Development of A Sliding Window Protocol for Data Synchronization in a Flow Cytometer

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1 Development of A Sliding Window Protocol for Data Synchronization in a Flow Cytometer Junhua Ding 1, 2 1) Dept. of Computer Science East Carolina University Greenville, NC dingj@ecu.edu Yuxiang Shao 1, 2 2) School of Computer Sciences China University of Geosciences Wuhan, Hubei, China shaocx25@163.com Dongmei Zhang 2 2) School of Computer Sciences China University of Geosciences Wuhan, Hubei, China jjielee@163.com Abstract Flow cytometers are instruments for analyzing blood cells individually and have been widely used for clinical diagnostics and research discovery. A flow cytometer is able to acquire signals of several independent physical or chemical parameters of each cell altogether. Therefore, synchronization of data acquisition among all parameters of each cell is required for a reliable flow cytometer. Most advanced flow cytometers use complex electronic design to ensure the data synchronization in the beginning of signal acquisition, but signals could still be corrupted during transmission or storing. In this paper, we present a software solution based on a dynamically sliding window protocol for data synchronization in a flow cytometer. Our solution is used for either a supplementary or a replacement of the hardware solution. The overhead due to the computing of sliding window is minimal. The design, implementation and testing of the solution is described in this paper, which shall be useful for others who are developing similar systems. Keywords- sliding window protocol; flow cytometer; data acquisition; software testing I. INTRODUCTION Flow cytometer is a high performance medical instrument for analyzing blood cells individually. Since it was invented in 1960s, it has been the premier instrument for analyzing single cells, and it is able to analyze blood cells from several hundred to 30 thousands every second [7]. Most flow cytometers detect blood cells with optical parameters coming from interaction between cells and laser beams. The optical parameters include forward scatters, side scatters, and different fluorescence signals. Flow cytometers have been widely used for clinical diagnostics and research discovery for over than 40 years. Because flow cytometers are able to analyze cells one by one with multiple parameters, they are effective and efficient for analyzing many immune related diseases such as HIV-infection and leukemia. For example, flow cytometers have been widely used for CD3+, CD4+ and CD8+ screening [6]. They are also used for apoptosis analysis and pharmaceutical discovery. The marketing value of flow cytometers including instruments, reagents, software and services in the world was over than $2.6 billion in year 2010, and it will grow over than 10% annually in the next decade. However, flow cytometers are expensive, and the price of a cheap instrument with basic features could be over than $50,000, and the service and reagents cost for each instrument could cost another $30,000 easily each year. The price of an advanced flow cytometer system including a sample preparation station could be over than $500,000. HIV-infection now is a serious problem in many developing countries, and flow cytometers are the most effective and highly efficient device to detect the infection in the early phase. Unfortunately, flow cytometers are too expensive for users in the developing countries who are suffering of HIV-infection. Building an affordable flow cytometer is important to improve the life quality in developing countries. In this paper, we describe a flow cytometer we are building with affordable and comparable performance in mind. We particularly discuss the sliding window protocol that we designed for the data synchronization in the system. A flow cytometer detects several parameters such as forward scatter and fluorescence for each cell to discover different information in the cell. Each parameter is detected by an independent signal detector like photomultiplier tube (PMT). The signals of the multiple parameters for a cell have to be acquired together. Missing a signal of any parameter will mess up the data of other cells and then the analysis result of the sample will be incorrect. The worst situation is that a user may unable to detect the error. Most existing flow cytometers have a complex electronic design for synchronizing the multiple parameters so that signals of all parameters have to be acquired together or none. Some inexpensive flow cytometers do not have any synchronization mechanism to ensure the data synchronization, which may produce potential error results. In this paper, we present a sliding window protocol for synchronizing the data acquisition of multiple parameters in a flow cytometer. The protocol can be used as a supplementary of the hardware solution or an addition to a flow cytometer that does not implement data synchronization. The overhead of the calculation of the synchronization in the software is ignorable. Our experiments have shown that our design of the sliding window protocol for data synchronization is both effective and efficient. The main contribution of this paper is due to the design and analysis of the dynamically sliding window protocol for the data synchronization in a flow cytometers. We introduce the background of flow cytometers and the issue regarding the data synchronization among multiple parameters. We describe the detail of design and implementation of the sliding window protocol, and we also validate the effectiveness and efficiency of the design and implementation using different techniques such as testing, simulation and model based testing. The design, implementation and validation of the sliding window protocol for the data synchronization could be useful to others who are building similar systems. 626

