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]

[1]  D. Warshaw,et al.  Computer-assisted tracking of actin filament motility. , 1992, Analytical biochemistry.

[2]  Henry Hess,et al.  Engineering applications of biomolecular motors. , 2011, Annual review of biomedical engineering.

[3]  E. Krementsova,et al.  Engineering the Processive Run Length of Myosin V* , 2007, Journal of Biological Chemistry.

[4]  Jeffrey R. Moore,et al.  The molecular basis of frictional loads in the in vitro motility assay with applications to the study of the loaded mechanochemistry of molecular motors , 2010, Cytoskeleton.

[5]  Kristin L. Wood,et al.  Bio-Inspired Design: An Overview Investigating Open Questions From the Broader Field of Design-by-Analogy , 2014 .

[6]  Jonathan Cagan,et al.  Improving human understanding and design of complex multi-level systems with animation and parametric relationship supports , 2015, Design Science.

[7]  D. Kell,et al.  Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. , 2004, BioEssays : news and reviews in molecular, cellular and developmental biology.

[8]  Joost R. Duflou,et al.  A scalable approach for ideation in biologically inspired design , 2014, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[9]  Joaquim R. R. A. Martins,et al.  Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop , 2011 .

[10]  Hod Lipson,et al.  Automated reverse engineering of nonlinear dynamical systems , 2007, Proceedings of the National Academy of Sciences.

[11]  Ashok K. Goel,et al.  Structure, behavior, and function of complex systems: The structure, behavior, and function modeling language , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[12]  Ashok K. Goel,et al.  Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models , 2011, J. Educ. Technol. Soc..

[13]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[14]  Ashok K. Goel,et al.  Cognitive, collaborative, conceptual and creative - Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design , 2012, Comput. Aided Des..

[15]  D. Manstein,et al.  Molecular engineering of a backwards-moving myosin motor , 2004, Nature.

[16]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[17]  Toshio Yanagida,et al.  From Single Molecule Fluctuations to Muscle Contraction: A Brownian Model of A.F. Huxley's Hypotheses , 2012, PloS one.

[18]  Pamela A. Silver,et al.  Informing Biological Design by Integration of Systems and Synthetic Biology , 2011, Cell.

[19]  Ritu Raman,et al.  Three-dimensionally printed biological machines powered by skeletal muscle , 2014, Proceedings of the National Academy of Sciences.

[20]  M. Bölker.  Complexity in Synthetic Biology: Unnecessary or Essential? , 2015 .

[21]  D. Randall,et al.  Eckert Animal Physiology: Mechanisms and Adaptations , 1997 .

[22]  James R Faeder,et al.  Efficient modeling, simulation and coarse-graining of biological complexity with NFsim , 2011, Nature Methods.

[23]  Robert Sinko,et al.  The role of mechanics in biological and bio-inspired systems , 2015, Nature Communications.

[24]  Daniel D. Frey,et al.  Cognition and complexity: An experiment on the effect of coupling in parameter design , 2002 .

[25]  Megan L. McCain,et al.  A tissue-engineered jellyfish with biomimetic propulsion , 2012, Nature Biotechnology.

[26]  Martin Fussenegger,et al.  Emerging biomedical applications of synthetic biology , 2011, Nature Reviews Genetics.

[27]  C. Kwong,et al.  Optimisation of product family design with consideration of supply risk and discount , 2016 .

[28]  Marissa G. Saunders,et al.  Coarse-graining methods for computational biology. , 2013, Annual review of biophysics.

[29]  Iain D. C. Fraser,et al.  A simple method for automatic tracking of actin filaments in the motility assay , 1996, Journal of Muscle Research & Cell Motility.

[30]  L. Rome.  Design and function of superfast muscles: new insights into the physiology of skeletal muscle. , 2006, Annual review of physiology.

[31]  Roger D Kamm,et al.  Measuring molecular rupture forces between single actin filaments and actin-binding proteins , 2008, Proceedings of the National Academy of Sciences.

[32]  J. Howard,et al.  Mechanics of Motor Proteins and the Cytoskeleton , 2001 .

[33]  Francisco J. Ayala,et al.  Darwin's greatest discovery: Design without designer , 2007, Proceedings of the National Academy of Sciences.

[34]  Jonathan Cagan,et al.  Design of Complex Biologically Based Nanoscale Systems Using Multi-Agent Simulations and Structure–Behavior–Function Representations , 2013 .

[35]  Yakov Ben-Haim,et al.  What Makes a Good Decision? Robust Satisficing as a Normative Standard of Rational Decision Making , 2011 .

[36]  Tim Menzies,et al.  Automatically finding the control variables for complex system behavior , 2010, Automated Software Engineering.

[37]  R. Weiss,et al.  Foundations for the design and implementation of synthetic genetic circuits , 2012, Nature Reviews Genetics.

[38]  Christina L. Bloebaum,et al.  An end-user decision model with information representation for improved performance and robustness in complex system design , 2015 .

[39]  Robert L. Nagel,et al.  Function-based, biologically inspired concept generation , 2010, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[40]  T. Lu,et al.  Synthetic biology: an emerging engineering discipline. , 2012, Annual review of biomedical engineering.

