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Research Papers

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

[+] Author and Article Information
Paul F. Egan

e-mail: pfe@cmu.edu

Jonathan Cagan

e-mail: cagan@cmu.edu
Integrated Design Innovation Group,
Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213

Christian Schunn

Cognitive Program,
Department of Psychology,
University of Pittsburgh,
Pittsburgh, PA 15213
e-mail: schunn@pitt.edu

Philip R. LeDuc

Departments of Mechanical Engineering,
Biomedical Engineering, Computational Biology, and Biological Sciences,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: prl@andrew.cmu.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 18, 2012; final manuscript received April 3, 2013; published online May 9, 2013. Assoc. Editor: Karthik Ramani.

J. Mech. Des 135(6), 061005 (May 09, 2013) (12 pages) Paper No: MD-12-1210; doi: 10.1115/1.4024227 History: Received April 18, 2012; Revised April 03, 2013

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.

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Figures

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Fig. 1

Schematic of a muscle hierarchy plotted by approximate number of components and approximate physical size (length by height)

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Fig. 2

A schematic a two-stroke motor and a myosin with labeled structures [30]

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Fig. 3

A schematic of structural states and behaviors for a two-stroke motor and a myosin [30]. Each labeled image represents a structural state and each numbered arrow represents a behavior.

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Fig. 4

A schematic of the function of a two-stroke motor and a system of myosins [30]

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Fig. 5

A schematic of key myosin structures labeled on the molecular representation via X-ray crystallography [25] on the left and an illustration on the right

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Fig. 6

A schematic of engineering synthetic myosin isoforms with (a) altered mechanical behaviors via lever swapping [41] or (b) altered chemical behaviors through replacement of a myosin's head structure [43] (relative size of up/down arrows represents the magnitude of a myosin's attachment/detachment chemical rate constant)

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Fig. 7

A schematic of myosins in a motility assay with actin filament velocity and forces at each binding site indicated

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Fig. 8

Rendering of the virtual environment of twenty five myosins and a long actin filament. Spacing parameters are indicated in the zoomed-view.

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Fig. 9

Modeling myosin agents. (a) A myosin's structural states and mechanochemical behaviors that are (b) simulated by each computational myosin agent's logic circuit.

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Fig. 10

Renderings of the simulation for four time points when the filament velocity is 3 μm/s. The resulting system force experienced by the filament is indicated for each panel. Simulation renderings are viewable at http://www.andrew.cmu.edu/org/IDIG/SBFSimu.htm.

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Fig. 11

Time-average force per myosin at different filament velocities using our simulation (squares). These results were compared with previously published analytical and empirical data [1], depicted by lines and circles, respectively.

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Fig. 12

Aggregate performance curves for each simulated system consisting of isoforms from Table 2. (a) The time-average force per myosin and (b) the percentage of myosins in a population that are attached, both plotted against steady-state filament velocities.

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Fig. 13

Percentage of a myosin population undergoing each detachment behavior for each simulated filament velocity. Each plot A-D represents a system consisting of isoforms from Table 2. Each line specifies the percentage of myosins detaching via each mechanism described in Fig. 9, with different dash styles correlating to different detachment behaviors.

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Fig. 14

Percentage of a myosin population that are undergoing power- and drag-stroke states, and the total percentage attached for each simulated filament velocity. States correlate to those found in Fig. 9 and are indicated by different dash styles for each line. Each plot A-D represents a system consisting of isoforms from Table 2.

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