Synthetic Daisies

Hard-to-define Events Workshop

2012-02-03T13:03:00.002-06:00

I just found out that the workshop I am trying to organize for Artificial Life XIII on hard-to-define phenomena was accepted by the program committee. Hard-to-define events are events in a complex system that set up major transitions or obvious features. Signatures of hard-to-define events are related to natural fluctuations, embedded patterns, and rare events of large magnitude. Unlike the underlying patterns and information revealed by machine learning techniques, hard-to-define techniques require alternative approaches not yet formalized.

It should be a good session, but I need to procure a lineup of participants. I am currently recruiting people to present their work in the context of hard-to-define events: the plan is to think about how one's research might involve hard-to-define events, and then consider how we might design analytical tools and/or measurement techniques to discover them.

I am interested in having people participate from any number of fields. Of particular interest is how this idea might apply to the biological, cognitive, and social sciences. I have launched a webpage devoted to the latest news on the session. Please check it out, and if you are interested in participating contact me for more information. If you are in the Midwest on the weekend of July 19-22, you should try to attend (see previous blog post for more information).

Representing rare events

2012-01-28T16:02:00.002-06:00

In this post I will be discussing the occurrence of rare events [1] and how they might be represented in computational systems. Rare events are intriguing precisely because they occur infrequently. We are all familiar with rare events in nature: avalanches, rogue waves, freak storms, and developmental mutations are but a few examples. Furthermore, while these events are rare, they are not improbable. I posted a few months back on the context of such processes (overproductive systems) and their potential relevance to biology. While the occurrence of rare events themselves is sparsely distributed in time, they occur against a background process filled with many events we like to call "normal". This is related to a normal distribution: background events occur within a certain range of expected outcomes.

Figure 1. A variety of rare events sampled from nature.

One way in which rare events have been addressed from a statistical standpoint is to model them as a distribution of noise. In processes where we expect no rare events, we can assume that events will unfold according to a white noise distribution [2]. For processes where these rare events occur at increasingly larger magnitudes, a model of colored (or 1/f) noise can be used to model the expectation of rare events embedded in a process. So-called pink noise provides a series of rare events with a near-uniform size many orders of magnitude above the size of background events, while black noise provides a series of rare events occurring against a background of near-inactivity.

The comparison of extreme 1/f noise to white noise can be made using a thought experiment. Suppose you enjoy sleeping with a white noise machine running in the background. Why do you enjoy this? One reason might be that a white noise machine provides a stochastic but uniform auditory stimulus that allows your relax enough to fall asleep. Yet suppose that you wanted to awake from your slumber. Most people use an alarm clock, which presents bursts of auditory stimuli at different magnitudes, depending on your preference.

This transition from a uniform stream of sound to a series of bursts embedded in a more uniform background is the transition from common events to rare events. Not only do rare events capture your attention, but they also provide a basis for large-scale transitions in coupled processes. In this case, your alarm clock is coupled to your sensory systems, which force you to wake up abruptly.

One also can think of a noisy distribution as the null model for the expectation and occurrence of rare events. Yet during a given natural process, not only are rare events expected to occur at a certain rate, but also recur at a certain rate. This is because rare events tend to be stochastic, and as a result are not evenly distributed in time. This connection to recursion suggests that such events are computable, if we only had the proper data structure for these events.

From the time of the difference engine to today, representing something for a computational system has involved using a discrete binary model that conforms to a logical data structure (e.g. logic gate, tree, matrix). Yet "natural" computation (or computable behavior) has been observed in systems such as chemical (B-Z and Turing) reactions [3], collective behavior, and gene expression, which may be neither discrete and binary nor explicitly logical. However, one could argue that rare and extreme events occur in such systems, and that it is a phenomenon very poorly captured by contemporary hardware and software.

LEFT: an example of a B-Z reaction. RIGHT: examples of reaction-diffusion seen in so-called Turing reactions (a key mechanism in biological development).

Why aren't contemporary data structures suitable for what I am describing? It is true that much of the work on fractal geometry was made possible using standard, existing computing logic [4]. Yet most of modern artificial intelligence and machine learning is based on the notion of normalization. For example, in machine learning and pattern recognition, a series of exemplars are used to identify discrete groups and isolated objects. A basis function used to delimit these groups is often based on the normal distribution, and the margin of these functions are generally associated with a deterministic amount of variance. This leads to a high rate of true positives for classifying objects, but can lead to catastrophic failures from time to time.

An alternate approach is to use a genetic algorithmic or artificial life representation. While these techniques are capable of generating rare and/or extreme events (through mutation and recombination), we must still understand the context of rare events. By observing systems such as rogue waves and avalanches, we can see that fluctuations are an important ingredient for generating rare events. Thus, systems that exhibit flux (where there are undulations in the acceleration and movement of individual particles) should be abstracted to a formal data structure in order for commercially-available computers to completely model rare events. Incorporating these features into a genetic algorithm or artificial life application would greatly help us understand natural instances of non-uniformity.