[41]  Cliff Hooker,et al.  Designing and sciencing: Response to Galle and Kroes , 2015 .

[42]  Susan Selke,et al.  Critical aspects in the life cycle assessment (LCA) of bio-based materials – Reviewing methodologies and deriving recommendations , 2013 .

[43]  Xia Sheng,et al.  Bayesian design of synthetic biological systems , 2011, Proceedings of the National Academy of Sciences.

[44]  Yoram Reich,et al.  Biomimetics: Structure–Function Patterns Approach , 2014 .

[45]  Marc Garbey,et al.  Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques , 2009, Cellular and molecular bioengineering.

[46]  C. Wittmann,et al.  Bio-based production of chemicals, materials and fuels -Corynebacterium glutamicum as versatile cell factory. , 2012, Current opinion in biotechnology.

[47]  Christina D Smolke,et al.  Building outside of the box: iGEM and the BioBricks Foundation , 2009, Nature Biotechnology.

[48]  John S. Gero,et al.  The Situated Function - Behaviour - Structure Framework , 2002, AID.

[49]  Jonathan Cagan,et al.  Protocol-Based Multi-Agent Systems: Examining the Effect of Diversity, Dynamism, and Cooperation in Heuristic Optimization Approaches , 2011 .

[50]  Keiichi Yamamoto,et al.  Kinetic Mechanism of the Fastest Motor Protein, Chara Myosin* , 2007, Journal of Biological Chemistry.

[51]  L. H. Shu,et al.  Retrieving Causally Related Functions From Natural-Language Text for Biomimetic Design , 2014 .

[52]  Lawrence C. Rome.  DESIGN AND FUNCTION OF SUPERFAST MUSCLES , 2006 .

[53]  Jonathan Cagan,et al.  Synergistic human-agent methods for deriving effective search strategies: the case of nanoscale design , 2015 .

[54]  Ibrahim T. Ozbolat,et al.  Bioprinting Technology: A Current State-of-the-Art Review , 2014 .

[55]  Cliff Hooker,et al.  The Simon–Kroes model of technical artifacts and the distinction between science and design , 2012 .

[56]  James A. Spudich,et al.  Functional diversity among a family of human skeletal muscle myosin motors , 2009, Proceedings of the National Academy of Sciences.

[57]  H. Sauro,et al.  Standard Biological Parts Knowledgebase , 2011, PloS one.

[58]  Timothy W. Simpson,et al.  Trade Space Exploration: Assessing the Benefits of Putting Designers ''Back-in-the-Loop'' during Engineering Optimization , 2011 .

[59]  Bryan C. Daniels,et al.  Sloppiness, robustness, and evolvability in systems biology. , 2008, Current opinion in biotechnology.

[60]  Alexander S. Mikhailov,et al.  Nonlinearity of Mechanochemical Motions in Motor Proteins , 2010, PLoS Comput. Biol..

[61]  T. Itoh,et al.  New Isoform of Cardiac Myosin Light Chain Kinase and the Role of Cardiac Myosin Phosphorylation in α1-Adrenoceptor Mediated Inotropic Response , 2015, PloS one.

[62]  Veronica J. Neiman,et al.  Synthetic bio-actuators and their applications in biomedicine , 2011 .

[63]  R. Ritchie,et al.  Tough, Bio-Inspired Hybrid Materials , 2008, Science.

[64]  Jonathan Cagan,et al.  The D3 Science-to-Design Methodology: Automated and Cognitive-Based Processes for Discovering, Describing, and Designing Complex Nanomechanical Biosystems , 2015 .

[65]  Kees Dorst,et al.  The core of ‘design thinking’ and its application , 2011 .

[66]  Herbert A. Simon,et al.  Formal Engineering Design Synthesis: Scientific Discovery and Inventive Engineering Design , 2001 .

[67]  D. Weitz,et al.  An active biopolymer network controlled by molecular motors , 2009, Proceedings of the National Academy of Sciences.

[68]  R. K. Wright,et al.  Smooth, cardiac and skeletal muscle myosin force and motion generation assessed by cross-bridge mechanical interactions in vitro , 1994, Journal of Muscle Research & Cell Motility.

[69]  Jonathan Cagan,et al.  A-Design: An Agent-Based Approach to Conceptual Design in a Dynamic Environment , 1999 .

[70]  Per Galle,et al.  Science and design: Identical twins? , 2014 .

[71]  Joachim P Spatz,et al.  Building up micromuscles , 2005, Nature materials.

[72]  Jonathan Cagan,et al.  Emergent Systems Energy Laws for Predicting Myosin Ensemble Processivity , 2015, PLoS Comput. Biol..

[73]  Charles M. Eastman.  Recent Developments in Representation in the Science of Design , 1981, 18th Design Automation Conference.

[74]  Erik Meijering,et al.  Methods for cell and particle tracking. , 2012, Methods in enzymology.

[75]  Goksel Misirli,et al.  Standard virtual biological parts: a repository of modular modeling components for synthetic biology , 2010, Bioinform..

[76]  Tua Björklund,et al.  Initial mental representations of design problems: Differences between experts and novices , 2013 .