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

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          Abstract

          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 bio-based 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.

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          Most cited references64

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          Tough, bio-inspired hybrid materials.

          The notion of mimicking natural structures in the synthesis of new structural materials has generated enormous interest but has yielded few practical advances. Natural composites achieve strength and toughness through complex hierarchical designs that are extremely difficult to replicate synthetically. We emulate nature's toughening mechanisms by combining two ordinary compounds, aluminum oxide and polymethyl methacrylate, into ice-templated structures whose toughness can be more than 300 times (in energy terms) that of their constituents. The final product is a bulk hybrid ceramic-based material whose high yield strength and fracture toughness [ approximately 200 megapascals (MPa) and approximately 30 MPa.m(1/2)] represent specific properties comparable to those of aluminum alloys. These model materials can be used to identify the key microstructural features that should guide the synthesis of bio-inspired ceramic-based composites with unique strength and toughness.
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            A tissue-engineered jellyfish with biomimetic propulsion.

            Reverse engineering of biological form and function requires hierarchical design over several orders of space and time. Recent advances in the mechanistic understanding of biosynthetic compound materials, computer-aided design approaches in molecular synthetic biology 4,5 and traditional soft robotics, and increasing aptitude in generating structural and chemical micro environments that promote cellular self-organization have enhanced the ability to recapitulate such hierarchical architecture in engineered biological systems. Here we combined these capabilities in a systematic design strategy to reverse engineer a muscular pump. We report the construction of a freely swimming jellyfish from chemically dissociated rat tissue and silicone polymer as a proof of concept. The constructs, termed 'medusoids', were designed with computer simulations and experiments to match key determinants of jellyfish propulsion and feeding performance by quantitatively mimicking structural design, stroke kinematics and animal-fluid interactions. The combination of the engineering design algorithm with quantitative benchmarks of physiological performance suggests that our strategy is broadly applicable to reverse engineering of muscular organs or simple life forms that pump to survive.
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              Automated reverse engineering of nonlinear dynamical systems.

              Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilizing the system to extract its less observable characteristics, and automatically simplifying the equations during modeling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated "reverse engineering" approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.
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                Author and article information

                Journal
                Journal of Mechanical Design
                ASME International
                1050-0472
                1528-9001
                August 1 2016
                August 1 2016
                June 16 2016
                : 138
                : 8
                Affiliations
                [1 ]Department of Mechanical and Process Engineering, Swiss Federal Institute of Technology (ETH Zurich), CLA F 34.1, Tannenstrasse 3, Zurich 8092, Switzerland e-mail:
                [2 ]Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 e-mail:
                [3 ]Department of Psychology, University of Pittsburgh, 4200 Fifth Avenue, Pittsburgh, PA 15260 e-mail:
                [4 ]Department of Biological Sciences, University of Massachusetts Lowell, One University Avenue, Lowell, MA 01854 e-mail:
                Article
                10.1115/1.4033751
                48c6db16-190e-4ad6-a24b-672be3846ddb
                © 2016
                History

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