But can this even be done using conventional computational abstractions? It is hard to say. I have thought about this problem, and remain convinced that we need fundamentally new forms of computation to truly address these features of nature. One option might be "natural" computing, which is the use of natural phenomena (such as chemical reactions or bubble formation) to execute computations [5]. Another option might be in the area of biomimetics, where the goal is to abstract functional features from biological and other natural systems for purposes of computing and developing engineering applications.

An interesting area indeed. More news as it develops.

References

[1] Rare events have been characterized by the Black Swan phenomenon and the Poisson distribution.

[2] A white noise distribution is a normally-distributed Gaussian process.

[3] Adamatzky, A. (2001). Computing in Nonlinear Media and Automata Collectives. Institute of Physics, Bristol, UK.

[4] Mandelbrot, B. (1982). The Fractal Geometry of Nature. W.H. Freeman, New York.

[5] I highly recommend checking out Andrew Adamatsky's work on reaction-diffusion computing and Manu Prakash's work on microfluidic logic using bubbles.

The Evolution and Neuromechanics of very-fast movements

2012-01-24T00:02:00.013-06:00

How are movements completed in under a millisecond (ms) generated and regulated, particularly when they result in forces many times the body weight of the organism in question? Much of the recent work (over the last 15-20 years) has focused on neuromuscular, sensory, and mechanical mechanisms. Some people have proposed a field called neuromechanics [1, 2] to synthesize how animals process sensory information and use it for adaptive movement. A neuromechanical approach requires a feedback loop between sensory stimuli and muscular output, mediated by neural mechanisms. One simple example of this is control of the gill structure in Sea Slugs (Aplysia). Using a series of conditioned stimuli, the gill structure will open and close in an adaptive manner similar to the learning and memory found in more complex neural systems.

On the January 20th episode of Science Friday (an NPR show on Friday afternoons), the video clip of the week featured work from the Patek Lab at UMass-Amherst. The Patek Lab focuses on the very fast predatory and fighting movements seen in Mantis Shrimp (Stomatopods) and Trap Jaw Ants (Odontomachus). By looking into the work being done at the Patek Lab, I was introduced to an entire body of work on ultra-fast and very powerful movements. This work is very cutting-edge, and provides a wealth of information on evolution, behavior, and the function of neuromuscular systems.

How does a mechanism specialized for rapid and powerful movements evolve and vary across phylogeny? In a paper by Spagna et.al [3], a phylogeny of trap-jaw ants was constructed by using observations of behaviors related to appendage usage, mechanical function, and several genetic loci. The authors were trying to trying to correlate differences in the speed and acceleration of appendage movement with morphological variation as observed across all species in the genus (see Figure 1).These findings suggest a recent origin and rapid diversification for appendage mechanisms.

Figure 1: Phylogenetic relationships between species of trap-jaw ant. Taken from [3].

Why do trap-jaw ants need to produce large amounts of force relative to their body weight, and why is it variable across evolution? While using their mandibular appendage, trap jaw ants produce a stress (force) that is 300x their own body weight [4]. This produces a movement so fast that it could not be studied until the proper high-speed motion capture equipment became available. If the ants strike the ground with their appendage, they can produce a movement called “ballistic” jumping, which results in an uncontrollable jump.

The authors of [4] refer to mandibular appendage closure as a "high-performance" behavior. In general, high-performance behaviors are associated with a single function. However, appendage closure is a multifunctional behavior, used for prey capture, fighting, and jumping to safety [4]. Perhaps more interestingly, multifunctional behaviors are related to evolutionary tradeoffs and the co-option (or exaptation) of shared and novel structures. In [3], it is suggested that size might be limited by energetic constraints but maximized by the requirements of prey capture. Yet since the production of force is many times larger than the organism produces in any other muscle, there could be other dynamics at work here, including evolutionary arms races within species.

In Figure 2, we can see the location of the mandibular appendage in relation to the anterior portion of their body. When this appendage strikes the ground, it produces the ballistic movement also seen in Figure 2. Ballistic movements are essentially bursts of muscular activity produced without much regard for control. Throwing, kicking, and eyeball saccade movements are examples of ballistic movements in humans.

Figure 2. LEFT: image of mandibular appendange. RIGHT: frames from video of a jaw propulsion movement. Taken from [3].

In the study of human movement, there is a notion called the speed-accuracy tradeoff, characterized by Fitts’ Law [5]. In essence, the faster the movement, the less accurate it will be. This has much to do with the lack of muscular control for movements that generate a large amount of force in a short period of time [6]. Highly-controlled movements such as drawing or balancing require that movements be made slowly and no large fluctuations in force be introduced. This requires extensive co-regulation of muscle groups, and results in a highly complex sequence of physiological events. Very fast movements, on the other hand, are produced simply by letting a single muscle or muscle group release the maximum amount of force that it is capable of producing.

