Models to predict protein biomaterial performance

预测蛋白质生物材料性能的模型

• 批准号: 8285441
• 负责人: Markus J. Buehler
• 金额: $ 64.7万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8285441
• 关键词: Address; Affect; Age; Amino Acid Sequence; Amino Acids; Animals; Anterior Cruciate Ligament; Architecture; Biocompatible Materials; Bladder; Blood Vessels; Boston; Cells; Cereals; Chemicals; Chemistry; Collagen; Computer Simulation; Cornea; Data; Data Set; Development; Disease; Elements; Engineering; Experimental Models; FDA approved; Failure; Fiber; Generic Drugs; Goals; Hernia; In Vitro; Lead; Length; Libraries; Link; Mechanics; Methods; Microfluidics; Modeling; Molecular; Molecular Structure; Molecular Weight; Morphology; Natural regeneration; Nerve; Oryctolagus cuniculus; Outcome; Performance; Polymers; Preparation; Process; Property; Proteins; Quantum Mechanics; Regenerative Medicine; Research Personnel; Resources; Rotator Cuff; Screening procedure; Series; Silk; Site; Solvents; Spiders; Stress; Structure; System; Tendon structure; Time; Tissues; Translations; Trauma; Universities; Weight-Bearing state; base; biodegradable polymer; cost; density; design; functional outcomes; improved; in vivo; insight; meetings; models and simulation; multi-scale modeling; particle; predictive modeling; programs; protein structure function; regenerative; repaired; research study; simulation; single molecule; subcutaneous; success; theories; tissue culture; tissue repair

DESCRIPTION (provided by applicant): There is a critical need to understand how tissue culture stimulation affects tissue construct development and function, with the ultimate goal of eliminating resource-intensive trial-and-error screening. Our goal is to develop predictive assessments of the in vivo performance of biomaterials so that a more rational approach based on a bottom-up modeling toolkit is used to guide the preparation of the required biomaterials. This new predictive approach would save time, animals, costs and accelerate the translation of such repair and regenerative systems. An important feature of our proposed approach is the direct integration of modeling and experimentation at multiple length scales, and the use of hierarchical material architectures across length scales, to reach enhanced material function. Our hypothesis is that predictions of biomaterials performance can be attained by the combined use of suitable experimental models to cover polymer features (chemistry, molecular weight), processing (fiber mechanical properties, hierarchical structure, degradation rate) and modeling at different length scales of materials structural hierarchy (from chemical to macroscopic). We have selected load bearing applications as the focus due to the generic needs in this field, such as for the anterior cruciate ligament, rotator cuff, bladder slings, hernia meshes, blood vessels, nerve guides and other tissues. Two well studied degradable polymer systems, silks and collagen, will be used for the experimental studies and model building, as they are directly amendable to highly controlled preparations and processing and cover a range of mechanical properties and degradation rates. In all cases, we build upon our extensive prior studies with these protein-based biomaterials, as well as developing hierarchical models of protein structure and function. The plans will be addressed in three Aims, (1) the in vitro preparation and characterization of the proteins in fiber-based biomaterials via microfluidic flow focusing, (2) development of multiscale models that span relevant length- and time-scales; including quantum mechanics, atomistic and molecular simulation, several coarse-grain and particle methods, and finite-element based continuum methods, and (3) in vivo characterization of fiber-based biomaterials to assess performance to refine the models. An interdisciplinary team of investigators will conduct the studies, including Markus Buehler (MIT) for multiscale modeling and simulation, David Kaplan (Tufts University) for polymer design/characterization and animal studies, and Joyce Wong (Boston University) for polymer processing/characterization. In all cases, strong preliminary data support all aspects of the planned study. What is unique in our multiscale approach is the intimate connection of experiment with simulation in a cohesive team. PUBLIC HEALTH RELEVANCE: Tissue failure, due to trauma, age and disease, lead to a major and growing demand for tissue replacement and regeneration strategies. At present, such approaches are hampered due to the inability to optimally design a biomaterial matrix to meet specific needs for a repair site. The proposed study would allow such predictions, by establishing a predictive modeling toolkit, upon which inputs such as the amino acid sequence or molecular weight, processing conditions (shear/flow details, pH/chemical and solvent changes during assembly can be used to predict outcomes such as hierarchical structure from the molecular level upwards as well as mechanical properties at all relevant scales (single molecule mechanics to tissue mechanics) and remodeling rate. This approach would allow the rational design of load-bearing biomaterial matrices to meet specific needs in regenerative medicine. Our main goal is load bearing biomaterials for tissue repair. However, the same "universal" library of elements (amino acids) forms materials as diverse as spider silk, tendon, cornea, blood vessels, or cells, each of which displays greatly variegated functional properties, we anticipate that our insights from the planned study would have broad impact and utility in a range of biomaterial and regenerative medicine needs.

描述（由申请人提供）：迫切需要了解组织培养刺激如何影响组织结构发育和功能，最终目标是消除资源密集型的试错筛选。我们的目标是开发生物材料在体内性能的预测评估，以便使用基于自下而上建模工具包的更合理的方法来指导所需生物材料的制备。这种新的预测方法将节省时间、动物和成本，并加速这种修复和再生系统的转化。我们提出的方法的一个重要特征是在多个长度尺度上直接集成建模和实验，并在长度尺度上使用分层材料架构，以达到增强的材料功能。我们的假设是，生物材料性能的预测可以通过结合使用合适的实验模型来实现，包括聚合物特征（化学，分子量），加工（纤维力学性能，层次结构，降解率）和材料结构层次（从化学到宏观）的不同长度尺度的建模。由于该领域的一般需求，我们选择了承重应用作为重点，例如前十字韧带，肩袖，膀胱吊带，疝网，血管，神经导尿管和其他组织。两种经过充分研究的可降解聚合物体系，蚕丝和胶原蛋白，将用于实验研究和模型构建，因为它们可以直接修改为高度控制的制备和加工，并且涵盖了一系列的机械性能和降解率。在所有情况下，我们都建立在对这些基于蛋白质的生物材料的广泛先前研究的基础上，以及开发蛋白质结构和功能的分层模型。该计划将在三个目标中得到解决，(1)通过微流控聚焦在体外制备和表征纤维基生物材料中的蛋白质，(2)开发跨越相关长度和时间尺度的多尺度模型；包括量子力学，原子和分子模拟，几种粗颗粒和颗粒方法，以及基于有限元的连续体方法，以及(3)纤维基生物材料的体内表征，以评估性能以完善模型。一个跨学科的研究团队将进行研究，包括Markus Buehler（麻省理工学院）的多尺度建模和模拟，David Kaplan（塔夫茨大学）的聚合物设计/表征和动物研究，以及Joyce Wong（波士顿大学）的聚合物加工/表征。在所有情况下，强有力的初步数据支持计划研究的各个方面。我们的多尺度方法的独特之处在于实验与模拟在一个有凝聚力的团队中的密切联系。