Laboratory Robotic System Research Articles

Dynamically optimizing experiment schedules of a laboratory robot system with simulated annealing.

A scheduler has been developed for an integrated laboratory robot system that operates in an always-on mode. The integrated system is designed for imaging plates containing protein crystallization experiments, and it allows crystallographers to enter plates at any time and request that they be imaged at multiple time points in the future. The scheduler must rearrange tasks within the time it takes to image one plate, trading off the quality of the schedule for the speed of the computation. For this reason, the scheduler was based on a simulated annealing algorithm with an objective function that makes use of a linear programming solver. To optimize the scheduler, extensive computational simulations were performed involving a difficult but representative scheduling problem. The simulations explore multiple configurations of the simulated annealing algorithm, including both geometric and adaptive annealing schedules, 3 neighborhood functions, and 20 neighborhood diameters. An optimal configuration was found that produced the best results in less than 60 seconds, well within the window necessary to dynamically reschedule imaging tasks as new plates are entered into the system.

An Integrated Laboratory Robotic System for Autonomous Discovery of Gene Function

Progress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of “Adam,” which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge.

Fully Automated Laboratory Robotic System for Automating Sample Preparation and Analysis to Reduce Cost and Time in Drug Development Process

We have developed a fully automated laboratory robotic system (FA-LAS) capable of conducting most of routine experiments. Main objectives for developing this novel robotic system are to streamline the analytical tasks by automating sample preparations and analytical procedures, and to ensure the safety of analysts by assigning risk-involving procedures, such as handling of highly active substances. FA-LAS integrates numbers of unique functional devices and is highly competent for analyses of various research and development products. Comparative evaluations of FA-LAS with conventional manual preparation for drug substances and pharmaceutical products' real quantitative high performance liquid chromatography (HPLC) tests fulfilled this study's targeted criteria, which are 100 ± 5% for accuracy and coefficient of variation (%CV) ≤ 3% for repeatability for both HPLC analyses of drug substances and pharmaceutical products. In addition, several advantages over conventional manual methods were confirmed, such as reducing “working hour” for analysts, eliminating “working-hour” restriction, and decreasing drastically lag time for each prepared sample toward HPLC analysis. FA-LAS's unique features will work as a bridge between the sample management system and automatic documentation system, and it will reduce drastically in human resources, budgets, and time frame for development of pharmaceutical products.

Gas sensing properties of volume-doped CoTiO 3 synthesized via polyol method

Volume-doped CoTiO 3 was synthesized via the polyol method. As volume dopants Gd, Ho, K, La, Li, Na, Pb, Sb, and Sm with a concentration of 2 at.% were applied. By X-ray diffraction (XRD) materials structure was confirmed, while the materials morphology was imaged using scanning electron microscopy (SEM). Thick films of the nanocrystalline material were applied with a laboratory robotic system to a 8 × 8 multielectrode substrate. We show that the p-type semiconducting material doped with various metals is applicable for the fast detection of ethanol and propylene. The volume dopants affect the sensing and response behavior of the base material.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

From infants to adults, each individual undergoes changes both physically and mentally through interaction with environments. These cognitive developments are usually staged, exhibited as behaviour changes and supported by neural growth and shrinking in the brain. The ultimate goal for an intelligent artificial system is to automatically build its skills in a similar way as the mental development in humans and animals, and adapt to different environments. In this paper, we present an approach to constructing robot coordination skills by developmental learning inspired by developmental psychology and neuroscience. In particular, we investigated the learning of two types of robot coordination skills: intra-modality mapping such as sensor-motor coordination of the arm; and inter-modality mapping such as eye/hand coordination. A self-organising radial basis function (RBF) network is used as the substrate to support the learning process. The RBF network grows or shrinks according to the novelty of the data obtained during the robot interaction with environment, and its parameters are adjusted by a simplified extended Kalman filter algorithm. The paper further reveals the possible biological evidence to support both the system architecture, the learning algorithm, and the adaptation of the system to its bodily changes such as in tool-use. The learning error was regarded as intrinsic motivation to drive the system to actively explore the workspace and reduce the learning errors. We tested our algorithms on a laboratory robot system with two industrial quality manipulator arms and a motorised pan/tilt head carrying a colour CCD camera. The experimental results demonstrated that the system can develop its intra-modal and inter-modal coordination skills by constructing mapping networks in a similar way as humans and animals during their early cognition development. In order to adapt to different tool sizes, the system can quickly reuse and adjust its learned knowledge in terms of the number of neurons, the size of receptive field of each neuron and the contribution from each neuron in the network. The active learning approach greatly reduces the nonuniform distribution of the learning errors.