Now that we know how muscle force production can be maximized with respect to output, we must now understand how muscle power is “amplified” in nature. From the standpoint of technology, we know how to amplify the amount of work done by a set of muscles in the human body. But how is this accomplished naturally? One way is via kinematic or mechanical linkage. In the human body, a whole-body movement such as a heave involves movement of the trunk, arms, and fingers. As one moves outward from the midline, the velocity and acceleration of a segment (e.g. humerus, forearm, or hand) become progressively larger and less uniform with regard to time. Now consider how the mantis shrimp uses its appendage. Unlike the trap jaw ant mandibular appendage, the mantis shrimp appendage is used for cracking open the shells of prey. This requires massive forces to be produced and displaced, but which also requires a highly specialized phenotype.

Muscle power amplification in the mantis shrimp works in a similar manner. Muscle power can be thought of as the amount of potential work done by the muscle [7]. Muscle “work” is related to the amount of force generated by the muscle and the appendage it is connected to. In [8] and [9], it is suggested that extremely fast movements for which power amplification is required are achieved by reducing the duration of the movement. The anatomical linkage involved in power amplification is the integrated function of three units (Figures 3 and 4): an engine, an amplifier, and a tool [9]. In the context of mantis shrimp anatomy, the engine is represented by muscle, while the amplifier is represented by spring and the tool is represented by a hammer. Consistent with the notion of kinematic linkage, the distal component of this system produce the greatest forces (and of course move the fastest).

Figure 3. LEFT, CENTER: biomechanical model depicting mantis shrimp appendage as a loaded spring. RIGHT: location of mantis shrimp appendage on body. Taken from [8].

Figure 4. LEFT: cartoon of the mantis shrimp appendage and a geometric model that approximates shape changes in anatomical structure. RIGHT: images of the mantis shrimp during behavior and after dissection/maceration. Taken from [8].

In [9], the authors consider the developmental origins of the muscle power amplifier system. To do this, they ask two questions. The first is whether or not the three components of the appendage system (muscle, merus region, and hammer) constitute three independent developmental modules. Since this is found to be the case, the second question centers around the scaling of the appendage system with respect to body size. The answer to this is yes, but with some interesting qualifications. As expected, muscle force increases proportionally to muscle shape and size. Yet there is a lack of change in shape relative to size, which is different from the isometric relationship between muscle size and body mass seen in jumping and running mammals. In addition, the merus region (the “spring” mechanism) has a selective capacity for stiffness, which results in maximal force production that scales allometrically to body size. Finally, the hammer (the “tool” mechanism) acts as a lever, which like the human foot provides a mechanical advantage to handling large loadings and forces.

References

[1] Nishikawa, K. et.al (2007). Neuromechanics: an integrative approach for understanding motor control. Integrative and Comparative Biology, 47(1), 16-54.

[2] Enoka, R. (2008). Neuromechanics of Human Movement. Human Kinetics, Champaign, IL.

[3] Spagna, J.C. et.al (2008). Phylogeny, scaling, and the generation of extreme forces in trap-jaw ants. The Journal of Experimental Biology, 211, 2358-2368.

[4] Patek, S.N., Baio, J.E., Fisher, B.L., and Suarez, A.V. (2006). Multifunctionality and mechanical origins: ballistic jaw propulsion in trap-jaw ants. PNAS, 103(34), 12787–12792.

[5] Meyer, D.E., Smith, J.E.K., Kornblum, S., Abrams, R.A., and Wright, C.E. (1990). Speed-accuracy tradeoffs in aimed movements: toward a theory of rapid voluntary action. In M. Jeannerod (ed.), Attention and performance XIII (pp. 173–226). Hillsdale, NJ: Lawrence Erlbaum.

[6] Ifft, P.J., Lebedev, M.A., and Nicolelis, M.A.L. (2011). Cortical correlates of Fitts’ law. Frontiers in Integrative Neuroscience, 5(85), 1-16.

[7] Enoka, R.M. and Fugelvand, A.J. (2001). Motor unit physiology: some unresolved issues. Muscle & Nerve, 24(1), 4–17.

[8] Patek, S.N., Nowroozi, B.N., Baio, J.E., Caldwell, R.L., and Summers, A.P. (2007). Linkage mechanics and power amplification of the mantis shrimp’s strike. The Journal of Experimental Biology, 210, 3677-3688.

[9] Claverie, T., Chan, E., and Patek, S.N. (2010). Modularity and scaling in fast movements: power amplification in mantis shrimp. Evolution, 65(2), 443–461.

