Journal of Laboratory Automation

SpectroML, a markup language for ultraviolet-visible spectroscopy data, has been developed as a “Web-aware” mechanism for instrument-to-instrument, instrument-to-application, and application-to-application data interchange and archiving. This article documents the application of SpectroML to the interchange and archiving of measurement data from three spectrophotometers that are used in the NIST optical filter standards program. It describes how result data from the NIST national reference spectrophotometer and two commercial spectrophotometers are converted into SpectroML format and how SpectroML-formatted data and metadata are imported into the optical filter standards database.

Automated chemical workstations capable of parallel, adaptive experimentation are well suited for performing reaction optimization. A new method for simplex-based optimization has been developed that enables multiple multidirectional search (MDS) procedures to be implemented in parallel. The MDS method employs a simplex with n + 1 points, where n is the number of dimensions or variables of the optimization. After the initial simplex is evaluated, a new simplex is generated by reflection about the one best point, where n new points are projected and all but the one best point are discarded. The MDS movements continue in the same fashion until a termination criterion is satisfied. The parallel version (PMDS') enables multiple, distinct optimizations in parallel in a single search space to find the global optimum, or individual optimizations in parallel of multiple independent search spaces. The PMDS' method is complementary to the parallel Simplex search method, in which only one new point is projected to generate a new simplex in each cycle of experimentation.

One of the challenges in applying automated chemistry workstations to problems of reaction optimization entails choosing an appropriate optimization algorithm. In the study described herein, 10 different algorithms have been examined for efficacy in searching reaction spaces using scenarios that explore effects of workstation parallelism and search space size. The algorithms differ in scheduling (serial vs. parallel), adaptive features (open loop vs. closed loop), and methods for stepping through the search space. Several two-tiered algorithms enable a breadth-first survey followed by an indepth optimization. For a workstation with modest parallel capacity, a parallel but nonadaptive algorithm is most effective in small or coarse-grained search spaces, whereas parallel adaptive algorithms are superior for examining large or fine-grained search spaces. The parallel adaptive algorithms become increasingly effective as the size of the search space increases. A serial algorithm is most attractive with a serial workstation, or when chemical resources are limited regardless of workstation or search space. The breadth-first survey of the twotiered algorithms significantly improves the efficiency of the subsequent in-depth optimization. The results obtained provide guidance in choosing optimization algorithms, designing more sophisticated algorithms, and developing workstations with parallel and/or adaptive features that use such algorithms.

The Analytical Information Markup Language (AnIML) is a standardization effort of ASTM (formerly American Society for Testing and Materials) Subcommittee E13.15 on Analytical Data. AnIML provides an XML-based format for analytical data. It is designed specifically for spectroscopy and chromatography data, but is suitable for use with many different analytical measurement techniques. AnIML consists of a generic core structure that permits the storage of arbitrary analytical data. These include multi-dimensional data, name-value pairs, and hierarchies. The concept of technique definitions permits the formal specification of constraints for usage of the core. This way, a definition can prescribe how the data for specific measurement techniques should be captured in the data file. To address changing requirements, AnIML supports an extension concept that allows vendors or end users to specify additional data that should be stored for a technique. These extensions can also be formally documented so that they do not break compatibility with existing software. This article presents an overview of AnIML and demonstrates how AnIML can be used to record data from everyday experimental workflows in a laboratoryenvironment. Issues related to the usage of AnIML in regulated environments are also discussed, including the use of digital signatures and audit trail functionality to ensure data integrity.

Laboratory environments are controlled more and more by automated systems. Written procedures and lab journals are replaced by workflow description languages and electronic notebooks, which not only describe the processes but are used also for the control of the entire workbench, data acquisition, and documentation. Dynamic scheduling is needed in such an environment. Multiple samples with different procedures are processed in parallel, competing for the instruments. The whole environment may be also underlaid by optimization strategies like throughput or minimum sample processing time.

