Single Molecule Studies Enabled by Model Based Controller Design

Shreyas Bhaban; Saurav Talukdar; Mingang Li; Thomas Hays; Peter Seiler; Murti Salapaka

Optical tweezers have enabled important insights into intracellular transport through the investigation of motor proteins, with their ability to manipulate particles at the microscale, affording femto newton force resolution. Its use to realize a constant force clamp has enabled vital insights into the behavior of motor proteins under different load conditions. However, the varying nature of disturbances and the effect of thermal noise pose key challenges to force regulation. Furthermore, often the main aim of many studies is to determine the motion of the motor and the statistics related to the motion, which can be at odds with the force regulation objective. In this article, we propose a mixed objective H2/H∞ optimization framework using a model-based design, that achieves the dual goals of force regulation and real time motion estimation with quantifiable guarantees. Here, we minimize the H∞ norm for the force regulation and error in step estimation while maintaining the H2 norm of the noise on step estimate within user specified bounds. We demonstrate the efficacy of the framework through extensive simulations and an experimental implementation using an optical tweezer setup with live samples of the motor protein ‘kinesin’; where regulation of forces below 1 piconewton with errors below 10% is obtained while simultaneously providing real time estimates of motor motion.

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Stochastic Optical Trapping and Manipulation of Micro Object with Neural-Network Adaptation

Xiang Li ; Chien Chern Cheah

Optical tweezers are capable of manipulating micro/nano objects without any physical contact, and therefore widely used in biomedical engineering and biological science. While much progress has been achieved in automated optical manipulation of micro objects, Brownian motion is commonly ignored in the stability analysis in order to simplify the control problem. However, random Brownian perturbations exist in micromanipulation problem and therefore may result in failure of optical trapping due to the escape of micro object from the trap. In addition, it is usually assumed in the development of controller that the model of trapping stiffness is known, but the model is difficult to obtain because of its spatially varying feature around the centre of laser beam and variations with laser power and dimensions of objects. In this paper, a neural-network control method is proposed for optical trapping and manipulation of micro object, in the presence of stochastic perturbations and unknown trapping stiffness. The unknown trapping stiffness and dynamic parameters of micro objects, which vary with different laser power settings and sizes of the objects, are approximated by using adaptive neural networks. The stability analysis is carried out from stochastic perspectives, by considering the effect of Brownian motion in the dynamic model. Both experimental results and simulation results are presented.

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Control of Single-Cell Migration Using a Robot-Aided Stimulus-Induced Manipulation System

Hao Yang ; Xiangpeng Li ; Yong Wang ; Gang Feng ; Dong Sun

Cell migration is a natural movement that occurs in response to stimuli in a living environment. Analysis and control of cell-migration behavior can help elucidate various developmental and maintenance processes of multicellular organisms, thereby contributing to the development of new target therapy approaches. In this study, we successfully achieved the automated control of single-cell migration by utilizing the intrinsic migration ability of cells in an engineering control framework. Chemoattractant-loaded microsource beads trapped by robotically controlled optical tweezers were used to release the stimuli and consequently induce target cells to migrate into a desired region. Cell-movement dynamics under optical tweezer manipulation was analyzed. A new geometric model that confined microsource beads within the effective trapping area of the optical tweezers and the high-motility area of the cell under study was established. Based on this model, we developed a potential field function-based controller that handled the optical tweezers in manipulating the microsource beads, thereby stimulating cells to migrate into desired regions. Simulations and experiments were further performed to verify the effectiveness of the proposed approach.

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Automated Transportation of Multiple Cell Types Using a Robot-Aided Cell Manipulation System with Holographic Optical Tweezers

Songyu Hu ; Shuxun Chen ; Si Chen ; Gang Xu ; Dong Sun

Transferring multiple cell types with high precision and efficiency has become increasingly important as developing cell-based assays. In this study, an enable technology is proposed for simultaneous automated transportation of multiple cell types utilizing a robot-aided cell manipulation system equipped with holographic optical tweezers. The dynamics of trapped cell is initially analyzed. A control constraint is introduced to confine the offset of cells within the optical trap to prevent cells from escaping the trap during transportation. Unlike existing methods determining the critical offset through manual calibration for only a particular cell type, this proposed approach can automatically derive and apply the control constraint to multiple cell types with different radii. A controller is then developed for automated transportation of multiple cell types with different sizes in which exact values of model parameters, such as trapping stiffness and drag coefficient, are not required. Experiments are finally performed on the transportation of yeast cells and osteoblast-like MC3T3-E1 cells to demonstrate the effectiveness of the proposed approach.