Dynamics Days 2012 Report

2012-01-10T14:23:00.023-06:00

Last week (January 3-7) I was an attendee at Dynamics Days, a complexity conference held this year in the Inner Harbor section of Baltimore, MD and hosted by the University of Maryland-College Park. The University of Maryland has an active research group studying chaotic systems and includes people such as Ed Ott, Wolgang Losert, and Michelle Girvan. In fact, this year's event featured a tribute to Ed Ott in honor of his 70th birthday.

I presented a poster on my own work, which was part of a very active poster session. There were also several overarching themes that were shared by a number of talks and poster presentations. One theme was using variations on Lagrangian analysis for understanding turbulent flows. One example is the calculation of finite size Lyapunov exponents (FSLEs) to understand the effects on scale in complex systems. Another example was in the use of second-order Lagrangian systems to understand the effects of turbulence on UAV aircraft. The third example involved incorporating a stochastic element into Lagrangian modeling through the use of an uncertainty estimator. In general, the use of Lagrangians to understand aggregations of particles due to flow field dynamics is currently at the cutting-edge of research in engineering and physics, most notably in the form of Lagrangian Coherent Structure (LCS) analysis.

Example of a Lorentz attractor (above) and a flow field containing Lagrangian Coherent Structures (LCSs) - (below).

A second theme was (of course) chaotic systems, or dynamical systems that are often operate far from equilibrium. Jim Yorke reminded us that there are several definitions of chaos, depending on the type of system under analysis. The best known form of chaos is "statistical regularity" chaos, the short hand of which is understood as a strange attractor. Other forms of chaos include: transient chaos, broad-band power spectra, and deterministic and bounded dynamic behavior. Chaos can be represented as either oscillatory regimes embodied in attractor maps or bifurcations and period doubling-type events embodied in Henon (horseshoe) and logistic maps. Yorke made the connection between each representation and how it relates to our understanding of chaos. He also briefly touched on an emerging application of chaos to system control, which involves a game of survival between the noise and control components of a system. In chaotic systems, where the noise is greater than the control component, the dataset first needs to be sculpted, which reveals a series of safe sets that can be further sculpted to increase controllability. A related presentation focused on the difference between "fast" and "slow" dynamics, and how they work together to define time dynamics.

Example of a Rossler attractor (above) and a logistic map (below).

A third theme was Biophysics. There were a number of posters on actin scaffoldings in cells and cell motility. In particular, actinomyosin elements in the cell are used for rigidity sensing and can be understood as a small-scale muscular machine. There was a presentation on the study of quorum sensing in bacteria using populations of magnetic dipoles as a physical analogues to biological signaling processes and spatiotemporal gradient sensing, and a related presentation on the statistical mechanics of chemotactic movements. There was also an interesting presentation by Theo Geisel on the perception of beat in music. We learned that derivations in drumbeat during the course of a song acts to "humanize" a given song, and that this information can be used to build audio editing technologies. There were several other presentations on neuroscience, including new directions in modeling the dynamical chaos of sleep/wake cycles (Victoria Booth) and the modeling of fast and slow neuronal dynamics using ion concentrations. Finally, Tomas Bohr gave a presentation on how plants are dynamical systems. In particular, the passive and active transport of water and sugars from the leaves to the root systems can be modeled using a series of mathematical techniques and predicts the growth and size limitations of plants.

Example of a Henon map, representing a horseshoe bifurcation.

A fourth theme was evolutionary game theory and the evolution of information and computation. This includes a wide range of approaches such as autonomous boolean networks, discontinuous percolation to model group selection, and encodings for regulatory networks. One notable presentation focused on a stochastic framework for understanding transcriptional dynamics. The major features of this model included relations between timescales and the role of oscillations, dampening, and excitability in the transcriptional process. Another talk involves using the rock-paper-scissors game to model the nonlinear hierarchical dynamics (where there is no dominant strategy) of viral infection and virulence. Jim Crutchfield's group had several posters on the nature of information and the role of entropy in biological complexity. One up-and-coming researcher by the name of Garrett Michener presented on his work encodings for regulatory networks that combines genetic algorithms, neural networks, regulatory networks, and computational linguistics. His models produce some interesting complex behaviors.

Example of active transport of water and sugars in a plant.

One obscure idea that interested me the most were various approaches to extreme events. While in many cases extreme events are treated as outliers, it is also known that not all data are normally-distributed. As a result, we need methods for understanding value that exhibit large variation from the mean in proper context. There was a poster than focused explicitly on how to measure extreme events in a system, and a presentation that discussed the phenomenon of rogue waves in optical systems with applications to rogue waves in other physical systems (such as the surface of the ocean). Next year's Dynamics Days (2013), obscure ideas and all, will be held in Boulder, Colorado.

