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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01EB014976-02
Application #
8476216
Study Section
Special Emphasis Panel (ZEB1-OSR-C (J1))
Program Officer
Peng, Grace
Project Start
2012-06-01
Project End
2017-05-31
Budget Start
2013-06-01
Budget End
2014-05-31
Support Year
2
Fiscal Year
2013
Total Cost
$573,497
Indirect Cost
$64,237
Name
Tufts University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
073134835
City
Medford
State
MA
Country
United States
Zip Code
02155
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