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: University of Houston
• 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
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953535&HistoricalAwards=false
• 关键词: Fusion; Inducing; Liposomes; Efficient; Intracellular

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.

该项目将数学和计算科学家与生物医学工程师聚集在一起，解决了对一个简单，安全和有效的系统的未满足和不断增长的需求，以将大分子传递到细胞内部。治疗性大分子，例如肽和蛋白质，在医学领域具有巨大的潜力，但它们的临床应用仍然有限，因为与小分子治疗剂相比，它们的递送更具挑战性。一个有前途的载体家族，称为融合脂质体，遭受了一个关键的打击：穿过细胞膜屏障所需的高浓度融合脂质导致体内毒性。这种限制可以通过产生含有相对低浓度的融合脂质但可以在其表面上以密集的斑块呈现它们的脂质体来克服。这可以通过膜相分离来实现，膜相分离是生物膜经常用于局部浓缩特定脂质物质的机制。该项目将应用互补的数学，计算和实验工具（i）设计和开发一类新的脂质体载体，称为斑块融合脂质体（PFL），以及（ii）研究融合斑块如何影响PFL与细胞膜融合的能力。更广泛的影响包括为参与的学生提供培训机会。本科生和研究生将接受培训，在实验生物工程，应用数学和科学计算的接口工作。跨学科的对话将通过密切的互动和联席会议来促进。使用大规模的数值实验来支持和补充脂质体设计中的实验实践，需要高效和计算成本低廉的数值方法。尽管最近的进展，分子动力学和粗粒模型仍然具有高计算成本。该项目将专注于复杂的新型连续模型，并将其与数值算法和数据分析工具相结合，以产生一个高效的计算平台。考虑中的多物理场模型考虑了横向相分离、膜流动性和静电相互作用，其预测能力将通过多阶段验证过程根据实验数据进行评估。高的计算效率，在实施该模型将实现通过基于物理和定向分裂算法的一个强大的几何非拟合有限元法。一旦确认，该软件将被系统地部署，以调查膜融合的关键PFL特征的作用。最终，该项目将提供PFL的设计，其特征在于最少量的融合成分，同时最大限度地提高与其他膜融合的机会。为该项目开发的表面偏微分方程和耦合表面-体系统的有效计算方法可用于各种各样的应用，从模拟肿瘤生长到真核细胞运动的建模。此外，新开发的软件将是开源的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Interpolatory tensorial reduced order models for parametric dynamical systems

参数动力系统的插值张量降阶模型

• 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

A cutFEM divergence–free discretization for the stokes problem

斯托克斯问题的 cutFEM 散度 — 自由离散化

• DOI: 10.1051/m2an/2022072
• 发表时间: 2023
• 期刊: ESAIM: Mathematical Modelling and Numerical Analysis
• 作者: Liu, Haoran;Neilan, Michael;Olshanskii, Maxim
• 通讯作者: Olshanskii, Maxim