OnInnovation, a site/blog/resource about "innovation"

2012-01-03T19:14:00.002-06:00

As I post on the concept of "innovation" from time to time, I would like to share a promising-looking resource that I encountered recently. The OnInnovation site (run by the Henry Ford Museum) features a wide swath of innovators, from historic inventors such as Henry Ford and Thomas Edison to contemporary people such as Steve Wozniak (computing), Elon Musk (venture capitalist), Dean Kamen (mechanical engineering), Rosa Parks (social change), Toshiko Mori (architecture), and Martha Stewart (entrepreneur).

There are also a number of interactive features to this site, which make it a good resource for people interested in understanding the embodiment of entrepreneurship and creative thinking. One feature is a video submission site associated with the American Invents! initiative and the Maker Faire. This is a good opportunity for people without a lot of resources or financial backing to try something new or show off their ideas.

There are also materials related to STEM (science, technology, and mathematics) education. Their Innovation 101 course offers five modules (definitions of innovation, process of innovation, personal traits of innovators, keys to innovation, and intellectual property) concerning different aspects of innovating. OnInnovation also includes a blog, which is not updated often, but keeps one up to date on where the site is headed.

The "nature" of materials: evolution and biomimetics

2011-12-26T13:53:00.003-06:00

How do highly specialized biological materials evolve? An even better question is how the many different materials that make up animal bodies have come to coexist in the same organism. In this post, I will address the basics of these questions, although there is much to learn. This has applications to both basic research on biological systems and engineering methodology [1].

I will review the evolution of materials from three perspectives: the formation of biological composites in evolution, the phenomenon of layering in evolution, and the effects of evolving new structures on materials. Based on contemporary scientific findings, these are the most likely scientific mechanisms that account for the evolution of specialized materials and surfaces.

I. Biological composites
If you examine any segment of an animal body, you will find that it is composed of many different types of material. A human forearm, for example, is composed of fat layers, skeletal muscle, lamellar bone, and various other liquids and solids. This is a veritable kaleidoscope of tissue types, from a highly mineralized matrix (bone) to loosely-associated cell populations (fat layers). This diversity arises from the maintenance of two key mechanisms at the organ level: cell differentiation and tissue microenvironments. By contrast, biomechanical function acts to constrain the types of materials that can form through the forces experienced in functional contexts.

MRI image of human leg (left), highlighting the composite nature of the limb [2]. Material properties of trabecular bone (right) -- Voelcker Lab, Flinders University, Australia.

Cell differentiation. The differentiation of cells plays a role in the formation of these composites. In embryonic development, cell differentiation is partially determined its niche, or who its neighbors are. One possible organizing mechanism involves intercellular signaling molecules. This flexibility might allow for a wide range of materials to be formed in response to stresses and other cellular functions. It has been shown that cells can exhibit gene expression and protein production changes in response to the frictions induced by adhesion in vitro [3]. The stability of specific cell types might focus on the ability of these cells to absorb environmental stresses and fluctuations, which includes mechanical forces during movement and other interactions with the environment.

Tissue microenvironments. The aggregation of cells into tissues is also required for composite formation. More specifically, it is the co-existence of these cell populations in communities that allow for these composites to become well-integrated [4-6]. Cell populations residing in organs and other structures are organized in a manner analogous to an ecosystem, with energy exchange and substrate formation being prime candidates for the maintenance of homeostasis. Therefore, there is a hierarchical dependence leading from protein synthesis by individual cells to organismal context and back again that determine what materials will be formed in response to environmental and natural selective pressures.

Biomechanical function. While it seems counterintuitive, many structures that exist in nature may not be optimized for biomechanical function. Those that are “optimal” in terms of function tend to be highly specialized structures [7], and are also likely to be made from highly unique materials. Those structures that are less specialized in terms of function, or perform multiple functions simultaneously, should be made out of materials with a higher tribological diversity. Ultimately, these are testable predictions, but can also be inferred through examining the role of layering and the evolution of downstream [8] morphological structures.

II. Layering
In fields such as BioMEMS and bionanotechnology, a central issue is how very small functional devices can be fabricated. Generally, there are two methods for fabrication in very small devices: etching features into an existing surface, and deposition of materials onto an existing surface.

Examples of etching (left, courtesy London Centre for Nanotechnology) and deposition (right, courtesy ASME). Several research groups have used X-ray beams to etch patterns into very small surfaces (on the order of uM). Carbon nanotubes can be grown on a surface using a process called vapor deposition to modify very small surface function (on the order of uM).

Natural Microfabrication. In systems that are driven by natural processes, microfabrication cannot be top-down as engineering techniques generally are. A bottom-up strategy involves a mechanism called directed self-assembly [9]. In self-assembly, the configuration of a molecular system is biased by the available free energy, and the result is a highly-ordered system. The directed component comes into play in the form of "templates" or building blocks that guide further self-organization [10]. This is similar to the idea of building blocks in genetic algorithms, which appear to play a role in allowing for evolvable genotypes in silico.

