CAREER: Engineering laminin globular domains for accelerated cardiomyocyte proliferation: validation with a 3D bioprinted in vitro infarct model

第一部分： 寻找工程生物材料的最佳候选物和组成是一项具有挑战性的任务，需要大量的实验成本和努力。为了设计用于心脏修复的有效的从头生物材料，由生物材料计划支持的CAREER奖旨在研究数据驱动方法的新生物材料设计策略。第一个目标是用计算机算法创建大量功能相关的蛋白质，这些蛋白质经过系统的筛选和选择。第二个目标是评估包括健康和梗塞状况的3D模型组织中的所选候选蛋白质库，其中模型组织用另一种计算机算法进行3D生物打印，以最大限度地减少实验试验和工作量。通过数据驱动的方法，PI寻求1）最佳的，新设计的治疗性蛋白质序列，以加速心肌细胞的增殖，2）生物材料和3D生物打印参数的最佳组合，以最小的实验成本制造模型组织。与传统方法相比，所提出的数据驱动方法能够适应改变的实验条件，以提高实验效率，达到生物材料特性的最佳目标。这个CAREER奖的结果可能会提供一个新的平台，工程治疗蛋白质和制造模型组织与最佳的成本和努力。建议开展外联活动和教育课程，以吸引从中学生到大学生的各种学生，促进这些学生在高度农业和石油化工工程为导向的工业环境中接触生物材料研究。 第二部分： 该职业奖旨在工程化细胞外基质（ECM）蛋白，以加速心肌细胞的增殖。一旦通过计算机算法创建了超过400万个功能相关的嵌合体，筛选和选择这些嵌合体在实验上是昂贵的。因此，CAREER奖中提出的新方法是利用公开可用的数据来构建机器学习模型来筛选和选择这些嵌合体，这些模型将建立在高斯过程的基础上，以从先验和观察中获得后验。为了避免机器学习与相对较少数量的公开可用数据的过拟合问题，将使用1）蛋白质折叠度量和2）基于细胞内蛋白质的测定构建额外的机器学习模型。为了评估设计的ECM蛋白的性能，将用3D生物打印制造梗死（边界区）的体外模型。为了以最少的3D生物打印试验次数概括边界区域的复杂特征，将应用贝叶斯优化来平衡整体搜索空间的探索和局部搜索空间的利用，以有效地实现最佳的3D生物打印和生物材料参数。为了明确识别模型边界区中心肌细胞的增殖，将在模型边界区中3D生物打印遗传标记的成年心肌细胞，以评估心肌细胞增殖的增加。该职业奖还旨在支持生物材料研究领域的STEM教育。该推广计划专门针对当地的中学和本科生，以吸引他们成为生物材料领域的未来劳动力，并优先考虑STEM教育中女性和代表性不足的群体。该项目由生物材料计划，材料研究部，激励竞争研究计划（EPSCoR）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。

PART 1: NON-TECHNICAL SUMMARY Finding optimal candidate and composition of engineered biomaterials is a challenging task with significant experimental cost and effort. To design effective, de novo biomaterials for cardiac repair, this CAREER award supported by the Biomaterials program aims to investigate a new biomaterials design strategy with data-driven approaches. The first objective is to create a large number of functionally relevant proteins with a computer algorithm, which are subject to systematic screening and selection. The second objective is to assess the selected pool of candidate proteins in a 3D model tissue including healthy and infarct conditions, where the model tissue is 3D bio-printed with another computer algorithm to minimize experimental trials and effort. With data-driven approaches, the PI seeks to attain 1) optimal, newly designed sequences of therapeutic proteins to accelerate the proliferation of cardiomyocytes and 2) optimal combinations of biomaterials and 3D bio-printing parameters to fabricate the model tissue with minimal experimental cost. In contrast to conventional approaches, the proposed data-driven approaches are adaptive to altered experimental conditions to reach the optimal target of biomaterial properties with enhanced experimental efficiency. The outcome of this CAREER award can potentially provide a novel platform of engineering therapeutic proteins and fabricating a model tissue with optimal cost and effort. Outreach activities and educational curricula are proposed to engage a range of students from middle school to undergraduate students to promote the exposure of biomaterials research to those students in highly agricultural and petrochemical engineering-oriented industrial settings. PART 2: TECHNICAL SUMMARY This CAREER award aims to engineer an extracellular matrix (ECM) protein to accelerate the proliferation of cardiomyocytes. Once over 4 million functionally relevant chimeras are created by a computer algorithm, screening and selection of those chimeras are experimentally expensive. Thus, the new approach proposed in the CAREER award is to utilize publicly available data to build machine learning models to screen and select those chimeras, which will be built upon a Gaussian Process to derive a posterior from a prior and observations. To avoid the overfitting problem of machine learning with the relatively smaller number of publicly available data, an additional machine learning model will be built with 1) protein folding metrics and 2) intracellular protein-based assay. To assess the performance of designer ECM proteins, an in vitro model of infarct (border zone) will be fabricated with 3D bio-printing. To recapitulate complex features of the border zone with minimal numbers of trials of 3D bio-printing, Bayesian Optimization will be applied to balance the exploration of the overall search space and the exploitation of the local search space to effectively achieve the optimal 3D bio-printing and biomaterial parameters. To unambiguously identify the proliferation of cardiomyocytes in the model border zone, genetically labeled adult cardiomyocytes will be 3D bio-printed in the model border zone to assess the increase of cardiomyocyte proliferation. This CAREER award also seeks to support STEM education in the areas of biomaterials research. The outreach program targets specifically local secondary school and undergraduate students to attract them into future workforce in biomaterials areas with priorities to female and underrepresented groups in STEM education.This project is jointly funded by the Biomaterials Program, Division of Materials Research, and the Established Program to Stimulate Competitive Research (EPSCoR).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.