Automated Robotic Assay of Phosphomonoesterase Activity in Soils

Phosphorus cycling in most ecosystems is dependent on plant and microbiologically derived phosphatase enzymes, and available P limits both microbial and plant growth. Measurements of enzymes in soil systems are often time‐consuming and labor intensive. In this study, we examined phosphomonoesterase activity in soils using a Zymark XP laboratory robotic system for soil handling, solvent addition and exchange, filtration, incubation, reagent addition, and final sample preparation. Phosphomonoesterase activity was measured using phenyl phosphate as substrate, and samples were analyzed using a 96‐well microplate reader. Results indicated that our robotic system was capable of effectively measuring phosphomonoesterase activity in soils differing in physical and chemical characteristics. The results obtained using manual and robotic procedures were comparable in accuracy and precision. The robotic system decreased labor associated with this assay by about a factor of 4.5 relative to the manual system, with considerable savings on reagent costs and labor.

A colorimetric assay suitable for screening epoxide hydrolase activity

A UV/VIS spectrophotometric microtiter-plate and a filter-paper based assay using 4-( p-nitrobenzyl)pyridine (NBP) were developed to determine epoxide hydrolytic activity by measuring the decrease of the epoxide concentration. Both systems were applied for screening an expression gene bank of Rhodococcus sp. NCIMB 11216. As a reference, whole cells from Rhodococcus sp. NCIMB 11216 and Beauveria sulfurescens ATCC 7159 exhibiting epoxide hydrolase activity were used. The microtiter-plate system was also evaluated for different epoxides and performed in a laboratory robotic system for high throughput screening. The microtiter-plate assay showed a high sensitivity for the detection of small concentrations of epoxides (0.1–1 mg/well) such as styrene oxide, ethyl phenylglycidate, n-hexane oxide and indene oxide. The filter paper assay was further optimized for styrene oxide. Both assays were suitable to screen within libraries of epoxide hydrolases without interference with other enzymes such as esterases, lipases or proteases. The assay should allow to screen large libraries obtained by directed evolution, strain collections and (expression) gene banks for epoxide hydrolytic activity or to monitor the purification process of an epoxide hydrolase.

Intelligent image analysis for error detection and correction in automated laboratory robot systems

Error detection and correction are essential steps in developing robust automated laboratory systems involving robots. Single-point sensors can be used to detect errors when the anticipated number is small. But the wide range of errors that can occur in an automated laboratory system makes this an impossible or impractical approach. Computer vision and image analysis techniques can be used to significantly broaden the range of dynamically identifiable error conditions. Object recognition and neural networks can be used to provide further characteristics of an identified error. By combining these techniques, it is often possible to extract sufficient information to direct a laboratory robot to clear or avoid an identified error, allowing the automated laboratory task to continue without human intervention. This has the potential to dramatically improve the overall reliability of an automated laboratory robot system. In this article, we report on the application of computer vision and neural networks to the detection of errors that can occur in a robot system designed to automate the loading and unloading of a laboratory centrifuge. © 1998 John Wiley & Sons, Inc. Lab Robotics and Automation 10: 273–282, 1998

Intelligent image analysis for error detection and correction in automated laboratory robot systems

Error detection and correction are essential steps in developing robust automated laboratory systems involving robots. Single-point sensors can be used to detect errors when the anticipated number is small. But the wide range of errors that can occur in an automated laboratory system makes this an impossible or impractical approach. Computer vision and image analysis techniques can be used to significantly broaden the range of dynamically identifiable error conditions. Object recognition and neural networks can be used to provide further characteristics of an identified error. By combining these techniques, it is often possible to extract sufficient information to direct a laboratory robot to clear or avoid an identified error, allowing the automated laboratory task to continue without human intervention. This has the potential to dramatically improve the overall reliability of an automated laboratory robot system. In this article, we report on the application of computer vision and neural networks to the detection of errors that can occur in a robot system designed to automate the loading and unloading of a laboratory centrifuge. © 1998 John Wiley & Sons, Inc. Lab Robotics and Automation 10: 273–282, 1998

The automation of solution phase synthetic chemistry using XP Zymate™ laboratory robotic systems

The automation of solution phase synthetic chemistry using XP Zymate™ laboratory robotic systems

The automation of solution phase synthetic chemistry using XP Zymate™ laboratory robotic systems

The challenge of developing automated systems to undertake solution phase synthesis was embarked upon at Zeneca Pharmaceuticals in 1992. The objective of this project, using XP Zymate™ laboratory robots, has been realised with the establishment of a Research Department facility that has enabled research chemists to utilise the systems to generate compounds for test in relevant biological screens. The systems developed use the minimum of modification to the chemical method of reaction assembly, execution and isolation required of the products. This year will see the expansion of the Robotics facility from 3 to 7 systems. To date over 8000 novel individual compounds have been synthesised. Products have been purified where necessary, and over 30 different types of chemistry have been utilised. The work undertaken has covered the full scope of synthetic chemistry in the research process, incorporating library generation for high throughput screening, lead identification, and lead optimisation. To describe the work of the group an overview of the capabilities of the systems and the types of work undertaken, within the facility, is presented.