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Transportation of Multiple Biological Cells through Saturation-controlled Optical Tweezers in Crowded Microenvironments

Chen, H. ; Wang, C. ; Li, X. ; Sun, D.

Transportation of biological cells has attracted increased attention in bioscience and nanomedicine. Existing approaches to achieve automated multi-cell transportation are generally based on numerous over-strict conditions or assumptions, including static and clean environments, complex theoretical convergence cond itions, omitting tool kinematics, and off-line calibrations. This paper presents a novel approach for the automated transportation of multiple cells by using robotically controlled holographic optical tweezers. First, a swarming controller was developed with easily satisfied convergence conditions. The offset between centers of the cell and optical tweezers was constrained by saturation control to maintain the cells in the optically trapping area. An artificial first-order kinematic model of the tweezers was considered in the controller design to reduce controller oscillation. Second, a solution to the collision avoidance of random-moving obstacles was developed to remove the assumption of static or clean environments. Finally, an automated method based on the drag force model and gradient descent optimization was presented to calibrate cell dynamics online. Experiments on yeast cells were performed to verify the effectiveness of the proposed approach.

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Automated Pairing Manipulation of Biological Cells With a Robot-Tweezers Manipulation System

Mingyang Xie; Yong Wang; Gang Feng; Dong Sun

With an increased demand for various cell-based clinical applications and drug discovery, an enable technology that can automatically locate and pair biological cells from different groups, with high precision and throughput, is highly demanded. This paper presents a novel approach to achieving such cell manipulation using an automatically controlled holographic optical tweezers system, where a robotically controlled optical tweezers functions as a special manipulator to transfer cells automatically. The proposed cell pairing approach utilizes the concept of concentric circles for topology design and the artificial potential field functions for controller development. The significance of the proposed method lies in that the preassignment of cell destinations is not needed, the interdistance amongst the paired cells is controllable, and grouping scalability is not limited. Experiments are performed to demonstrate the effectiveness of the proposed approach.

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Robotic Cell Manipulation Using Optical Tweezers With Unknown Trapping Stiffness and Limited FOV

Xiang Li; Chien Chern Cheah

In existing control methods for optical tweezers, the trapping stiffness is usually assumed to be constant and known exactly. However, the stiffness varies according to the size of the trapped particle and is also dependant on the distance between the center of the laser beam and the particle. It is, therefore, difficult to identify the exact model of the trapping stiffness. In addition, it is also assumed that the entire workspace is visible within the field of view (FOV) of the microscope. During trapping and manipulation, certain image features such as the desired position may leave the FOV, and therefore, visual feedback is not available. In this paper, a robotic setpoint control technique is proposed for optical manipulation with unknown trapping stiffness and limited FOV of the microscope. The proposed method allows the system to operate beyond the FOV and perform trapping and manipulation tasks without any knowledge of the trapping stiffness. The stability of the overall system is analyzed by using Lyapunov-like method, with consideration of the dynamics of both the cell and the manipulator of laser source. Experimental results are presented to illustrate the performance of the proposed method.

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Dynamic Force Sensing Using an Optically Trapped Probing System

Yanan Huang; Peng Cheng; Chia-Hsiang Menq

This paper presents the design of an adaptive observer that is implemented to enable real-time dynamic force sensing and parameter estimation in an optically trapped probing system. According to the principle of separation of estimation and control, the design of this observer is independent of that of the feedback controller when operating within the linear range of the optical trap. Dynamic force sensing, probe steering/clamping, and Brownian motion control can, therefore, be developed separately and activated simultaneously. The adaptive observer utilizes the measured motion of the trapped probe and input control effort to recursively estimate the probe-sample interaction force in real time, along with the estimation of the probing system's trapping bandwidth. This capability is very important to achieving accurate dynamic force sensing in a time-varying process, wherein the trapping dynamics is nonstationary due to local variations of the surrounding medium. The adaptive estimator utilizes the Kalman filter algorithm to compute the time-varying gain in real time and minimize the estimation error for force probing. A series of experiments are conducted to validate the design of and assess the performance of the adaptive observer.

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