Paul Egan

The role of mechanics in biological and bio-inspired systems

Natural systems frequently exploit intricate multiscale and multiphasic structures to achieve functionalities beyond those of man-made systems. Although understanding the chemical make-up of these systems is essential, the passive and active mechanics within biological systems are crucial when considering the many natural systems that achieve advanced properties, such as high strength-to-weight ratios and stimuli-responsive adaptability. Discovering how and why biological systems attain these desirable mechanical functionalities often reveals principles that inform new synthetic designs based on biological systems. Such approaches have traditionally found success in medical applications, and are now informing breakthroughs in diverse frontiers of science and engineering.

A Generalized Optimality Criteria Method for Optimization of Additively Manufactured Multimaterial Lattice Structures

Recent progress in Additive Manufacturing (AM) allows for printing customized products with multiple materials and complex geometries that could form the basis of multi-material designs with high performance and novel functions. Effectively designing such complex products for optimal performance within the confines of additive manufacturing constraints is challenging due to the need to consider fabrication constraints while searching for optimal designs with a large number of variables, which stem from new AM capabilities. In this study, fabrication constraints are addressed through empirically characterizing multiple printed materials’ Young’s modulus and density using a multi-material inkjet-based 3D-printer. Data curves are modeled for the empirical data describing two base printing materials and twelve mixtures of them as inputs for a computational optimization process. An optimality criteria method is developed to search for solutions of multi-material lattices with fixed topology and truss cross-section sizes. Two representative optimization studies are presented and demonstrate higher performance with multi-material approaches in comparison to using a single material. These include the optimization of a cubic lattice structure that must adhere to a fixed displacement constraint and a compliant beam lattice structure that must meet multiple fixed displacement constraints. Results demonstrate the feasibility of the approach as a general synthesis and optimization method for multi-material, lightweight lattice structures that are large-scale and manufacturable on a commercial AM printer directly from the design optimization results.

Design of Complex Biologically Based Nanoscale Systems Using Multi-Agent Simulations and Structure–Behavior–Function Representations

The process of designing integrated biological systems across scales is difficult, with challenges arising from the modeling, understanding, and search of complex system design spaces. This paper explores these challenges through consideration of how stochastic nanoscale phenomenon relate to higher level systems functioning across many scales. A domain-independent methodology is introduced which uses multi-agent simulations to predict emergent system behavior and structure‐behavior‐function (SBF) representations to facilitate design space navigation. The methodology is validated through a nanoscale design application of synthetic myosin motor systems. In the multi-agent simulation, myosins are independent computational agents with varied structural inputs that enable differently tuned mechanochemical behaviors. Four synthetic myosins were designed and replicated as agent populations, and their simulated behavior was consistent with empirical studies of individual myosins and the macroscopic performance of myosin-powered muscle contractions. However, in order to configure high performance technologies, designers must effectively reason about simulation inputs and outputs; we find that counter-intuitive relations arise when linking system performance to individual myosin structures. For instance, one myosin population had a lower system force even though more myosins contributed to system-level force. This relationship is elucidated with SBF by considering the distribution of structural states and behaviors in agent populations. For the lower system force population, it is found that although more myosins are producing force, a greater percentage of the population produces negative force. The success of employing SBF for understanding system interactions demonstrates how the methodology may aid designers in complex systems embodiment. The methodology’s domain-independence promotes its extendibility to similar complex systems, and in the myosin test case the approach enabled the reduction of a complex physical phenomenon to a design space consisting of only a few critical parameters. The methodology is particularly suited for complex systems with many parts operating stochastically across scales, and should prove invaluable for engineers facing the challenges of biological nanoscale design, where designs with unique properties require novel approaches or useful configurations in nature await discovery. [DOI: 10.1115/1.4024227]

Computationally designed lattices with tuned properties for tissue engineering using 3D printing

Tissue scaffolds provide structural support while facilitating tissue growth, but are challenging to design due to diverse property trade-offs. Here, a computational approach was developed for modeling scaffolds with lattice structures of eight different topologies and assessing properties relevant to bone tissue engineering applications. Evaluated properties include porosity, pore size, surface-volume ratio, elastic modulus, shear modulus, and permeability. Lattice topologies were generated by patterning beam-based unit cells, with design parameters for beam diameter and unit cell length. Finite element simulations were conducted for each topology and quantified how elastic modulus and shear modulus scale with porosity, and how permeability scales with porosity cubed over surface-volume ratio squared. Lattices were compared with controlled properties related to porosity and pore size. Relative comparisons suggest that lattice topology leads to specializations in achievable properties. For instance, Cube topologies tend to have high elastic and low shear moduli while Octet topologies have high shear moduli and surface-volume ratios but low permeability. The developed method was utilized to analyze property trade-offs as beam diameter was altered for a given topology, and used to prototype a 3D printed lattice embedded in an interbody cage for spinal fusion treatments. Findings provide a basis for modeling and understanding relative differences among beam-based lattices designed to facilitate bone tissue growth.

