Models to predict protein biomaterial performance

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的发育和功能，最终目标是消除资源密集型反复试验和错误筛选。我们的目标是开发对生物材料体内性能的预测性评估，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。这种新的预测性方法将节省时间、动物和成本，并加快这种修复和再生系统的转换。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，并使用跨长度尺度的分层材料结构，以实现增强的材料功能。我们的假设是，生物材料性能的预测可以通过组合使用合适的实验模型来实现，这些模型涵盖了聚合物的特性(化学、分子质量)、加工(纤维机械性能、层次结构、降解率)以及在不同长度尺度的材料结构层次(从化学到宏观)的建模。由于这一领域的一般需求，我们选择了承载应用作为重点，如前十字韧带、肩袖、膀胱吊索、腹股沟、血管、神经导引和其他组织。两种经过充分研究的可降解聚合物系统，丝绸和胶原蛋白，将用于实验研究和模型建立，因为它们可以直接修改为高度受控的制备和加工，并涵盖一系列机械性能和降解率。在所有情况下，我们都建立在我们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。这些计划将涉及三个目标，(1)通过微流体聚焦体外制备和表征纤维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、几种粗粒和粒子方法以及基于有限元的连续介质方法，以及(3)纤维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这些研究，其中包括麻省理工学院的Markus Buehler(麻省理工学院)进行多尺度建模和模拟，David Kaplan(塔夫茨大学)进行聚合物设计/表征和动物研究，Joyce Wong(波士顿大学)进行聚合物加工/表征。在所有情况下，强劲的初步数据支持计划研究的所有方面。我们的多尺度方法的独特之处在于，在一个有凝聚力的团队中，实验与模拟的紧密联系。

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.