2 The rest of this paper is organized as follows: Section 2 presents a brief introduction of flow cytometers and data acquisition in flow cytometers. Section 3 describes the design, implementation and validation of the dynamically sliding window protocol. Section 4 is the related work, and Section 5 concludes this paper. II. BACKGROUND In this section, we give a general description of flow cytometers. recognizing specific antigens in the surface of cells. In a flow cytometer, fluorescence with each different wavelength is filtered with an optical lens and mirrored to a fluorescence detector, which normally is PMT. Figure 2 shows a typical configuration of a flow cytometer, where structure of the optical and signal detection components are shown in detail. An embedded software system is used to automate and control the instrument. Acquired signals in the instrument are sent to the application software system of the flow cytometer via network, and users manage the cell analysis and the instrument via the application software. Figure 1. A schematic of a flow cell [10] A. Flow Cytometers Although the origins of flow cytometers can be tracked to 19 th century, the development of flow cytometers became feasible until late of 1970s when reliable lasers, commercially produced monoclonal antibodies and inexpensive computer became available [7]. Cytometry is about the measurement of physical and chemical as well as biological characteristics of cells, and flow cytometry is a process for measuring individual cells when they pass through detectors in a fluid steam [9]. Flow cytometer is an instrument of flow cytometry for measuring size, internal complexity, and fluorescence of cells based on measurement of electronic and optical parameters obtained as a result of the interrogation of flowing cells and lights [9]. A typical flow cytometer includes several major components: (1) a sample handling component for taking blood from sample tubes or wells; (2) a flow cell for building the blood sample into a fluid stream where the cells pass the light sources, (3) an optical component consisting of one or more laser light sources, (4) signal detection component for acquiring light scatter signals and fluorescence signals, and (5) a software system for instrument management and data analysis. A schematic representation of a flow cell for a typical flow cytometer is shown in Figure 1. The cell sample is sent to a tiny tube and then injected into the stream of sheath, which ensures the cell sample remain in the center according to laminar flow principle [8]. The cells in the sample interrogate with laser lights when they pass through them individually. Corresponding signal detectors acquire the scatted lights and fluorescence signals when a cell passes through a light source. Forward scatted lights are proportional to the surface area and size of the cell, and side scatted lights are proportional to the granularity and internal complexity of a cell. When a cell is prepared with monoclonal antibody and the antigen of the cell binds to the antibody, the cell will activate fluorescence in particular wavelength when it interacts with a laser light. Fluorescence signals are used for Figure 2. A schematic of a flow cytometer [10] B. Data Acquisition in Flow Cytometers In this section, we describe the data acquisition and synchronization in flow cytometers. Figure 3. An illustration of data display and analysis When a cell passes through the flow cell and interrogates with a laser beam, it generates signals like forward scatter signals or fluorescence signals. Each signal detector acquires the signals of a particular parameter. We build the set of signals of all parameters for each cell as a record, and each item in the record is a signal value of a parameter. If the value of an item is 0, then we know that the corresponding detector of the parameter did not get the signal of the cell. The 0 value of a parameter maybe caused by weak signal or problem of the detector, and it may occur many times. The system processes each record to exclude noise signals such as those coming from debris or other irregular particles. Each processed record is put into a buffer, and then the records are packaged and delivered to the workstation for data analysis and additional processing. The data analysis software stores the data records into a file or database in a standard format such as FCS 3.0 so that the data can be exchanged among different systems. At the same time, the data are displayed in different graphic formats such as two dimension dot plots or density plots or 627