The modeling of all components interacting on the workbench—samples, devices, sensors, results, database systems, and so on—needs to rely on concepts building a consistent framework. This article gives a set of terms and definitions used in a dynamic scheduling environment. It describes most of these entities in detail, including their functionality and attributes as well as their logical and physical interactions. It also describes concepts such as workflows with activities and constraints, functional libraries, hidden transport, and dynamic execution. Maintenance, calibration, and error management also are included. Finally, it discusses how the entities interact with the different components in the scheduling system.

We present a Web-based system developed at Bristol-Myers Squibb that provides status and queue information for a dispersed group of analytical instruments. Status and queue files maintained by the analytical software on individual instrument computers are copied to a central server, where they are parsed by a custom software application written in Microsoft Visual Basic 6. The parsed information is stored in a database, where it is accessible to custom web server scripts that search, filter, and format the data for display in a Web browser.

Within the past few years a new paradigm for software organization and intercommunication has emerged—SOA, or Service-Oriented Architecture. This article explores the relevance of this approach to automation and control of laboratory devices and examines some of the excellent open-source software tools, libraries, and frameworks available to implement software according to SOA principles. Not only can the open-source packages described here serve as the basis for practical laboratory automation systems, they also can serve an educational role to illustrate the principles and advantages of an SOA approach. Open-source software represents a significant opportunity for leveraging the skills and budgets of small automation development groups to produce very sophisticated and reliable software systems that would otherwise be out of reach for such small teams. An added benefit is that because of the standardized protocols involved, opportunities for interoperation with other systems that are following a similar approach becomes a realistic possibility. Although intellectual property and licensing issues may represent a barrier to the open-source approach for commercial developers seeking to market their solutions, it may be the ideal approach for many in-house projects and for standardized elements within commercial packages.

Most of the scheduling software and instrument integration frameworks are written in Visual Basic, C/C++, or the LabView programming environment. A lot of these frameworks are proprietary tools of instrument vendors and are used by these companies during system integration of their instruments. In addition to the closed architecture of these products, the scheduler choice is very limited. ReTiSoft Inc. has created a suite of software products that address these problems.

In this article we would like to introduce ReTiSoft's open-architecture framework for instrument integration, a hybrid scheduler (static and dynamic) and a Web-enabled interface to the automated system. In addition to ReTiSoft's integration framework (Genera) and the hybrid-scheduling software (Supra), we recently developed a Web-enabled application that allows scientists to log onto the automated system remotely, set up and run assays, examine and analyze the data produced during the experiment. The software is called DataPilot and is comprised of a high-performance database engine and the Apache Web server.

In unison with Genera's and Supra's open-architecture approach, DataPilot can be modified and customized by system integrators to suit their specific application needs. The application serves as a data repository and adheres to guidelines presented by the Code of Federal Regulations for electronic records and electronic signatures; the guidelines are known as 21 CFR Part 11.

This article provides an architectural overview of our software products and justifies its merits in comparison to other technologies commonly used in laboratories. We describe our Genera integration framework, give an overview of our scheduling algorithms, describe a Web-enabled data-tracking software and the enzyme-linked immunosorbent assay (ELISA assay), and describe different methods of automated system validation using our software. We also offer some conclusions.

Laboratory informatics is defined as the specialized application of information technology to optimize and extend laboratory operations. Rising with the tide of informatics in general, laboratory informatics is one of the fastest growing areas of laboratory-related technology. However, this technological growth has outstripped the expertise of the ones who stand to gain most from it: scientists and other end users in the laboratory. This gap could be bridged by specialists in laboratory informatics who are well grounded in the scientific basis of lab operations, yet also trained in informatics and its particular applications in the lab. However, formal educational programs in laboratory informatics are lacking. To set an educational agenda, laboratory informatics must be delineated as a field, and based on that delineation, a curriculum must be developed that meets the standards of higher education. To address these issues, this paper gives an overview of informatics, describes the context of laboratory informatics in this setting, explains the emergence of laboratory informatics as a distinct field, sets the place for laboratory informatics in higher education by suggesting the nature and scope of the curriculum, and briefly describes the laboratory informatics initiative at Indiana University.