Issues in the development of an industrial bioprocess advisory system

FEATURES

Issues in the development of an industrial bioprocess advisory system Jarka Glassey, Gary Montague and Pankaj Mohan The background and motivation for the construction of a fault detection and advisory system for an industrial fermentation process plant are described. Here, the knowledge extracted from the operators (implemented in the form of production rules) is integrated with multivariate data-based methods for fault detection. The industrial benefits arising from this integrated system include: (1) reduced variability, (2) increased mean performance levels, (3) reduced operator-training time and (4) knowledge management in the broader organization.

onsistent and efficient bioprocess plant operation is an admirable aim for the process engineers, but many hurdles must be overcome if this is to be achieved. Typical issues that arise during the operation of traditional chemical process plants are compounded by problems related to the biological aspects of production. Consequently, a high level of variability in production is common. Operational procedures have evolved that attempt to minimize the variability, largely relying on the expertise of plant operators and engineers, rather than on automatic control systems. Feedback control systems are scarce because of the difficulties in the measurement of the actual condition of the bioprocess. Measurements that can be made require some degree of interpretation before they can be used to make plant adjustments. Furthermore, the information required can be hidden in several temporal process measurements. Thus, human expertise in pattern extraction and interpretation is exploited. ‘Human in the loop’ control systems can be effective, but by their very nature are not consistent. Operators with varying levels of capability, and the fact that process experts are not always available 24 hours a day, means that control is not always ideal. One method of improving the situation is to exploit artificial-intelligence (AI; see Glossary) procedures.

C

Expert systems in bioindustries The developments in the real-time knowledge based systems (KBS) and expert systems (ES) have enabled the application of AI techniques for the monitoring and control of bioprocesses. Several approaches have been aimed at using KBS to improve the quality of information presented to the operators1,2 and to increase the level of automatic process supervision3–5. Konstantinov et al.6 described two basic schemes for incorporating expert knowledge into bioprocess control. Direct control systems use the ES at the level of the conventional proportional integral derivative (PID) controllers. With the sophistication of the latest fermentation control systems, this is not an effective use of ES technology. A more complex, and thus more suitable, task for the ES J. Glassey ([email protected]) and G. Montague are at the Department of Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne, UK NE1 7RU. P. Mohan is at Eli Lilly Speke Operations, Speke, Liverpool, UK L24 9LN.

136

is the supervision of the process wherein the ES sends supervisory commands to the low-level controllers. At this level, the ES functions to simultaneously monitor all process information and thus gain an insight into the overall progress of the bioprocess. In addition to monitoring tasks, schedule modification in response to process occurrences is an important issue in large-scale production. Pharmaceutical companies are beginning to recognize the worth of AI techniques in general, and ES, in particular. Although several ES shells are available commercially, G2 from Gensym (Cambridge, MA, USA) is finding widespread use; the first reported industrial application was by Eli Lilly in 1992 (Ref. 2). Since then, many other pharmaceutical companies have started using this ES shell. G2 provides a very userfriendly means of coding and implementing expert knowledge, but this is not the only issue to consider. Knowledge gathering The expert knowledge must be accumulated, checked for consistency and, most importantly, stored and coded in such a way that it is maintainable. Indeed, process plants are subject to change and the knowledge base must evolve in parallel; without this, the system would soon become redundant. With regard to the knowledge elicitation task, many approaches have been suggested, most of which are based on some variant of repertory grid analysis, card sort, goal decomposition, protocol analysis, forward scenario simulation and structured interviews. These techniques have evolved from research into human thought processes and have predominantly been developed by psychologists. From a computational point of view, they are therefore geared towards collecting knowledge rather than representing it in a usable form. Most of the approaches are based on one-to-one interviews during which the knowledge engineer attempts to elicit the reasoning and the decision-making process of an expert within their domain. During forward scenario simulation, the knowledge engineer asks the expert to ascertain the conditions leading to a previously specified goal or outcome. The resulting rule set will provide advice on how to proceed, given this situation. However, the cost of interview time and the fact that the questioning process significantly skews the knowledge (i.e. the expert only