3 one dimension histograms. Users are able to display the plots with different combinations of the parameters in the 2 dimension plots or histograms for different parameters. The classification of the cell population in the sample can be observed in the plots in some cases. In many cases, users need to select partial of the cell population or divide the cell population into several regions in the plots for further processing, and the procedure is called gating [9]. Statistics such as mean value, cell count, standard derivation or percentage of a cell type in a sample are calculated based on gating. Figure 3 shows a sample of data display and analysis plots in a flow cytometer. The left plot in Figure 3 is a histogram of a parameter, and the right plot is a dot plot of side scatter v.s. forward scatter, and it is gated with R1. Table 1: Sample data collected for each parameter FS SS FL1 FL2 FL3 cell cell2 x cell3 3 3 x 3 x cell4 4 x cell Table 2: Sample records built for each cell in Table 1 FS SS FL1 FL2 FL3 cell cell cell cell cell5 5 Although cells in a flow cytometer pass detectors individually, data analysis is still performed on a collection of cells like 1000 cells or 5000 cells. Therefore, the signals records are continuously acquired and stored when cells are passing in the fluid stream. Ideally, when a cell passes through a light beam, each detector detects one and only one signal of a particular parameter and all detectors get their signals altogether at the exact same moment and then reset themselves for next acquisition. However, one detector may not detect a passing cell, and other detectors do. In this case, the next signal acquired by the detector that missed the previous one will be accidently put into the previous position that is supposed to be occupied by the missing value of last cell. The problem will only be found at the end of the sample analysis due to some missing values of the last several cells. Then the analysis data of the whole sample have to be discarded. In the worst situation, each detector missed the same number of signals, and then the problem even will not be found easily. Let s explain the problem in details with examples. We assume the instrument collects 5-parameters signals for each cell. The 5 parameters are forward scatter FS, side scatter SS, fluorescence 1 FL1, fluorescence 2 FL2, and fluorescence 3 FL3. Each parameter has a signal detector for, and each detector collects data independently when a cell interrogates a laser beam. Table 1 shows the acquired signals of 5 cells, where x represents a lost signal, and an integer number means a valid value that is collected. Then the program is going to build a record for each cell, and each record has 5 parameter values representing the analysis result. Since no one is aware of the lost values due to the high speed of data generation (most existing flow cytometers process around cells each second), the positions of the lost signals are filled by signals of coming cells. The 5 records built based on data collected in Table 1 is shown in Table 2, where we found the records of cell2, cell3, cell4 and cell5 are all wrong. Therefore, the analysis result of the whole sample has to be discarded. Some hardware design can solve the data synchronization problem mentioned above. For example, one detector (it is the detector of FS in most cases since FS has the strongest signals) is designed as the master detector and it facilitates the synchronization among all detectors. When the master detector detects its signal, the corresponding position of each parameter in the record is filled with the signal values the detectors detected or a default value 0. If the master detector was not trigged by a passing cell, then none of other detectors will be trigged either. However, signals still could be lost during the data transmission or storing. For enhancing the quality of the instrument we developed, we designed a dynamically sliding window for implementing the data synchronization. It could be particularly useful for instruments that do not have a hardware based data synchronization design. III. SLIDING WINDOW PROTOCOL In this section, we discuss the design, implementation and validation of the dynamically sliding window for data synchronization in a flow cytometer. A. Design When a cell interrogates a light beam, it may or may not trigger the signal detectors. If one detector loses a signal, then the data acquired for a sample are messed up. In some cases, the messed up of acquired data may even not be found at the end of the analysis. In order to solving the problem, we designed a dynamically sliding window for checking the data integrity at real time. We assume the instrument acquires values of 5-parameters signals of one light source for each cell. If the instrument has two laser light sources, then there are 10 signals total when a cell interacts with 2 laser lights each time. We discuss the instrument that has only one laser light, and the design can be easily extended for systems that have multiple laser lights. The 5 parameters we defined as FS, SS, FL1, FL2, and FL3. The missing of a signal could happen at any time. Therefore, the size of the sliding window is 1 in the beginning, and it increases if no missing of signals occurs next time, and it continuously increases the size until it reaches to a preset limitation as soon as no signal is lost. If a signal is lost, then the size of the sliding window is reduced until it reaches to 1 if losing of signals is continuously found. When the size of the window is larger than 1, and the numbers of missing signals for each parameter is same, then unfortunately our design is unable to detect the problem. However, the possibility of the case in a small size of window is extremely rare; we don t consider the 628

