Stochastic properties of actomyosin motor

How to Build a Biological Machine Using Engineering Materials and Methods

We present work in 3D printing electric motors from basic materials as the key to building a self-replicating machine to colonise the Moon. First, we explore the nature of the biological realm to ascertain its essence, particularly in relation to the origin of life when the inanimate became animate. We take an expansive view of this to ascertain parallels between the biological and the manufactured worlds. Life must have emerged from the available raw material on Earth and, similarly, a self-replicating machine must exploit and leverage the available resources on the Moon. We then examine these lessons to explore the construction of a self-replicating machine using a universal constructor. It is through the universal constructor that the actuator emerges as critical. We propose that 3D printing constitutes an analogue of the biological ribosome and that 3D printing may constitute a universal construction mechanism. Following a description of our progress in 3D printing motors, we suggest that this engineering effort can inform biology, that motors are a key facet of living organisms and illustrate the importance of motors in biology viewed from the perspective of engineering (in the Feynman spirit of “what I cannot create, I cannot understand”).

Broadcast Feedback of Stochastic Cellular Actuators Inspired by Biological Muscle Control

This paper presents a broadcast feedback approach to the distributed stochastic control of an actuator system consisting of many cellular units. This control architecture was inspired by skeletal muscles comprising a vast number of tiny functional units, called sarcomeres. The output of the actuator system is an aggregate e fect of numerous cellular units, each taking a bistable ON—OFF state. A central controller “broadcasts” the error between the aggregate output and a reference input. Rather than ordering the individual units to take specific states, the central controller merely broadcasts the overall error signal to all the cellular units uniformly. In turn each cellular unit makes a stochastic decision with a state transition probability, which is modulated in relation to the broadcasted error. Stochastic properties of both open-loop and closed-loop control systems are analyzed. Stability conditions of the broadcast feedback system are obtained by using a stochastic Lyapunov function. The proposed method is simulated for an artificial cellular actuator, consisting of many segments of smart actuator material. Theoretical results are verified through simulation. It is demonstrated that, even in the absence of deterministic coordination, the ensemble of the cellular units can track a given trajectory stably and robustly.

Translation initiation in eukaryotes: versatility of the scanning model

It is generally accepted that the initiation of translation in eukaryotes involves the binding of the 40S ribosomal subunit to the capped 5' end of an mRNA and subsequent scanning of 5' UTR in search of an initiation codon. However, until recently this has remained a mere hypothesis. This review describes the novel experimental evidence in support of this classical model. Data on the participation of various factors in the eukaryotic initiation process are summarized. The sequence of initiation events is described in light of the latest experimental data. The existing physical models of scanning are presented. Special attention is paid to discussion of alternative models of eukaryotic initiation of translation. It is demonstrated that the canonical mechanism of initiation is more versatile than previously thought.

A fingerprint method for variability and robustness analysis of stochastically controlled cellular actuator arrays

In this paper we present a ‘fingerprint method’ for modeling and subsequently characterizing stochastically controlled actuator arrays. The actuator arrays are built from small actuator cells with structural elasticity. These cells are controlled using a bistable stochastic process wherein all cells are given a common input probability (control) value which they use to determine whether to actuate or relax. Arranging the cells in different networks gives different actuator array properties, which must be found before the actuator arrays can be applied to manipulators. The fingerprint method is used to describe and automatically generate every possible stochastic actuator array topology for a given number of cells, and to calculate actuator array properties such as: travel, required actuator strength/displacement, force range, force variance, and robustness for any array topology. The properties of several illustrative examples are shown and a discussion covers the importance of the properties, and trends between actuator array layouts and their properties. Finally, results from a validation experiment using a stochastically controlled solenoid array are presented.

A probabilistic model of glenohumeral external rotation strength for healthy normals and rotator cuff tear cases

The reigning paradigm of musculoskeletal modeling is to construct deterministic models from parameters of an "average" subject and make predictions for muscle forces and joint torques with this model. This approach is limited because it does not perform well for outliers, and it does not model the effects of population parameter variability. The purpose of this study was to simulate variability in musculoskeletal parameters on glenohumeral external rotation strength in healthy normals, and in rotator cuff tear case using a Monte Carlo model. The goal was to determine if variability in musculoskeletal parameters could quantifiably explain variability in glenohumeral external rotation strength. Multivariate Gamma distributions for musculoskeletal architecture and moment arm were constructed from empirical data. Gamma distributions of measured joint strength were constructed. Parameters were sampled from the distributions and input to the model to predict muscle forces and joint torques. The model predicted measured joint torques for healthy normals, subjects with supraspinatus tears, and subjects with infraspinatus-supraspinatus tears with small error. Muscle forces for the three conditions were predicted and compared. Variability in measured torques can be explained by differences in parameter variability.

The Neck Domain of Myosin II Primarily Regulates the Actomyosin Kinetics, not the Stepsize

In order to study the role of the neck domain of myosin in muscle contraction, we measured the steps of individual myosin II molecules engineered to have no neck domain (light chain-binding domain) by optical trapping nanometry. The actin filament and myosin cofilaments interacted on a glass surface to minimize the angle between them, and to minimize the interaction between myosin and the glass surface. The results showed that the average myosin stepsize did not change much when the neck domain was removed, but the sliding velocity decreased approximately fivefold. Furthermore, the duration of steps for neckless myosin was several times longer at saturated ATP concentration, indicating that the slower velocity was due to a slower dissociation rate of myosin heads from actin. From these data, we conclude that the neck domain of myosin-II primarily regulates the actomyosin kinetics, not the mechanics.