Use of laboratory robotics for gel permeation chromatography sample preparation: Automation of high‐temperature polymer dissolution

Manual preparation of polymer samples for molecular weight characterization by solution methods such as gel permeation chromatography (GPC) is a time-consuming, labor-intensive, and redundant task. Preparation of samples for high-temperature GPC characterization further complicates the procedure. A typical manual high-temperature sample preparation exposes the analyst to both hot surfaces and solvent vapors. An automated system to prepare samples for high temperature GPC analysis has been developed. The system is based on a Zymark laboratory robotic system, and custom hardware peripherals developed at The Dow Chemical Company. The automated procedure performs all the steps required to prepare samples for high-temperature GPC analysis, including the hot steps. The samples were analyzed using the Waters 150-C GPC in combination with the differential refractive index (DRI) and low-angle laser light scattering (LALLS) detectors to demonstrate the reproducibility and reliability of the automated procedure. The system hardware, software options, and performance are presented in this paper. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 64: 1613–1623, 1997

Use of laboratory robotics for gel permeation chromatography sample preparation: Automation of high-temperature polymer dissolution

Manual preparation of polymer samples for molecular weight characterization by solution methods such as gel permeation chromatography (GPC) is a time-consuming, labor-intensive, and redundant task. Preparation of samples for high-temperature GPC characterization further complicates the procedure. A typical manual high-temperature sample preparation exposes the analyst to both hot surfaces and solvent vapors. An automated system to prepare samples for high temperature GPC analysis has been developed. The system is based on a Zymark laboratory robotic system, and custom hardware peripherals developed at The Dow Chemical Company. The automated procedure performs all the steps required to prepare samples for high-temperature GPC analysis, including the hot steps. The samples were analyzed using the Waters 150-C GPC in combination with the differential refractive index (DRI) and low-angle laser light scattering (LALLS) detectors to demonstrate the reproducibility and reliability of the automated procedure. The system hardware, software options, and performance are presented in this paper. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 64: 1613–1623, 1997

Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

This paper presents the design and experiment of a fuzzy proportional integral derivative (PID) controller for a flexible-joint robot arm with uncertainties from time-varying loads. Experimental results have shown remarkable tracking performance of this fuzzy PID controller, and have convincingly demonstrated that fuzzy logic control can be used for flexible-joint robot arms with uncertainties and it is quite robust. In this paper, the fuzzy PID controller is first described briefly, using a simple and practical PD+I controller configuration. This configuration preserves the linear structure of the conventional PD+I controller, but has nonconstant gains: the proportional, integral, and derivative gains are nonlinear functions of their input signals, which have self-tuning (adaptive) capabilities in set-point tracking performance. Moreover, these variable gains make the fuzzy PID controller robust with faster response time and less overshoot than its conventional counterpart. The proposed design was tested using a flexible-joint robot arm driven by a DC motor in a laboratory, where the arm was experienced with time-varying loads. Control performance by the conventional and fuzzy PID controllers for such a laboratory robotic system are both included in this paper for comparison.

Robotics and the changing face of the clinical laboratory

Rapid changes in healthcare coupled with parallel advances in technology have stimulated the evolution of new approaches for laboratory automation. In particular, the emergence of commercially available laboratory robotic systems offers promise for streamlining the clinical laboratory. Increasing cost-containment pressures make the application of this technology extremely attractive, and several organizations have begun to systematically integrate robotic devices into their laboratory automation schemes. Integration of these technologies, however, presents many challenges for software developers, instrument manufacturers, and laboratory workers. Differing needs across laboratories require flexibility and intelligence in robots, instruments, and control systems. Standardization of mechanical and electronic interfaces will be key to making these systems easy to integrate. Systems engineering, aided by simulation modeling and artificial intelligence schemes, will be important to assist in the design of optimal configurations. Software for the overall control of integrated automation will be needed that can be tailored by the laboratorian to fit the requirements of the individual laboratory. Thus, laboratory workers will need to be actively involved in implementing this new wave of laboratory automation, becoming well-versed in computers, electronics, and systems engineering.

