Control in technological systems and physical intelligence: an emerging theory

The transduction and processing of physical information is becoming important in a range of research fields, from the design of materials and virtual environments to the dynamics of cellular microenvironments. Previous approaches such as morphological computation/soft robotics, neuromechanics, and embodiment have provided valuable insight. This work approaches haptic, proprioception, and physical sensing as all part of the same subject. In this presentation, three design criteria for applying physical intelligence to engineering applications will be presented. These criteria have several properties in common, which inspires two types of end-effector model: stochastic (based on a spring) and deterministic (based on a piezomechanical array). The generalized behavior and output dynamics of these models can be described as three findings summarized from previous work. In conclusion, future directions for modeling neural control using a neuromorphic approach will be discussed

Natural Variation and Neuromechanical Systems

Natural variation plays an important but subtle and often ignored role in neuromechanical systems. This is especially important when designing for living or hybrid systems \ud which involve a biological or self-assembling component. Accounting for natural variation can be accomplished by taking a population phenomics approach to modeling and analyzing such systems. I will advocate the position that noise in neuromechanical systems is partially represented by natural variation inherent in user physiology. Furthermore, this noise can be augmentative in systems that couple physiological systems with technology. There are several tools and approaches that can be borrowed from computational biology to characterize the populations of users as they interact with the technology. In addition to transplanted approaches, the potential of natural variation can be understood as having a range of effects on both the individual's physiology and function of the living/hybrid system over time. Finally, accounting for natural variation can be put to good use in human-machine system design, as three prescriptions for exploiting variation in design are proposed

Cellular decision-making bias: the missing ingredient in cell functional diversity

Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.Comment: 18 pages; 6 figures, 2 tables, 4 supplemental figure

Formal Systems Architectures for Biology

When the word "systems" is used in systems biology, it invokes a variety of assumptions about what defines the subject under investigation, which in turn can lead to divergent research outcomes. We will take the position that systems are defined by their potential organizing and "control" mechanisms, 
which distinguishes complex, living systems from a primordial soup. This will be accomplished by defining and investigating three interesting control motifs in biological systems: dominoes and clocks, futile cycles, and complex feedforward regulation. Additional mechanisms that combine feedback and feedforward mechanisms will also be briefly elaborated upon. Throughout these examples, our focus will be on the connection between top-down control mechanisms and bottom-up self-organizing mechanisms

Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems

In this paper, a method for decoupling the neuromuscular function of a set of limbs from the role morphology plays in regulating the performance of an activity is introduced. This method is based on two previous methods: the rescaled range analysis specific to time series data, and the use of scaling laws. A review of the literature suggests that limb geometry can either facilitate or constrain performance as measured experimentally. Whether limb geometry is facilitatory or acts as a constraint depends on the size differential between arm morphology and the underlying muscle. "Changes in size and shape" are theoretically extrapolations of morphological geometry to other members of a population or species, to other species, or to technological manipulations of an individual via prosthetic devices. Three datasets are analyzed using the range-based method and a Monte-Carlo simulation, and are used to test the various ways of executing this analysis. It was found that when performance is kept stable but limb size and shape is scaled by a factor of .25, the greatest gain in performance results. It was also found that introducing force-based perturbations results in 'shifts' in the body geometry/performance relationship. While results such as this could be interpreted as a statistical artifact, the non-linear rise within a measurement class and linear decrease between measurement classes suggests an effect of scale in the optimality of this relationship. Overall, range-based techniques allow for the simulation and modeling of myriad changes in phenotype that result from biological and technological manipulation