4 solution of the case. Here is the design of the dynamically sliding window: 1. As soon as the data acquisition of a sample starts, the size of the slide window is set to 1. A data buffer that has 5 fields is dedicated for storing data collected from each detector, and each detector is assigned with a field. Figure 4 shows how each detector stores its signal values into the fields in the buffer, where the arrow line means the direction of data pumping, and the dash line represents data stored in the buffer. Buffer F1 F2 F3 F4 F5 Storing Detectors FS SS FL1 FL2 FL3 Figure 4 An illustration of data storing in a buffer 2. According to the speed setting of the fluid system in the flow cytometer and the sample type, we are able to calculate the minimal time interval of 2 cells passing through a laser beam. We set the time interval s as the cut off time for checking the sliding window. Since each detector gets its signal in near to light speed, we consider all detectors get their signals at the exact same moment. 3. When the first signal of FS is received and stored in the buffer, the program starts clock t. As soon as t = s, the program checks the buffer, if each field has one value, the analysis result is complete; otherwise, the fields that don t have signals are filled with value 0. This step is to ensure that no value is lost in the first line. Buffer F1 F2 F3 F4 F5 Sliding Window Detectors FS SS FL1 FL2 FL3 Figure 5 An illustration of sliding windows 4. The program increases the size of the sliding window to 10 so that the buffer will accept additional 10 cells. As soon as the program finds that the buffer already stored additional 10 values of FS, it immediately checks all other fields. During the time, if new signals are coming, they are stored in a temporary buffer that has the same data structure as current one. Normally it will not happen since it takes much less time for checking the synchronization in a window than the time interval s of two cells passing through the light. 5. The program counts the number of signals for each field in the buffer. If an inconsistency of number among fields is found, it means some detectors missed signals for some cells. Since FS is the master parameter, the number of signals in other fields could be over than the size of the window if FS lost some values. 6. If the program finds a missing signal in the window, the analysis results of all cells in the window have to be discarded because the program does not know which cell that really missed a signal. The size of the window is reduced to half of the current window until the size is reduced to 1 if missing of values is keeping occurring. 7. If every field has the same number of signal values within the window, the size of the window is doubled until it reaches to 100, which is the maximal size of the window, as soon as no missing signal is found. A flow cytometer normally analyzes around cells for each sample; therefore, discarding 100 cells of cells is still acceptable. The users can decide whether to continue acquire data from the sample until it reaches to a preset criterion. The idea of the dynamically sliding window is shown in Figure 5, where the rectangles represent sliding windows. 8. If there are some items in the temporary buffer at the end of analysis, the program just simply counts the number of signals in each field. If the number is same for every field, the data is appended to the data in the original buffer. If inconsistency is found, the data in the temporary buffer are discarded. B. Implementation The main embedded program of the flow cytometer we developed has only one process that is implemented in an ARM single board computer, and the data acquisition program is implemented in a FPGA board. The sliding window program is implemented in the FPGA board with data acquisition programs. The program sets a buffer according to the number of cells to be processed in a sample. For example, if a user needs to analyze cells from a sample, the buffer size is 200KB considering each value of a parameter is a 32 bits number. The real buffer is 400KB since each detector actually acquires two different signal values for each cell. We don t discuss the case since the sliding window only needs one signal for each parameter. Each acquired datum is processed for multiplying or digitalizing by the detector, and then the datum is stored in a particular position of the data buffer. The sliding window program monitors the number of FS data, stores and records the position of the current processing. As soon as the number of FS data in the window equals to the size of the window, which starts from the next position ws of the last processed position in the buffer to position we, which is ws pluses the current size of window sw (we shall be the last item in the current buffer). When the program counts the number of items in each field in the window, it counts all items from position ws to the last item we of the field in the buffer. During the counting period, incoming data are stored in a temporary buffer. If a missing value is found, the data from position ws to the last one in the buffer are deleted, and the size of the window is reduced to half of sw or 1 if sw is 10. If data in the window are complete, then ws is set to the next position of we, and the size of the window is set to the smaller one between sw and