Plasma input function determination for PET using a commercial laboratory robot

A commercial laboratory robot system (Zymate PyTechnology II Laboratory Automation System) was interfaced to standard and custom laboratory equipment and programmed to perform rapid radiochemical assays necessary for plasma input function determination in quantitative PET studies in humans and baboons. A Zymark XP robot arm was used to carry out two assays: (1) the determination of total plasma radioactivity concentrations in a series of small-volume whole blood samples and (2) the determination of unchanged (parent) radiotracer in plasma using only solid phase extraction methods. Steady state robotic throughput for determination of total plasma radioactivity in whole blood samples (0.350 mL) is 14.3 samples/h, which includes automated centrifugation, pipetting, weighing and radioactivity counting. Robotic throughput for the assay of parent radiotracer in plasma is 4–6 samples/h depending on the radiotracer. Percents of total radioactivities present as parent radiotracers at 60 min. postinjection of 25 ± 5.0 ( N = 25), 26 ± 6.8 ( N = 68), 13 ± 4.4 ( N = 30), 32 ± 7.2 ( N = 18), 16 ± 4.9 ( N = 20), were obtained for carbon-11 labeled benztropine, raclopride, methylphenidate, SR 46349B (trans, 4-[(3Z)3-(2-dimethylamino-ethyl) oxyimino-3 (2-fluorophenyl)propen-1-yl]phenol), and cocaine respectively in baboon plasma and 84 ± 6.4 ( N = 9), 18 ± 11 ( N = 10), 74 ± 5.7 ( N = 118) and 16 ± 3.7 ( N = 18) for carbon-11 labeled benztropine, deprenyl, raclopride, and methylphenidate respectively in human plasma. The automated system has been used for more than 4 years for all plasma analyses for 7 different C-11 labeled compounds used routinely in our laboratory. The robotic radiotracer assay runs unattended and includes automated cleanup procedures that eliminates all human contact with plasma-contaminated containers.

Fully automated sample preparation and electrospray mass spectrometry analysis

AbstractA fully automated sample preparation–mass spectrometry system has been described which prepares samples prior to electrospray mass spectrometry analysis, injects the samples, and acquires and processes the data. Fifty samples can be analysed without operator intervention within 8 hours. This type of system ensures optimum use of both the mass spectrometer, which can be in operation 24 hours per day, and the mass spectroscopist, who can concentrate on more challenging samples and spectral interpretation. The laboratory robot system used can be easily adapted for different types of sample preparation and other methods of analysis. For example, in addition to a spectrum exhibiting mainly protonated or deprotonated molecular ions, data could be acquired concurrently with a higher cone voltage, or under tandem mass spectrometry conditions, to generate structurally informative fragment ions. This sample preparation method has been adapted to encompass a variety of samples such as proteins and nucleic acids.

Integration of robotics and chemometrics for the automated optimisation of drug extractions

The liquid—liquid extraction of tricyclic amines (antidepressants) was optimised using mixture experimental designs, the physical part of which was performed by a laboratory robotic system. This system was controlled and directed by a second, external computer. After extraction, phase separation, evaporation of the organic phase and reconstitution of the residue, the final step was the on-line injection of the extracts in the reversed phase high-performance liquid chromatographic (RP-HPLC) system. Criteria used in this optimisation procedure were the extraction yield (recovery) and the partition coefficient. For simultaneous extraction of several compounds the minimal recovery was used as criterion. A totally automated extraction optimisation procedure was now obtained. It was concluded that the robot is suitable for the performance of automated optimisation.

Recent advances in robotic automation of microplate assays

Robotic systems have been used for ten years in automating microplate assays. Today, microplate assays represent the single largest application of laboratory robotic systems. The need to improving automated microplate assays is speed, throughput and the ability to work with small liquid volumes. This paper will discuss each step in a microplate assay, what the current technology can accomplish and what new advances are under development. Results from current systems, capable of performing one million assays per year, will be presented.

Optimization of a Liquid Chromatographic Method Based on Information Theory: Application of the Method to Dissolution Testing

Abstract The simultaneous optimization of mobile phase composition, column length, detection wavelength, and flow rate was applied to the development of an assay method for dissolution testing of multi-ingredient formulations. This optimization procedure is called Total Chromatographic Optimization and is based on the information theory of chromatography. Optimal chromatographic conditions resulted in very rapid and precise measurements. The dissolution test procedures, including sampling, dilution, filtering, and assay, were performed automatically by a laboratory robotic system. The dissolution medium was sampled every 15 min and analyzed immediately for drug concentration.