0167-7799/00/$ – see front matter © 2000 Elsevier Science Ltd. All rights reserved. PII: S0167-7799(00)01423-2

TIBTECH APRIL 2000 (Vol. 18)

FEATURES

answers the questions asked by the knowledge engineer, based on his experience within the domain) make this approach unfavourable. Repertory grid analysis and card sort provide more structure to the interview by asking the expert to sort elements within his domain according to a certain conceptual dimension or to explain the relationships between them. However, although they can both result in a map of the domain, they do not provide a method for constructing rules. Structured interviews, even though considered to be the most effective of this class of knowledge elicitation techniques, are highly time consuming and rely heavily on the experience of the knowledge engineer in rejecting certain areas of the domain that are not fully justified by the expert. During protocol analysis (‘work shadowing’), the knowledge engineer observes the expert performing their tasks and questions their actions, recording the reasoning process. The major limitations of this technique are the disruption of the expert during actual (and possibly critical) work and the inability to capture unusual situations within the domain that may be most important for the ES to handle. Recently, a new knowledge acquisition technique (KAT) has been shown to significantly reduce the knowledge elicitation time, resulting in a complete, correct and consistent knowledge base7. A fundamental part of the knowledge elicitation proceeds through a highly structured and methodical seeking of successive falsifications of the states of belief of the expert about some core belief state. The line of questioning is carried out to strictly defined limits until the expert believes there is no further condition to overturn the belief under the preceding conditions. During interviews, the knowledge base is structured in the form of exception graphs that capture the expert’s decision process. From these graphs, the production rules or production-based objects can be generated and relatively straightforwardly coded within G2 or indeed any ES shell. Rules extracted from experts do not provide an allencompassing solution to bioprocess control problems. Algorithmic methods are more suited to tackling certain forms of problem domain, such as in extremely data-rich situations encountered in large-scale bioprocess operations, where cognitive overload is a recognized problem. Thus, an efficient bioprocess control system must encompass a combination of different techniques, with algorithmic procedures complementing rule-based methods. For example, inferential measurements based on algorithmic procedures provide greater knowledge of the state of the process, to some extent overcoming measurement limitations8. Consequently, deviations from the desired behaviour can be detected early and, if possible, rectified. Here, we concentrate on fault handling, and therefore the discussion will be limited to the application of algorithmic methods in this area. A review of recent progress and outstanding challenges in using algorithmic methods for fault detection and diagnosis is presented by Kramer and Fjellheim9. Several pattern-recognition techniques have been applied to detect the faults in batch processes10–14; methods for transforming quantitative information into qualitative forms have also been developed15–17. TIBTECH APRIL 2000 (Vol. 18)

Glossary Artificial intelligence A wide-ranging term encompassing computer applications that have the ability to make decisions; the ability to explain reasoning is evidence of intelligence. Artificial intelligence also covers methods that have the ability to learn. Automatic control systems Hardware and/or software that are designed to maintain conditions at desired levels by adjusting suitable variables. For instance, an automatic control system is generally used to regulate pH by modifying alkali or acid additions to a fermenter. Cognitive overload The situation that can occur in fault circumstances when a process operator is presented with an excessive amount of information, thus making it extremely difficult to ascertain the root cause of the problem. Expert system A computer-based program that encodes rules obtained from process experts usually in the form of ‘if – then’ statements. These systems can operate online where they receive information in real-time from a process plant, in which case they are termed real-time expert systems. Fault detection The warning that a process is not operating as would be expected. There is not necessarily an indication of the cause of the problem. Fault diagnosis Once a fault is detected, the process of fault diagnosis refers to the determination of the cause. Fault diagnosis requires the use of process knowledge in some form, usually a model of the process behaviour. In this case, the procedure would be termed modelbased fault diagnosis. Feedback control systems A common method in which a measured signal is compared with a desired value (set point) and the error is used by a controller to make modifications to a process to correct the error. An example is a pH control system where pH measurements are compared with the desired value and a pH controller then adjusts the acid or alkali flow to control pH to the desired value. Feedback control systems are a subset of automatic control systems. Inferential measurement The term given to the procedure by which easily available measurements related to key process variables are used to estimate the value of the key variable that is difficult to measure online. A simple example would be measurements of CO2 concentration in off-gas, which can be used to estimate an organism’s growth rate. The relationship is usually specified by a mathematical expression. Knowledge based systems An extension of the expert system concept wherein additional forms of knowledge, such as mathematical models, are incorporated with the expert rules. As with expert systems, when online information is received the system is termed a real-time knowledge based system. Knowledge elicitation The procedure during which an expert is questioned generally in an interview situation and their knowledge is recorded. Multivariate data analysis A technique in which measurements of multiple process parameters are analysed to extract information relating to process conditions or for the purposes of prediction of future conditions. PID controllers The standard form of controller (proportional, integral and derivative, PID), which is used in a feedback control system to attempt to achieve a desired process condition.