5 C. Validation Although implementing the dynamically sliding window is easy, testing the program is challenged due to several reasons: (1). Missing of values from each signal detector is very rare so that it is very difficult to reproduce missing of values; (2). The data generation from each detector is very fast, usually one signal for each parameter every 0.05 millisecond or less; (3). The data are produced at real time, and testing has to be performed at real time; (4). The time period between two subsequent values of a parameter could be less than 0.05 millisecond, and it is difficult to control the time period during the testing; and (5). The number of data items for a sample could be or more, which makes the verification of the testing result fairly difficult. In order to test the implementation of the sliding window thoroughly, we test the program with three different ways [11]: (1). System testing; (2) Special cases based testing; and (3) Model based testing. System testing. If some signal values of a parameter are lost in the early phase of the data acquisition, the analysis result of the sample will be messed up (see the explanation in section 2.B). We can prepare two exact same blood samples such as some standard quality control kits [7]. Then we run one sample in a reference flow cytometer that has been tested and calibrated, and run another sample in the flow cytometer that has the sliding window under test. We compare the analysis results such as patterns in the dot plots, histograms and statistics from two tests like those shown in Figure 6. If a significant difference of the two results is found, then something must be wrong in our program or design. However, it is not necessary that the messed-up data due to missing of values during the data acquisition will cause a significant difference of two analysis results. In many cases, the difference between two analysis results might be very difficult to be observed. Considering the fact that missing values in the data acquisition is fairly rare, system testing does not work well for testing the sliding window program. Special cases based testing. It is impossible for us to interrupt some detectors to disable the data acquisition of some cells at real time due to the high speed of data acquisition. In addition, the rate of dropping signals is so low that no any signal may be lost even running the analysis for several days without interruption. It is difficult to change the firmware in the instrument for dropping signals. However, we can work around the problem via changing the setting of the instrument and testing. For example, we may set a very large buffer such as 1MB for storing the acquired data, and dilute the blood sample to such as 20% of the original one. Then it will take about 25 times of time to acquire enough data to fill the space in the buffer. We also set the size limitation of the sliding window to 1000 instead of 100. Based on above configuration, we have enough time to turn off one detector and then turn on it immediately during data acquisition. Then we can check whether the final result has discarded the cells that have incomplete data, and number of items for each field is same. If we turn off a detector before the data acquisition starts, we can check whether the missing value in the field of the first cell has been filled with 0 as we designed. The speed of fluid stream can be adjusted in some instruments, and then we can also increase the time interval between two passing cells via reducing the speed of the fluid stream. However, it is still difficult to test a situation that a detector only drops one or two cells since many cells have passed the light during we turn on and off the detector even with above modifications. In addition, it is almost impossible to test the case that signals are dropped from two or more detectors randomly. Model based testing. In order to thoroughly testing the sliding window protocol, we developed a program to simulate the data acquisition and the sliding window. The simulator generates a signal for each parameter and pumps it into the buffer every 0.05 millisecond or less, and randomly drops a datum in the buffer for any parameter. The simulator is able to simulate many different cases such as dropping a signal in the beginning or at the end of data acquisition, or dropping several signals of multiple parameters together. We run and test the simulation program in a PC environment. We shall test many different cases via combining different scenarios based on combinatorial testing technique [5]. The scenarios could include ones like missing data items in the beginning, missing just one data item for a parameter, missing several data items altogether for multiple parameters, missing 2 or more data items altogether for a parameter, and the time interval of generating signals of two cells could be set as 0.05 millisecond, 0.03 millisecond, 0.01 millisecond, and others between the range. However, it is difficult to know when the program is adequately tested. In this research, we used model-based testing technique to generate adequate tests. The tool we used in this project is called MISTA [12], which is a model-based testing tool for automated generation of executable test code in model level and program level. It uses function nets (a type of PrT nets extended with inhibitor arcs and reset arcs) [12] for specifying test models so that complete tests can be automatically generated. The detail of MISTA can be found in [12]. Figure 6 is a function net model of the sliding window protocol in MISTA. In the model, place Signals and transition Generator are used for generating signals, and transitions SendSS, SendFS, SendFL1, SendFL2, and SendFL3 are used for sending corresponding signals to the buffer of each parameter. Transitions Drop1, Drop2, Drop3, Drop4, and Drop5 are used for simulating randomly dropping signals during transmission. When a signal is dropped, and the size of the sliding window is reached, transition DoReset is used for discarding signals in buffers within the sliding window. If no signal is lost within current sliding window, then transition Sent delivers all signals to place Success. The size of the sliding window is set by the guard conditions in transitions DoReset and Sent. After compiled and inspected executions of the model, we generated tests according to different adequate criteria including reachability tree, state overages and transition coverage. Through mapping the function net model to the Java implementation of the simulator with sliding window, MISTA automatically generated tests for testing the implementation based on the tests generated from the model. Due to the limitation of the space, we don t discuss the details of the tests in this paper. 630

