models and Experiments

Fusion-Inducing Liposomes for Efficient Intracellular Delivery: Continuum Models and Experiments
用于高效细胞内递送的融合诱导脂质体:连续体模型和实验
批准号:
1953535
负责人:
Annalisa Quaini
金额:
$48.15万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-07-01 至 2024-06-30

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中文摘要
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英文摘要
Bringing mathematical and computational scientists together with biomedical engineers, this project addresses an unmet and growing need for a simple, safe, and efficient system to deliver macromolecules to the cellular interior. Therapeutic macromolecules, for example, peptides and proteins, have tremendous potential in medical fields but their clinical applications have remained limited as their delivery is much more challenging compared to small molecule therapeutics. A promising family of carriers, called fusogenic liposomes, suffer from a key shotcoming: the high concentrations of fusogenic lipids needed to cross cellular membrane barriers lead to toxicity in vivo. This limitation may be overcome by creating liposomes that contain relatively low concentrations of fusogenic lipids but can present them in dense patches on their surfaces. This may be achieved through membrane phase-separation, a mechanism that biological membranes often use to locally concentrate specific lipid species. This project will apply complementary mathematical, computational, and experimental tools to (i) design and develop a new class of liposomal carriers, called patchy fusogenic liposomes (PFLs), and (ii) investigate how the fusogenic patches affect the ability of PFLs to fuse with cellular membranes. Broader impacts include training opportunities for participating students. Undergraduate and graduate students will be trained to work at the interface of experimental bioengineering, applied mathematics, and scientific computing. Cross-disciplinary conversations will be fostered by close interactions and joint meetings.The use of massive numerical experimentation to support and complement experimental practice in the design of liposomes requires highly efficient and computationally cheap numerical methods. Despite recent advances, molecular dynamics and coarse grain models still feature high computational costs. This project will focus on sophisticated novel continuum models and combine them with numerical algorithms and data analysis tools to produce a highly efficient computational platform. The multiphysics model under consideration accounts for lateral phase-separation, membrane fluidity, and electrostatic interaction and its predictive capability will be assessed against experimental data through a multi-stage validation process. High computational efficiency in implementing the model will be achieved through physics-based and directional splitting algorithms for a robust geometrically unfitted finite element method. Once validated, the software will be systematically deployed to investigate the role of critical PFL characteristics for membrane fusion. Ultimately, this project will deliver the design of PFLs that feature minimal amounts of fusogenic components while maximizing the chances of fusion with other membranes. The efficient computational methods for surface PDEs and coupled surface-bulk systems developed for this project could be used for a large variety of applications, from simulations of tumor growth to modeling of eukaryotic cell motility. In addition, the newly developed software will be open source.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(26)
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会议论文
DOI: 10.1016/j.cma.2022.115122
发表时间: 2022
期刊: Computer Methods in Applied Mechanics and Engineering
影响因子: 7.2
作者: [Mamonov, Alexander V., Olshanskii, Maxim A.]
通讯作者: Olshanskii, Maxim A.
A POD-Galerkin reduced order model for the Navier–Stokes equations in stream function-vorticity formulation
流函数涡度公式中纳维斯托克斯方程的 POD-Galerkin 降阶模型
DOI: 10.1016/j.compfluid.2022.105536
发表时间: 2022
期刊: Computers & Fluids
影响因子: 2.8
作者: [Girfoglio, Michele, Quaini, Annalisa, Rozza, Gianluigi]
通讯作者: Rozza, Gianluigi
A Comparison of Cahn–Hilliard and Navier–Stokes–Cahn–Hilliard Models on Manifolds
Cahn-Hilliard 和 Navier-Stokes-Cahn-Hilliard 流形模型的比较
DOI: 10.1007/s10013-022-00564-5
发表时间: 2022
期刊: Vietnam Journal of Mathematics
影响因子: 0.8
作者: [Olshanskii, Maxim, Palzhanov, Yerbol, Quaini, Annalisa]
通讯作者: Quaini, Annalisa
Validation of an OpenFOAM®-based solver for the Euler equations with benchmarks for mesoscale atmospheric modeling
使用中尺度大气建模基准验证基于 OpenFOAM® 的欧拉方程求解器
DOI: 10.1063/5.0147457
发表时间: 2023
期刊: AIP Advances
影响因子: 1.6
作者: [Girfoglio, Michele, Quaini, Annalisa, Rozza, Gianluigi]
通讯作者: Rozza, Gianluigi
24
    Conference: Power of Diversity in Uncertainty Quantification (PoD UQ)
    • 批准号:
      2403506
    • 项目类别:
      Standard Grant
    • 资助金额:
      $2.35万
    • 财政年份:
      2024
    • 负责人:
      Annalisa Quaini
    • 依托单位:
    Collaborative Research: Efficient Modeling of Incompressible Fluid Dynamics at Moderate Reynolds Numbers by Deconvolution LES Filters - Analysis and Applications to Hemodynamics
    • 批准号:
      1620384
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $17.99万
    • 财政年份:
      2016
    • 负责人:
      Annalisa Quaini
    • 依托单位:
    国内基金
    海外基金
    基础于人参提取的促醒(Wakefulness-inducing)药物研究