Although these methods when applied independently have previously proved their worth, together with rule-based expert knowledge, their capabilities can be further enhanced. Two aspects of knowledge based fault-handlingsystem development that are the key to its success and are not commonly adopted in practice are: (1) the knowledge elicitation experience leading to the rulebased system; and (2) the added capabilities provided by the algorithmic methods.

137

FEATURES

Box 1. A set of simple production rules for a fictitious case of low dissolved oxygen (DO2) concentration, considered to be the cause of the problem ‘Good seed’ is true if condition ‘DO2 too low’ is true conclude ‘Good seed’ is false actions: ‘Check the DO2 probe’ end if if condition ‘DO2 too low’ is true and ‘DO2 probe faulty’ is true conclude ‘Good seed’ is true actions: ‘Switch to an alternative functional DO2 probe’ end if

Application outline A large-scale fermentation facility (operated by Eli Lilly, Speke, UK) produces several products using traditional fermentation techniques. As part of an ongoing programme for continuous process improvement, several years ago a G2 system was installed for process monitoring. The system was set up to monitor the online process measurements and to warn when deviations occur. In addition to issuing alarm messages on screen, a pager system calls the person most able to correct the problem. The system has already proved its worth: several batches have been saved following a system warning18. Most recently, a project was initiated to extend the system further by more-readily exploiting the knowledge of the engineers and scientists. The aim was to address all stages of the process with the objective of reducing process variability and increasing productivity. This would be achieved by coding the knowledge within G2 to rapidly provide assistance to the operators. It was recognized that if the project was to be successful then all personnel influenced by the system would have to ‘buy in’ to the objectives and the general means of achieving them. In a discussion with all those involved, it was decided that the project should be undertaken in stages defined by the various unit operations. The seed fermentation stage was seen as one of the most critical steps in the process. This was in keeping with the experience of others19,20, where problems with seed fermentation led to irrecoverable loss of productivity in the production vessels. For this reason, the project set out to warn the operators if conditions were arising that would lead to a poor seed. Thus, the definition of a fault is wide because it includes any situation that leads to a non-ideal seed. With seed quality identified as the issue, the question arises of how to measure it. There is not one single parameter that identifies a seed as being ‘good’. At the end of the seed stage, the decision on quality is based on the operating characteristics experienced and some combination of the process measurements, interpreted by the experts. Ultimately, if high performance is attained at the end of the production run, then a good seed is indicated. However, problems arise when a poor performance is attained because this could be related to problems in the production stage that are not a direct result of the seed quality. Thus, for KBS purposes, a good seed was left as an abstract term, judged by the expert.

