Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

The National Patient Safety Foundation has sponsored a report to describe the current research being pursued in the area of medical error reduction and to identify the gaps in this effort. 1 A total of 23 gaps were identified—among them was a need for more research in the area of communication and information sharing. One of the areas that we see a need to explore is the wealth of published information in the form of medical algorithms. Centralizing and automating medical algorithms is one way to share information among a wide range of clinical care providers. Furthermore, automation of medical algorithms assists in the correct selection (reducing errors of planning) and application of that information (reducing errors of execution). The purpose of a medical algorithm is to improve the delivery of medical care. A previous effort revealed 16 types of algorithms that were encountered during the construction of a centralized repository of such algorithms ranging from simple calculations such as determining Body-Mass Index to complicated formulas predicting clinical outcomes. 2 Issued we have identified include: 1) Not Using an Algorithm which can be further characterized as causing the following errors or inefficiencies: a) Failure to use an algorithm when it would be appropriate to do so. b) Failure to recognize or characterize different population and situations. c) Failure to risk adjust outcomes. d) Spending unnecessary money, wasting time. 2) The second issue is Potential Errors Using Algorithms which are characterized as: a) Inappropriate use of output. b) Using a version out of date or with an error. c) Using an algorithm irrelevant or unresponsive to condition. d) Using an algorithm with inappropriate complexity. e) Using an algorithm based on ambiguous data. f) Failure to properly specify the appropriate population or group. 3) The third area is Problems with Typical Algorithm Representations which include: a) Oversimplification of algorithms to enable questionnaire or checklist formats, or cumbersome forms with lots of choice points. b) Calculations are often simplified to reduce calculation time. c) Supporting documentation is often not available. d) Assumptions about patient population are often unspecified. The following are potential benefits of automation: a) Reduce data entry errors. b) Remove calculation errors. c) Facilitate recall of algorithm details. d) Reduce selection of overly simple algorithm. e) Reduce selection of wrong algorithm for population or situation. f) Guide appropriate use of output. g) Prevent use of a version out of date or with an error. This is an extremely important issue which we lack the space to describe. The goal of the Medical Algorithms Project is to provide a collection of algorithms in a format that supports clinicians, programmers, and validators. 2 , 3 It contains a collection of over 2,650 algorithms represented in a spreadsheet format with references to the original sources. MEDAL uses a standardized representation of the algorithms that was designed to support future automation. See (2) for a more complete discussion. It is clear that medical algorithms are one key format for sharing medically relevant information and that the sharing of such information is needed for safe patient care. We have described here a number of errors that can be minimized through the use of automated medical algorithms. We have also described ways in which potential introduction of errors by such automation can be minimized, primarily through rich communication of algorithm details and validation. Since the ultimate responsibility for proper use resides with the clinician, there is a great need for such a complete representation of the algorithms, enabling easy retrieval of relevant information. Reprinted from the Proceedings of the 2001 AMIA Annual Symposium, with permission.

We are concerned with the problem of determining the potential for load balancing in a distributed computing system. We define a precise measure, called the number of sharable jobs, of this potential in terms of the number of jobs that can usefully be transferred across sites in the system. Properties of this measure are derived, including a general formula for its probability distribution, independent of any particular queuing discipline. A normalized version of the number of sharable jobs, called the job sharing coefficient, is defined. From the general formula, the probability distribution of the number of sharable jobs is computed for three important queuing models and exact expressions are derived in two cases. For queuing models in which an exact expression for the probability distribution of the number of sharable jobs is difficult to obtain, two methods are presented for numerical computation of this distribution. The job sharing coefficient is plotted against traffic intensity for various values of system parameters. Both of these measures are shown to be useful analytic tools for understanding the characteristics of load sharing in distributed systems and can aid in the design of such systems.>