One link between surfaces and materials fabricated through natural means and contemporary biomimetic engineering is the characterization of superhydrophobic surfaces. Hydrophobicity occurs when water molecules are repelled from a membrane rather than being absorbed by it. This is a feature characterized in nature by the lotus leaf, and is a design consideration in a range of BioMEMS applications.

Superhydrophobic surfaces are created by "dual-scale roughness". As the name implies, the surface exhibits a rough topology at two distinct spatial scales [11]. This further implies that hierarchical structures, with each component performing either a distinct or overlapping role, is required for evolving such surfaces. Surface stiffness is also a factor in guiding self-assembly [12], and may play the role of templates discussed in the previous section.

A picture characterizing the dual-scale roughness of a lotus leaf (courtesy, Soft Matter Interface group, Max Planck Institute).

Response to Function. While natural selection generally acts upon the entire organism, there may also be selective processes acting at multiple spatial scales within morphological structures. The structure of the Gecko's foot is an example of this. Like the lotus leaf, the Gecko's foot uses a hierarchical structure of grooves and cilia to maintain a highly specialized function [13-15]. This type of specialization is seen across plant and animal species as a diversity of biological attachment mechanisms.

While the "optimality of structure" idea presented as an outcome related to the biomechanical function of biological composites, is best exemplified by these systems, the existence of tribological diversity requires a different set of examples. To do this, we must turn to the evolution of a generalized trait, preferably one that plays a role in the biomechanics of an organism.

III. Evolution of the Jaw
In the evolution of the jaw, it appears that one change gives rise to another. The jaw evolved rather early in vertebrate phylogeny, constituting a group called Gnathostomes [16] and includes a diverse collection of forms. In mammals, the jaw allows for the mandible to articulate relative to the upper portion of the head in concert with muscle activity allows for chewing and active manipulation at the mouth opening. The range of motion in a jaw is variable, from the very large aperture of the hippopotamus to the more constrained mouth opening of a Primate.

Highly-stylized phylogenetic relationship between agnathans (e.g. lamprey, left) and gnathostomes (e.g. alligator snapping turtle and hippopotamus, center and right). Above phylogeny are the anatomical substrates of the jaw joint in non-eutherian Gnathostomes (left) and eutherian Gnathostomes (right).

Diagram showing the route from a joint (human temporomandibular joint, left) that can potentially transfer and/or dampen forces with regard to "downstream" morphological structures (right).

The evolution of a jaw has several indirect consequences, one of which is the introduction of large forces and stresses at the head. Much like in the case of vertebrate limbs, which are formed from highly durable and selective compliant [17] composite materials, the introduction of new functions requires a displacement of forces encountered during function.

A single evolutionary origin (monophyly). In the case of specialized and structurally specialized materials, such as specific instances of mineralized tissue, materials will arise once in a common ancestor and then be retained among members of a single clade. For example, vertebrates have tended to keep their skeletons made of either cartilage or hard bone. Major innovations in bone types tend to be restricted to specific clades. In cartilagenous fishes, the large-scale use of cartilage in the skeleton results in a single tissue type with the ability to endure a greater range of mechanical deformations. In mammals, hard bone comes in a number of forms (e.g. cortical, cancellous, and lamellar bone), each with their own physical properties and ability to endure and respond to various stresses and strains.

Multiple routes to refinement. The other option, observed in the origin of compounds such as chitin, is that a precursor material will evolve first and then be modified in various lineages to perform specific roles in a morphological system. For example, the basic chitin molecule may be adapted to a create a number of specialized materials in different lineages. Likewise, a basic set of molecules can be configured into different topologies through layering and/or other self-organized structures to produce highly-specialized materials. This is true of spider silk, which is used to build webs and other structures essential to their survival. Spider silk is a secreted protein that when spun into a silk strand has impressive mechanical properties. However, the performance characteristics of the resulting silk vary greatly in terms of both species of spider and gland of origin.

Conclusion
While the tribological profile of a given organisms is both determined by and a determiner of the evolution trajectory of a species, this can be approached from two perspectives. Reductionists will likely focus on the molecular building blocks of specific materials, answering questions such as: what are the signals that initiate the production of highly specific proteins and signaling molecules? Meanwhile, systems-level scientists will be more interested in the process of material synthesis during development, and answer questions such as: how is the self-assembly of highly specialized coordinated by generic processes? Both perspectives will be needed to fully appreciate the role specialized materials observed from across the diversity of the natural world and apply their artificial analogues to engineering technologies.

References and Notes

[1] Xia, F. and Jiang, L. (2008). Bio-inspired smart, multiscale interfacial materials. Advanced Materials, 20, 2842-2858.

[2] Courtesy: Imiaos.

[3] Mahoney, T.S. et.al (2001). Cell adhesion regulates gene expression at translational checkpoints in human myeloid leukocytes. PNAS USA, 98(18), 10284–10289.