138

Knowledge elicitation The quality of the final rule-base is highly dependent on the correctness of the expert knowledge and the capabilities of the elicitation method to capture this in a complete and accurate manner. An early and critical decision point is therefore the identification of a panel of experts to be interviewed. The knowledge base (KB) owner (the industrial project leader) plays a key role in finalizing the expert panel composition. In this case, 14 experts made up the panel and three knowledge engineers were available to conduct the elicitation, which was to be completed in two calendar months. Defining the key objective and its state was the first task in each of the knowledge elicitation sessions. A key question, although influenced by the domain of experience of the expert, was in all cases related to the overall objective of achieving a good-quality seed; following this, situations that overturn the belief were sought. The KAT process results in a unique exception logic, which can be illustrated in a simple form as follows: good-quality seed is normally achieved, unless condition ‘A’ occurs, in which case the expert believes that the quality of the seed will be poor. However, if condition ‘B’ also occurs, then the seed quality will be good. Associated with these conditions, actions and explanations are sought in a defined and highly structured fashion from the expert to complete the advisory system. In this simple case, the resulting rules would have a form similar to that illustrated in Box 1. In the application considered, the rule base is more complex than that described in Box 1, but the principle remains the same. Because a panel of experts with varying experience was interviewed, it was necessary to combine the individual experts’ knowledge. Inevitably, some degree of overlap exists, and in some cases, apparent conflicts arise owing to individual differences in experience. In these situations, arbitration is necessary and the KB owner is called upon to make the recommendation. The interview process was completed within the planned schedule, with the longest interview taking four man-days. The majority of the interviews lasted approximately one man-day. The exhaustive nature of the interviews limited single elicitation sessions to be no longer than half a day. In theory, there is no requirement for the knowledge engineer to be experienced in the subject domain. The best practice is to start with the most-experienced expert who has the most-extensive coverage of the domain and is cooperative in offering all possible scenarios. This acts to focus the subsequent expert interviews more efficiently. In the case of this application, the knowledge engineers had an appreciation of the basic features of the domain. This resulted in a faster, more-complete elicitation being achieved. However, there are several hidden dangers if the knowledge engineer is too experienced in the domain: (1) biasing the KB from pre-conceived opinions; (2) modifying (rather than simply recording) the knowledge; and (3) subconsciously intimidating the expert during the interview. The project specification is also a vital consideration during elicitation. The experts tended to offer knowledge on all aspects of the domain but the current application was limited to handling seed-quality issues online and in real-time. This acted to keep the interviews focused on obtaining relevant knowledge. TIBTECH APRIL 2000 (Vol. 18)

FEATURES

TIBTECH APRIL 2000 (Vol. 18)

6 ∗

Principal component 2

5

∗ ∗

4 3 2 1 0 -1 -2

∗∗ ∗ ∗

-3 -4

-3

-2 -1 0 1 2 Principal component 1

3

4

trends in Biotechnology

Figure 1 A score plot of the first two PCs for 20 pilot-stage seed cultivations. Key: , seed fermentations carried out in vessels used for production seed cultivations; ∗, seed fermentations carried out specifically for pilot plant experiments.

are identical, variations in vessel geometry and conditions clearly influence behaviour. This confirms the long-held belief of the influence that the vessel has on fermentation performance. However, this distinguishing feature is not influential when production is concerned, because the seed fermentations are carried out in the same vessel. Thus, MPCA was solely applied to the batches of seed fermentations carried out in vessels used for production seed cultivations. Figure 2 illustrates the degree of separation within this cluster. Two clusters based on the final productivity are predominantly linearly separable, apart from two low-productivity batches clustered with the ‘good’ batches. This is not unexpected because no account was taken of the final-stage condit ions and it is feasible that the seed was of good quality, but problems occurred during the final-stage cultivation. 3 Principal component 2