6 V. SUMMARY AND FUTURE WORK Data acquisition and analysis is the most important part in a flow cytometer system [6]. In order to facilitate data synchronization among multiple parameters, hardware design has been used in many flow cytometers. We designed a sliding window protocol to ensure the data synchronization. The purpose of our sliding window is used for detecting missing of data during data acquisition. One of the difficulties comes from the high speed of data generation and the independence of data acquisition of each detector, and another difficulty comes from the requirements for rigorous techniques to ensure high quality of the system design and implementation. We address both of the difficulties in this paper. Our validation results show that our solution was able to be used for either a supplementary or a replacement of the hardware solution. The overhead due to the computing of sliding window is minimal. The design, implementation and validation of our solution described in this paper shall be useful for others who are developing similar systems. Figure 6 A function net model of the sliding window protocol ACKNOWLEDGMENTS The result of the model based testing has shown effectiveness of the design and implementation of the sliding window protocol. IV. We thank Dr. Xinhua Hu at East Carolina University and Eric Statler from Beckman Coulter for numerous discussions. This research is supported in part by award #CNS from the National Science Foundation. Junhua Ding s research was also partially supported by the guest professorship grant from school of computer sciences at China University of Geosciences. RELATED WORK There are many tutorials on flow cytometers, and the one [9] by Shapiro might be the most referred one. The quality of flow cytometers software is critically important to the quality of the instrument. The software normally is designed to enhance features and ensure the quality of the instrument. Therefore, it is important to develop a rigorous approach to ensure the software quality. In this paper, we designed a sliding window protocol for data synchronization in flow cytometers, and analyzed the design using different approaches to ensure the correctness. Sliding window protocols have been proposed for reliably transforming data packets in-order [2], but the sliding window protocol proposed in this paper is relatively simple since the delivering of signals in-order is not an issue. Formal modeling of software systems provides a solid foundation for formally analyzing software systems. Although formal analysis provides a rigorous way for verifying correctness of software systems, it is infeasible for analyzing large software systems due to the state explosion issue. Software testing is still the most widely adopted technique to check the quality of software, but it lacks rigorousness in many cases. Many researches have been done on combination of formal analysis and testing such as the one discussed in [3] and [4]. The analysis approach used in this paper combines both advantages of software testing and formal analysis. Tests were generated based on formal analysis results and testing bridges the gap between simulation and formal analysis. More important, tests generated from the model not only were used for testing the model, but also were used for generating code level tests so that the consistency between a model and its implementation is well ensured [12]. REFERENCES [1] P. K Chattopadhyay, M. Roederer, Cytometry: today's technology and tomorrow's horizons, Methods, Vol. 57, pp , July [2] D. E. Comer, Internetworking with TCP/IP, Volume 1: Principles, Protocols, and Architecture, Prentice Hall, [3] J. Ding, X. He, Formal Specification and Analysis of an Agent-Based Medical Image Processing System, Intl. Journal of Soft. Eng. and Knowledge Eng., Vol. 20, No. 3, pp. 1 35, [4] X. He, H. Yu, T. Shi, J. Ding, and Y. Deng, Formally Specifying and Analyzing Software Architectural Specifications Using SAM. Journal of Systems and Software, vol.71, no.1-2, pp.11-29, [5] R. Kuhn, R. Kacker, Y. Lei, J. Hunter, Combinatorial Software Testing, IEEE Computer, vol. 42, no. 8, August [6] E. Lugli, M. Roederer, and A. Cossarizza, Data Analysis in Flow Cytometry: The Future Just Started, Cytometry A. 77(7): , July [7] M. G. Macey (Ed.), Flow Cytometry, Principles and Applications, Humana Press, 2007.Transfus [8] F. F. Mandy, M. Bergeron, T. Minkus, Principles of Flow Cytometry, Transfus. Sci, Vol. 16, No. 4, pp , [9] H. M. Shapiro, Practical Flow Cytometry, 4st edn. Wiley-Liss, [10] last accessed on March 16, [11] S. Vance, Quality Code: Software Testing Principles, Practices, and Patterns, Addison-Wesley Professional, [12] D. Xu, A Tool for Automated Test Code Generation from High-Level Petri Nets. 32nd Int. Conf. on Apps. and Theory of Petri Nets, Newcastle, UK, June 20-24,