[4] Poole, C.A. (1997). Articular cartilage chondrons: form, function and failure. Journal of Anatomy, 191, 1-13.

[5] Jeanes, A.I. et.al (2010). Cellular Microenvironment Influences the Ability of Mammary Epithelia to Undergo Cell Cycle. PLoS One, 6(3), e18144.

[6] Burdick, J.A. and Vunjak-Novakovic, G. (2009). Engineered Microenvironments for Controlled Stem Cell Differentiation. Tissue Engineering Part A, 15(2), 205-219.

[7] McNeill-Alexander, R. (1988). Elastic Mechanisms in Animal Movement. Cambridge University Press, Cambridge, UK.

[8] "downstream" in this case means structures whose change in function have an indirect effect on other structures.

[9] Romano, F. and Sciortino, F. (2011). Colloidal self-assembly: Patchy from the bottom up. Nature Materials 10, 171–173.

[10] Wang, D. and Mohwald, H. (2004). Template-directed colloidal self-assembly – the route to ‘top-down’ nanochemical engineering. Journal of Materials Chemistry, 14, 459-468.

[11] Shirtcliffe, N.J. et.al (2004). Dual-scale roughness produces unusually water-repellent surfaces. Advanced Materials, 16, 1929-1932.

[12] Kima, T.W. and Bhushan, B. (2007). Effect of stiffness of multi-level hierarchical attachment system on adhesion enhancement. Ultramicroscopy, 107, 10-11, 902-912.

[13] Gorb, S. and Scherge, M. (2000). Biological microtribology: anisotropy in frictional forces of orthopteran attachment pads reflects the ultrastructure of a highly deformable material. Proceedings of the Royal Society of London B, 267, 1239-1244.

[14] Arzt, E., Gorb, S., and Spolenak, R. (2003). From micro to nano contacts in biological attachment devices. PNAS, 100(19), 10603-10606.

[15] I refer interested readers to a comprehensive online bibliography covering research on the biology and engineering of surface adhesion and attachment mechanisms (Gecko-centric) hosted by the Robotics and Intelligent Machines Lab at the University of California-Berkeley.

[16] Tree of Life, Gnathostomes.

[17] flexible and pliable in response to functional needs. For example, bone (e.g. mineralized tissue) can be both rigid and flexible, depending on how it is loaded and degree of hysteretic response. Material compliance is not directly related to plasticity, although there may be some interesting parallels between the two phenomena.

John Hodgeman Does the (Towel) Twist

2011-12-16T11:43:00.009-06:00

I am currently reading the new John Hodgeman book "That is All". I also enjoyed his previous two books “Areas of my Expertise” and “More Information than you Require”. I’m not sure what it is about his style that I enjoy. Probably the wordiness mixed with wryness and absurdity. For example, on the book jacket, there is a quote from Neil Gaiman which reads: “If you see John Hodgeman on the road, kill him”. Great stuff. So do as the Buddhists do: transcend the illusion of John Hodgeman, and drink the Kool Aid if necessary (but not too much of it). Here is a little passage I wrote that is “Hodgeman-esque” in nature:

“Have you ever wondered what it would be like to mail a package to an enemy nation? John Hodgeman knows. He also knows how to custom write a series of non-sequiturs to be delivered orally at weddings, bar mitzvahs, and town council meetings (as the need arises -- he is versatile that way). Or better yet, what would it be like to mail a towel to the same rogue nation? In John Hodgeman’s house, there are thousands of unique-looking towels (mounted on the walls, not kept in the drawers, which would be silly) from all over the world, even enemy nations. While it might be a stretch to call him a "hoarder" (as seen on the television program 'Hoarders'), I can think of no other television program I would rather see him on. So the next time someone asks you 'can anthrax be delivered using a wet towel?' -- think of John Hodgeman”.

Figure 1. John Hodgeman (left) and the zombie muppet that dwells inside him (right). Don't believe me? Kill him and find out.

Google Doodle - Robert Noyce

2011-12-12T14:47:00.002-06:00

Another interesting Google doodle, this time it is in honor of Robert Noyce (the honor being a posthumous birthday), the co-inventor of the integrated circuit.

Recent Adventures in Virtual Reality

2011-12-09T00:14:00.007-06:00

About a month ago, I announced publication of the paper "Virtual reality in neuroscience research and therapy", published in Nature Neuroscience Reviews by myself and two other colleagues. The paper actually inspired the cover art for the December issue [1], which is now online. A closeup of the cover art (below) is entitled "Virtual Reality Reaches New Heights" by Kirsten Lee.

Also, I have been revisiting Second Life as a venue for scientific research [2]. I gave a lecture to the Embryo Physics course on 12/8 (Thursday). Topic: Cellular Reprogramming. Aside from some glitches [3] due to bandwidth issues, the talk went well. Pictures [4] from the venue can be seen below. The first picture is the meeting space, while the second is a picture of virtual horses (no kidding!) in a stable, and the third is a screenshot of my alternate avatar [5] presenting.