Algorithmic procedures Multivariate data-analysis techniques provide a means of compressing high-dimensional data onto a lower dimension to extract the key features. They have proved to be effective in many diverse applications11, with a common theme being the fact that the patterns relating to problems were hidden in a complex dataset and distributed throughout it both among many variables and over time. In such cases, univariate statistical process control (SPC) cannot always identify process problems. It is likely that this situation is found with the seed fermentations as a result of the complex interactions occurring between variables. Analysis of seed quality is problematic. The data available online is of limited accuracy owing to the inherent characteristics of the seed stage of most bioprocesses (e.g. because of low initial biomass concentrations, the off-gas measurements are not as accurate as in subsequent stages). Furthermore, several measurements, such as mycelial volume, are available only from laboratory analysis. However, the major problem is that there is no direct indication of the seed quality at the time of transfer into the production vessel. The quality of the seed is only ascertained retrospectively once final production data is available. A previous study concentrated on the application of multiway principal component analysis (MPCA)11 to pilot plant fermentations. MPCA extracts features that are present in the data and compressed information in the form of principal components (PCs) can be plotted to assist in identifying process variations. To maximize the efficiency of the feature extraction method, it is necessary to carry out the following: • Select the process variables containing the mostrelevant information; incorporating irrelevant variables reduces the precision of the technique by introducing noise. • Select the data-sampling rate to capture features most concisely. In general, it is desirable to minimize the dimension of the data in order to avoid problems with robustness. Thus, a compromise exists between reducing information content and improving robustness. • Select the combination(s) of PCs that demonstrate the features most effectively. It is not necessarily the PCs that capture the greatest variability in the process measurements that are the most descriptive of the features relating to production variability. Although it is not essential to investigate combinations of all the PCs (the lower ones tend to capture the noise), it is desirable to assess a reasonable number of combinations visually. In a practical situation, this could be extremely time consuming and automation of the selection is preferable. This can be achieved by assessing the separation of high- and low-quality batches achieved. To investigate pilot plant fermentations, data from 20 seed batches were made available and, following the selection of the process variables and their sampling rates, MPCA was applied to this data. Figure 1 shows the plot of PC1 and PC2 that demonstrates the clearest separation between the batches. The triangles represent batches grown in fermenters used for production seed cultivations; the stars represent batches carried out specifically for the pilot plant experiments and thus run in different vessels. Although the operating procedures

2 1 0 -1 -2 -3 -4 -5 -5

-4

-3

-2 -1 0 1 2 Principal component 1

3

4

trends in Biotechnology

Figure 2 A score plot of the first two PCs for pilot-stage seed cultivations of seeds cultivated specifically for pilot plant experiments. Key: 1, high final productivity; , low final productivity.

139

FEATURES

Knowledge based system Intelligent alarming Rules Process inputs

Univariate SPC

Process effects Multivariate SPC

Feedback via operator intervention/ direct feedback opportunities

Operational advice

trends in Biotechnology

Figure 3 Overview of the information flow and knowledge base that make up an operator advisory system. The potential for operator or automatic process improvement is highlighted. Abbreviation: SPC, statistical process control.

Because these results appear to offer a useful indicator of seed quality, the methodology is currently being applied to data from production-scale cultivations. Implementation in the G2 environment In the final G2-based intelligent system, rule-based advice complements information provided by databased methods (univariate and multivariate SPC; Fig. 3). In Fig. 3, three components make up the KBS, the univariate SPC indicates deviations in process variables from standard profiles, and the rules elicited from the experts incorporated the univariate SPC procedures because it was standard monitoring policy in this situation. Hence, knowledge elicited was in many cases a direct implementation of univariate SPC and was in standard rule form. Multivariate SPC (MSPC) could not be easily implemented in such a way. Rather than rules, algorithmic methods provide the operational information from the data. Methods are available to interpret the output of the MSPC and to identify likely process problems, but relating the information directly back to process-causes rather than combinations of effects can be more challenging depending on the particular fault. In some instances, this will require the use of a KB that can only be constructed after experience is gained in the performance of MSPC. Thus, in the current system, MSPC provides indications of problems, but the output might require interpretation by operators to determine corrective action. The true power of the MSPC approach will be gained when the KBS interpreting its output is constructed. This is part of an ongoing improvement programme. With regard to the rule-based information, following elicitation from individual experts, a fused KB made up of the expertise of 14 experts was constructed by the knowledge engineers and agreed by the top experts and the KB owner. The next stage was to convert the exception graphs into production rules for implementation within G2. Although a software package is available from Empiricom (Lancaster, UK) that will instantly convert the graphs directly into G2 rules or objects, in this application the rules were written manually for experience and assessment purposes. Two

