The MAXAMIZE advisory system determines from user-provided restriction maps an optimal ' strategy to do nucleotide sequencing by methods involving end-labeled fragments. The maps may be either simple linear restriction maps of fragments or complex circular maps including restriction sites of a vector. The whole system is interactive and is written in the Genetic English language provided by the GENESIS System, a molecular genetics knowledge representation and manipulation package. In addition, MAXAMIZE provides bookkeeping facilities for sequencing and offers advice on how to verify the newly obtained sequence data. Introduction Using the knowledge representation and manipulation tools developed by the MOLGEN project [1], we have developed a system which provides assistance in determining a nucleotide sequence using a given restriction map. The problem is to derive an efficient sequencing strategy which minimizes ttie number of gels run and sequencing reactions performed and maximizes the number of nucleotides read in each step. Starting from the restriction map, the sequencing advisor determines how much sequence information can be obtained considering both the average length the user can sequence from any labeled terminus and whether the fragments can be separated. Having determined the best order of successive digests, the system predicts for each digest the pattern of fragments (on a gel) and advises which of them have to be eluted and sequenced. In addition, the sequencing advisor provides the user with bookkeeping capacities which include the ability to continually update the restriction-map with the newly gathered sequence information. Also, the user is advised: 1. how to verify the experimentally determined sequence 2. how to map RNA ends (5, 3' or both) using the nuclease protection technique [2] 3. which additional enzymes might be good cutter candidates in a region where there are no sites for enzymes already tested. This advisor is most useful for directed sequencing performed using the Maxam and Gilbert method [3] [4]. See [5] for the description of a system which assists in providing bookkeeping for both site specific and random sequencing experiments. © IRL Press limited, 1 Falconborg Court, London W1V 5FG. U.K. 295 Downloaded from https://academic.oup.com/nar/article-abstract/10/1/295/2358573/MAXAMIZE-A-DNA-sequencing-strategy-advisor by guest on 15 September 2017 Nucleic Acids Research Method of Solution The sequencing advisor is written in a subset of Genetic English (Genglish) provided by GENESIS [1]. GENESIS is based on the Unit System [6] [7] [8] [9], a general-purpose knowledge acquisition program written in Interiisp, which runs on the Digital Equipment Corporation DecSystem 10 and 20 series of computers. GENESIS provides the ability to represent store and modify molecular genetic information 3uch as restriction maps, sequences, and restriction enzyme properties, as well as more general types of information like numbers, strings, lists, and tables. The total collection of Information is known as a knowledge base, and may be easily examined, shared, and updated by a variety of users. Furthermore, the system allows manipulation of the knowledge base by using Genglish, the Genetic English language. This language allows a non-programmer molecular biologist to construct sophisticated computational systems that embody domain-specific expertise. For the case of the system discussed in this paper, all developmental work was performed by one of the authors (R.B.), a molecular biologist with essentially no programming experience. Sequencing Experiment Description The scientist describes his problem by providing MAXAMIZE with a restriction map constructed with the GENESIS map editor. The restriction map can describe one of two type3 of DNA structures: either just the DNA of interest, or a vector containing that DNA. In the latter case, the user has to indicate which region is the inserted DNA by marking it as a specific region on the restriction map. MAXAMIZE first establishes the list of restriction enzymes cutting within the inserted DNA fragment, (or, by default, the whole molecule). Editing facilities allow modification of that list by the user, for example, to avoid using a particular enzyme because he had temporarily exhausted his supply. The user is next asked to provide several experimental parameters: • the average number of bases the user is able to read off a gel • the minimum size difference of two fragments which allows purification • the coordinates of the region(s) to be sequenced • the names of the restriction enzymes (from the list discussed above) which are relevant to the sequencing strategy problem. An example of the experiment description phase is presented below. All user responses are shown in underlined letters: we have added comments in italics. <CR> stands for carriage-return. ARE YOU FAMILIAR WITH MAXAMIZE? : fl MAXAMIZE w i l l determine an optimal s t ra tegy to sequence using any def ined res t r i c t i on -map . The res t r i c t i on -map 1s described by using the GENESIS map e d i t o r . I f you are prov id ing a vector map which Includes an Inser ted DNA sequence, then you should def ine a region named INSERT on the map.

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Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome have been right if the outcome had been the desired one. The algorithm makes use of these intermediate successes to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it make sense in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

The Journal of Automated Reasoning is an interdisciplinary journal that maintains a balance between theory, implementation and application. The spectrum of material published ranges from the presentation of a new inference rule with proofs of its logical properties to a detailed account of a computer program designed to solve some problem from industry. The main fields cov-ered are automated theorem proving, logic program-ming, expert systems, program synthesis and validation, artificial intelligence, computational logic, robotics, and various industrial applications. The papers share the common feature of focusing on some aspects of automated reasoning, a field whose objective is the design and implementation of a computer program that serves as an assistant in solving problems and in answering questions that require reasoning. The Journal of Automated Reasoning provides a forum and a means for ex-changing information for those interested purely in theory, those interested primarily in implementation, and those interested in specific research and industrial applications. DSS Experiences, Management, and Education, experiences in developing or operating DSSs; systems solutions to specific decision support needs; approaches to managing approaches.

This paper presents a technique for statistically characterizing a search space and demonstrates the use of this technique within a practical telescope scheduling application. The characterization provides the following: (i) an estimate of the search space size, (ii) a scaling technique for multi-attribute objective functions and search heuristics, (iii) a quality density function for schedules in a search space, (iv) a measure of a scheduler's performance, and (v) support for constructing and tuning search heuristics. This paper describes the random sampling algorithm used to construct this characterization and explains how it can be used to produce this information. As an example, we include a comparative analysis of an heuristic dispatch scheduler and a look-ahead scheduler that performs greedy search.

One of the most significant new opportunities that the Space Station affords cell biologists is the ability to do long-term cultivation of cells in the space environment. This facility is essential for investigations that are primarily focused on effects requiring a longer timeline of observation than that provided by the STS (Space Transportation System) platform. Such work requires both very strong laboratory skills to properly and quickly interact with the hardware hosting the culture and deep knowledge of the cell biology domain in order to optimally react to unanticipated scientific developments. Such work can be enabled by advanced automation techniques that have recently been used in the STS-based Spacelab, and that are being readied for the Space Station. In this paper, we describe the adaptation of PI-in-a-Box, the first interactive space science assistant system, to the study of the effects of space flight on cell cycle progression and proliferation.

In October 1993, the Astronaut Science Advisor (ASA) was on board the STS-58 flight of the space shuttle. ASA is an interactive system providing data acquisition and analysis, experiment step re-scheduling, and various other forms of reasoning. As fielded, the system runs on a single Macintosh PowerBook 170, which hosts the six ASA modules. There is one other piece of hardware, an external (GW Instruments, Sommerville, Massachusetts) analog-to-digital converter connected to the PowerBook's SCSI port. Three main software tools were used: LabVIEW, CLIPS, and HyperCard: First, a module written in LabVIEW (National Instruments, Austin, Texas) controls the A/D conversion and stores the resulting data in appropriate arrays. This module also analyzes the numerical data to produce a small set of characteristic numbers or symbols describing the results of an experiment trial. Second, a forward-chaining inference system written in CLIPS (NASA) uses the symbolic information provided by the first stage with a static rule base to infer decisions about the experiment. This expert system shell is used by the system for diagnosis. The third component of the system is the user interface, written in HyperCard (Claris Inc. and Apple Inc., both in Cupertino, California).

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