Notes and References:

[1] issue 12(12).

[2] this link is a photo-log of my forays into "virtual science".

[3] I did the entire lecture using text chat. Using a tablet PC, I was able to review the slides in the real world, and type (e.g. speak) to the slides being presented in Second Life.

[4] my avatar -- biodroid -- is in the foreground of the first two pictures.

[5] apparently, the "skins" of my robot were taking up too much bandwidth. I switched to a human form, but I still couldn't speak in-world.

Breakout Labs: another funding model

2011-12-03T12:50:00.001-06:00

In a previous post, I discussed the potential of small-scale (e.g. peer-to-peer) science funding. One very recent development in alternative funding has been the founding of Breakout Labs, funded by PayPal founder Peter Theil.

Breakout Labs specializes in funding high-risk, early-stage science. These are project too risky for the major funding agencies (e.g. NIH, NSF) and too formative for venture capital investments. One area that could benefit from this model is so-called "entrepreneurial" science, which is not immediately profitable but may lead to innovations down the road. This is a "blind spot" in current science funding models. Due to the high degree of risk inherent in these early-stage projects, a funding model that provides support first and expects a rate of return on investment later is critical. If you are interested in applying for funding, see the Breakout Labs website for more details.

Tour of the OtherLab

2011-12-01T11:31:00.017-06:00

While doing a recent search on up-and-coming technologies and cutting-edge science, I stumbled upon some YouTube videos of soft robots, and subsequently found the website of OtherLab. Other Lab is a collective of scientists and inventors involved in a number of projects, including proof-of-concept mechatronics that might be useful in building functionally adaptive and intelligent machines. In this post, I will review a number of videos from their YouTube channel.

Peristaltic pump

Similar to a number of physiological pumping processes, here is a peristaltic pump design built by the lab. The process of peristalsis is the main actuation process in smooth and skeletal muscle systems. Peristalsis allows for symmetrical displacements of the muscular surface that occurs in waves and moves fluids through the body. The gastrointestinal tract and four- chambered heart are two examples of this. This pump design, optimized for modeling muscular output during joint flexion, is a key component of OtherLab's pneubotic machines.

Nesting of 2D parts (using design software)

This video shows the strategy OtherLab is using to design their soft robots and other creations. To fabricate motors, robots, and other mechanical things OtherLab specializes in, they use a CNC (computer numerical control) machine technique called "nesting". I have discussed rapid prototyping in earlier posts, particularly as it relates to building physical models. In this case, OtherLab is using a technique called nesting. In fabrication programming (as opposed to computational programming), nesting refers to the laying out of multiple parts, some entirely contained within others, on a single surface. This allows for dynamic nesting, which allows for the management of many part sizes and shapes simultaneously. A complex layer is then created, which is subsequently cut and incorporated into a technological design.

Introduction to Pneubotics (elephant soft robot)

Pneubot stands for "pneumatic robot", or a robot that is actuated by pneumatic technology. A pneumatic technology involves the use of compressed air to drive mechanical motion. The compressed air can be moved through soft, balloon-like tubes, which allows for both rigidity (when filled) and flexibility (when decompressed or empty). In this video, an elephant-shaped pneubot is used to demonstrate the level of motor control allowed using this technology. The OtherLab is developing this technology in concert with Manu Prakash's lab at Stanford and DARPA's Maximum Mobility and Manipulation program.

Soft robot design

In this video, a soft-bodied "crawler" robot moves across a flat tabletop and does the limbo, demonstrating the flexibility of gait and movement achievable with soft robot bodies. This "crawler" bot is another instance of the pneubot, just scaled down considerably. The idea of a soft-bodied robot is generally new, and can be applied to a number of medical and industrial problems.

Inflatable Knee Brace

Pneumatics are not only used to build selectively compliant skeletons for robots. They can also be used as braces and muscles, exhibiting rigidity when required. In this video, a knee brace is demonstrated that can force a human knee joint to full extension.

Autonomous Flight (on-board control)

According to the embodiment school of thought, a robot's body and brain are dependent on each other, and interact accordingly. The soft robot examples do not come with an on-board brain. Fully autonomous control (or the robot's ability to control its own beahvior) is a "holy grail" of robotics, as it allows for both remote and on-the-fly operation that does not rely on human input. In this example, the OtherLab group demonstrates autonomous control in a model aircraft, which can maintain a circular heading without external commands.

Heliostatic Actuator

This is another example of autonomous control, this time in the form of actuation. This form of autonomous actuation mimics tropic behaviors observed in many plants and animals. The mirror array shown in the picture/video moves with the sun (e.g. heliostatic), or in this case another light source that moves around the environment with respect to the stationary mirror array. This kind of actuation is currently used in very large solar panel arrays and solar furnaces.