140

man-weeks were required to produce a final set of 50 rules. However, several critical issues had to be addressed before these rules could be implemented online. For the operators to accept and to use the system, it is vital to display only the necessary information and in a form that is acceptable to them. It is also essential to construct the system in a way that would require minimum intervention at a later date and would enable easy expansion as the knowledge evolves; the nature of G2 allows this to be achieved with relative ease. Conclusions This project set out to implement an AI-based supervisory system making maximum use of the available information. The diverse forms of knowledge necessitated using several different approaches, all integrated into one package. With regard to knowledge elicitation, a direct comparison of the effectiveness of the KAT method with alternative techniques is almost impossible without using a different technique on each of several different experts in the same area of a given domain. After an elicitation from an expert has been completed using one technique, it is impossible to reelicit using an alternative technique without biasing the result. However, the depth of the knowledge and the speed with which it was elicited indicate that the KAT method is a particularly fast and efficient procedure. Seed data analysis using MSPC appears to provide some useful information on seed quality, which cannot be readily obtained using rule-based or univariate SPC. The use of the MSPC method, alongside the expert knowledge, has the potential to provide a powerful analysis tool for complex processes. However, one problem is justifying the capital outlay to implement a KBS. The time of experts is valuable and the costs involved in coding this information can be high; it is therefore essential to build a business case. This requires some assessment of the likely savings, and without experience of application this can be difficult. Acknowledgments The authors are indebted to Eli Lilly Speke Operations for their financial support, assistance in the construction TIBTECH APRIL 2000 (Vol. 18)

FEATURES

of the knowledge base and permission to publish the results of the work, and Gensym for their provision of software. Furthermore, the contributions of P. Duke and R. Turner of Empiricom are gratefully acknowledged. Thanks also to C. Cunha and A. Ranjan for their work in the algorithmic analysis and G2 implementation. G2 is trademark of Gensym; the KAT and KATKit are trademarks of Empiricom t/a CKD and CKDesign. The G2 program, the KAT technique and the KATKit software are proprietary and protected by UK and international copyright and patent laws, treaties and agreements. References 1 Karim, M.N. and Halme, A. (1988) Reconciliation of measurement data in fermentation using online expert system. In Proc. 4th Int. Congress on Computer Applications in Fermentation Technology – Modelling and Control of Biotechnical Processes (Fish, N. and Fox, R.I., eds), SCI/IFAC, pp. 37–46, Ellis Horwood 2 Fowler, G. et al. (1992) Development of real-time expert systems approach for the online analysis of fermentation respiration data. Proc. 2nd IFAC Symposium on Modelling and Control of Biotechnical Processes (173–178), Pergamon Press 3 Cooney, C.L. et al. (1988) An expert system for intelligent supervisory control of fermentation processes. In Proc of 8th Int. Biotech. Symp. (Durand, G. et al., eds), pp. 563–575, Societe francaise de microbiologie 4 Halme, A. and Visala, A. (1991) Combining symbolic and numerical information in modelling the state of biotechnological processes. In Proc. ECC, pp. 218–223, Hermes 5 Alford, J. et al. (1992) Development of real-time expert system applications for the online analysis of fermentation data. Proc. 9th Int. Biotech. Symposium and Exposition, 375–379 6 Konstantinov, K.B. et al. (1994) Expert systems in the control of animal cell culture processes: potentials, functions and perspectives. Cytotechnology 14, 233–246