Design of Hierarchical Three-Dimensional Printed Scaffolds Considering Mechanical and Biological Factors for Bone Tissue Engineering

Computational approaches have great potential for aiding clinical product development by finding promising candidate designs prior to expensive testing and clinical trials. Here, an approach for designing multilevel bone tissue scaffolds that provide structural support during tissue regeneration is developed by considering mechanical and biological perspectives. Three key scaffold design properties are considered: (1) porosity, which influences potential tissue growth volume and nutrient transport, (2) surface area, which influences biodegradable scaffold dissolution rate and initial cell attachment, and (3) elastic modulus, which influences scaffold deformation under load and, therefore, tissue stimulation. Four scaffold topology types are generated by patterning beam or trussbased unit cells continuously or hierarchically and tuning the element diameter, unit cell length, and number of unit cells. Parametric comparisons suggest that structures with truss-based scaffolds have higher surface areas but lower elastic moduli for a given porosity in comparison to beam-based scaffolds. Hierarchical scaffolds possess a large central pore that increases porosity but lowers elastic moduli and surface area. Scaffold samples of all topology types are 3D printed with dimensions suitable for scientific testing. A hierarchical scaffold is fabricated with dimensions and properties relevant for a spinal interbody fusion cage with a maximized surface-volume ratio, which illustrates a potentially high performing design configured for mechanical and biological factors. These findings demonstrate the merit in using multidisciplinary and computational approaches as a foundation of tissue scaffold development for regenerative medicine. [DOI: 10.1115/1.4036396]

Emergent systems energy laws for predicting myosin ensemble processivity.

In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems.

Cognitive-Based Search Strategies for Complex Bio-Nanotechnology Design Derived Through Symbiotic Human and Agent-Based Approaches

Complex systems are challenging for engineers to understand and design. This work demonstrates a synergistic cognitive and agent-based methodology for developing and implementing rule-based strategies that improve human search performance in optimization design tasks. The domain of our study is the design of synthetic myosin-based systems, the biologically-based building block of muscle. We began with an initial cognitive study of users solving design tasks with three varied difficulties using a graphical user interface, and tracked how they manipulated design variables in their search process. User search behaviors resulting in the best and worst designs were then examined. Trends were identified that were used to formulate three strategies automated by computational agents solving the same tasks as the users. The most successful identified strategy implemented by the agents was a combination of univariate searches to learn parameter relationships and then applying that knowledge in greedy local searches. On one of the three tasks, an initial random search improved results. A subsequent cognitive study was conducted with users implementing the best agent-tested strategies. Users implementing the strategy performed significantly better than users performed in the first study with no provided strategy. These results show the power of synergistic human and agent-based approaches, in that cognitive-based findings can provide a starting place for computational search algorithms to begin testing strategies. Experimentation through agent-based methods via fast and extensive automated searches can then produce effective strategies that are given back to users. Our primary findings demonstrate that these agent-tested strategies significantly improve human search performance in designing these complex systems.Copyright

Optimization of Additively Manufactured Multi-Material Lattice Structures Using Generalized Optimality Criteria

Recent progress in additive manufacturing allows for printing customized products with multiple materials and complex geometries. Effectively designing such complex products for optimal performance within the confines of additive manufacturing constraints is challenging, due to the large number of variables in the search space and uncertainties about how the manufacturing processes affect fabricated materials and structures. In this study, characteristics of materials, i.e. Young’s modulus (E), ultimate tensile strength (UTS) and density (ρ), for a multi-material inkjet-based 3D-printer are measured experimentally in order to generate data curves for a computational optimization process in configuring multimaterial lattice structures. An optimality criteria method is developed for computationally searching for optimal solutions of a multi-material lattice with fixed topology and truss cross-section sizes using the empirically obtained material measurements. Results demonstrate the feasibility of the approach for optimizing multi-material, lightweight truss structures subject to displacement constraints.Copyright

Human and Computational Approaches for Design Problem-Solving

Human and computational approaches are both commonly used to solve design problems, and each offers unique advantages. Human designers may draw upon their expertise, intuition, and creativity, while computational approaches are used to algorithmically configure and evaluate design alternatives quickly. It is possible to leverage the advantages of each with a human-in-the-loop design approach, which relies on human designers guiding computational processes; empirical design research for better understanding human designers’ strengths and limitations can inform the development human-in-the-loop design approaches. In this chapter, the advantages of human and computational design processes are outlined, in addition to how they are researched. An empirical research example is provided for conducting human participant experiments and simulating human design problem-solving strategies with software agent simulations that are used to develop improved strategies. The chapter concludes by discussing general considerations in human and computational research, and their role in developing new human-in-the-loop design processes for complex engineering applications.

The D3 Methodology: Bridging Science and Design for Bio-Based Product Development

New opportunities in design surface with scientific advances: however, the rapid pace of scientific discoveries combined with the complexity of technical barriers often impedes new product development. Bio-based technologies, for instance, typically require decisions across complex multiscale system organizations that are difficult for humans to understand and formalize computationally. This paper addresses such challenges in science and design by weaving phases of empirical discovery, analytical description, and technological development in an integrative “D3 Methodology.” The phases are bridged with human-guided computational processes suitable for human-in-the-loop design approaches. Optimization of biolibraries, which are sets of standardized biological parts for adaptation into new products, is used as a characteristic design problem for demonstrating the methodology. Results from this test case suggest that biolibraries with synthetic biological components can promote the development of high-performance biobased products. These new products motivate further scientific studies to characterize designed synthetic biological components, thus illustrating reciprocity among science and design. Successes in implementing each phase suggest the D3 Methodology is a feasible route for bio-based research and development and for driving the scientific inquiries of today toward the novel technologies of tomorrow. [DOI: 10.1115/1.4033751]