7 Ross, D. et al. (1998) Predictive inferencing and graphical knowledge representation. Gensym User Symposium 1998 8 Lant, P.A. et al. (1993) On the applicability of adaptive bioprocess state estimators. Biotechnol. Bioeng. 42, 1311–1321 9 Kramer, M.A. and Fjellheim, R. (1996) Fault diagnosis and computer aided diagnostic advisors. In AICHE Symposium series 0065–8812 (Vol. 92, 312), pp. 12–24, American Institute of Chemical Engineers 10 Gregersen, L. and Jo/rgensen, S.B. (1999) Supervision of fed-batch ferementations. Chem. Eng. J. 75, 69–76 11 Nomikos, P. and MacGregor, J.F. (1995) Monitoring of batch processes using multi-way principal component analysis. AIChE J. 40, 1361–1375 12 Dong, D. and McAvoy, T.J. (1996) Non-linear principal component analysis based on principal curves and neural networks. Comput. Chem. Eng. 65–78 13 Glassey, J. et al. (1994) Enhanced supervision of recombinant E. coli fermentations via artificial neural networks. Process Biochem. 29, 387–398 14 Bakshi, B.R. et al. (1994) Analysis of operating data for evaluation, diagnosis and control of batch operations. J. Process Cont. 4, 179–194 15 Konstantinov, K.B. and Yoshida, T. (1992) Real-time qualitative analysis of temporal shapes of (bio)process variables. AIChE J. 38, 1703–1715 16 Cheung, J.T.Y. and Stephanopoulos, G. (1990) Representation of process trends – Part I. A formal representation framework. Comp. Chem. Engng. 495–510 17 Whiteley, J.R. and Davis, J.F. (1993) Qualitative interpretation of sensor patterns, IEEE Expert, 54–63 18 Alford, J. et al. (1999) Real rewards from artificial intelligence. InTech, ISA, 52–55 19 Calam, C.T. (1976) Starting investigational and production cultures. Proc. Biochem. 11, 7–12 20 Warr, S.R.C. et al. (1996) Seed stage development for improved fermentation performance: increased milbemycin production by Streptomyces hygroscopicus. J. Ind. Microbiol. 16, 295–300

Safe biotechnology 10: DNA content of biotechnological process waste The Safety in Biotechnology Working Party of the European Federation of Biotechnology The adequacy of the existing treatment, disposal and recycling processes of waste streams from biotechnological laboratories and industrial processes, especially those using genetically modified microorganisms, have been repeatedly discussed. Here, we focus on the discussions linked to the DNA content of these wastes, the properties of extracellular (or ‘naked’) DNA and the ability to transfer genetic information between bacteria (e.g. antibiotic resistances) or into higher organisms.

he precision molecular-genetic techniques, sometimes known as ‘modern biotechnology’, involve the direct modification of the DNA (or RNA) molecules, which carry the genetic material of an

T

O. Doblhoff-Dier (Institute for Applied Microbiology, University for Agricultural Sciences, Muthgasse 18, A-1190 Vienna, Austria; e-mail: [email protected]), H. Bachmayer, A. Bennett, G. Brunius, M. Cantley, C. Collins, J-M. Collard, P. Crooy, A. Elmqvist, C. Frontali-Botti, H.G. Gassen, R. Havenaar, H. Haymerle, D. Lamy, M. Lex, J.L. Mahler, L. Martinez, C. Mosgaard, L. Olsen, J. Pazlarova, F. Rudan, M. Sarvas, H. Stepankova, G. Tzotzos, K. Wagner and R. Werner. The authors are members of the Safety in Biotechnology Working Party or have been coopted as experts by the Working Party. TIBTECH APRIL 2000 (Vol. 18)

organism, resulting in a genetically modified organism (GMO). Approximately 25 years of safe practice have not revealed any special risks associated with the largescale commercial preparation of products derived from modern biotechnology or the disposal of resulting wastes or byproducts. However, concerns about risks and uncertainties, and public or political perceptions about these so far conjectural risks, have given the safety and regulation of GMOs high prominence in recent years. The series on ‘Safe biotechnology’ published by the Working Party ‘Safety in Biotechnology’ of the European Federation of Biotechnology has tried to provide

0167-7799/00/$ – see front matter © 2000 Elsevier Science Ltd. All rights reserved. PII: S0167-7799(00)01424-4

141