VIEWPOINT ▼
ENGINEERING
64 A ■ ENVIRONMENTAL SCIENCE & TECHNOLOGY / FEBRUARY 1, 2003
© 2003 American Chemical Society
Are we standing on the threshold of a renaissance in designing biological systems?
BIOLOGY T H O M A S P. C U R T I S IAN M. HEAD UNIVERSITY OF NEWCASTLE-UPON-TYNE, UNITED KINGDOM D AV I D W. G R A H A M
ALL IMAGES: IAN HEAD, THOMAS CURTIS, AND RUSSELL DAVENPORT/ UNIVERSITY OF NEWCASTLE-UPON-TYNE
UNIVERSITY OF KANSAS
or some engineers and scientists, failure can be fast and brutal. When the Tay Bridge collapsed near Dundee, Scotland, 90 souls were lost, and the professional reputation of Sir Thomas Bouch was ruined. Bouch, who had designed the bridge, died shortly afterward, with his many successes forgotten and his one failure immortalized in a truly awful poem. That was in 1880. However, had the bridge failed 100 years earlier, the failure might have been considered understandable or even acceptable. The failure of the Tay Bridge was intolerable because of Sir Isaac Newton, who had shown that the physical world could be described and predicted by understanding the laws of physics. Structural engineers embraced this new philosophy and applied it to their designs, making breathtaking new structures and confident before the ground was broken that new structures would stand firm. The story shows that structural engineering was liberated from purely empirical design and incremental improvement by the application of theory. The spirit of this new age in engineering is captured by Tredgold’s 1828 definition of engineering as “the art of directing the great sources of power in Nature for
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©2003 American Chemical Society
the use and convenience of man,” being that practical application of the most important principles of natural philosophy (1). Tredgold wrote these words just as the combination of urbanization and the first outbreaks of Asiatic cholera led to improvements in sanitation and the emergence of the new disciplines of sanitary engineering and environmental chemistry. The resulting provision of cleaner water and improved treatment of wastes, through applied science, is one of our greatest engineering successes. Sustaining and extending this achievement is one of our greatest challenges for the future. Unfortunately, until quite recently, the science behind most water and wastewater treatment systems, especially in biological processes, was almost wholly empirical rather than theoretical. Consequently, there is still a major “craft” element to engineering biological systems. There is no equivalent comprehensive theoretical foundation, analogous to Newtonian mechanics, to guide design. Fortunately, the 19th- and 20th-century revolution in fundamental biology made a more theoretical understanding of biological system behavior possible. Darwin, Watson, Crick, Pauling, and Woese all made major contributions. MacArthur FEBRUARY 1, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY ■ 65 A
and May also provided crucial insights to engineering practitioners. We suggest that these individuals may have laid a foundation for a new era in sanitary—now environmental—engineering by providing new techniques and theories that can be employed in biological process design. Darwin brought a new worldview; Watson, Crick, and Pauling showed how the essence of this worldview lay in the genetic code and its expression in proteins; and Woese brought earlier work together by using differences in genetic sequences to describe evolutionary relationships among all living things. These efforts, in turn, laid the foundation for many new tools for studying microbial ecology, which have generated much excitement and many new insights in engineering biology. Despite the many technical advances, however, it would not be unfair for a sardonic engineer to observe that these new “toys” have merely changed the names of the bacteria without changing the “engineering”. This is why MacArthur and May are so important.
Giants of ecology Robert MacArthur was a theoretical ecologist whose most famous work was on the theory of island biogeography (2). More than any other individual, he placed postwar ecology on a more sound theoretical footing. Tragically, MacArthur died relatively young. However, he inspired a generation of ecologists, including Jared Diamond and Robert May, who have gone on to become household names in applied biology and ecology. The work of May is particularly pertinent to engineered biological systems because he studied biological communities as nonlinear dynamic systems and made predictions about community stability and instability. In fact, we believe that MacArthur and May may have actually provided a “new” theoretical foundation for the design of engineered biological systems, analogous to Newton’s legacy in bridge design. As an interesting aside, both MacArthur and May explicitly considered microbial systems in their theoretical work. Thus, although their work has not been widely applied to microbial systems, it undoubtedly applies.
There is nothing as practical as a good theory Sometimes practical scientists and engineers use the expression “theoretical” to mean impractical. However, theory is vital to engineering because it allows us to transcend experience and move beyond trial-and-error when developing new systems. Indeed, there is nothing so practical as a good theory! Theory is also important because it can simplify matters and one can “substitute one theory for many facts” (2). This is especially important if we are to make sense of the growing literature on the molecular and functional ecology of biological systems. 66 A ■ ENVIRONMENTAL SCIENCE & TECHNOLOGY / FEBRUARY 1, 2003
No theory is perfect; however, even crude or simplistic theories can be very useful if they carry within them the kernel of truth required for a particular application. For example, the last major theoretical advances in wastewater treatment engineering were made in the 1960s by Downing et al. (3) and then Lawrence and McCarty (4). Both approaches treated the process biomass as if it were one independent population (although it really is a complex community of interacting populations) and then derived usable equations based on enzyme biochemistry and microbial kinetics that permit the prediction of key process variables. Lawrence and McCarty made the additional conceptual advance by using the same approach to describe two very different biological systems—aerobic and anaerobic. This is an interesting and important point. A bacterium should follow the same basic rules of behavior irrespective of location—it cannot know if it is in a bog or a butter dish. That is, a bacterium merely senses its environment and responds appropriately. Thus, in principle, some form of universal theory that predicts organisms’ responses and behavior in their environment is conceivable. Many theories exist in ecology that might help us to better understand engineered biological processes, ranging from deterministic to stochastic approaches. Although different theories have different relative utilities, only three will be considered in this introductory discussion: Resource–ratio theory (RRT), which shows how and why resource supply conditions influence community composition and size; nonlinear dynamics, which describes how the nonlinear nature of biological growth affects process stability and instability; and island biogeography, which addresses how and why treatment communities assemble and develop.
Manipulating resource conditions RRT was formulated by David Tilman to examine the relationship between resource competition and community composition in natural biological communities (5). The theory was originally developed to describe relationships between resource conditions, such as light or essential nutrients, and plant community composition and diversity. However, RRT has subsequently been used to describe many different ecosystems (6), including hydrocarbon degradation in slurry reactors (7). It is useful for understanding biological process design because it is quantitative, can be calibrated, and deals explicitly with competition in multiple resource systems. Thus, it provides a broad framework for predicting resource supply conditions, which might be controllable in engineered processes, that might selectively promote the success (or failure) of desired (or undesired) populations (or activities) in a system. In principle, RRT can be used to manipulate the steady-state biomass, composition, and activity of a community by altering growth-limiting resource supply rates and ratios in a process. How this is possible is best described graphically.
Nonlinear growth dynamics and process stability? Predictions from nonlinear dynamics and chaos theory have been used to study many complex systems, including weather, fluid turbulence, shape, nonlinear biological oscillators, and aggregation and floc properties (11, 12). There is, therefore, a mathematical foundation for describing complex resource–community interactions, which likely dominate engineered biological process behavior. The key to incorporating the “nonlinear dynamic” worldview into practical design, however, is to translate the existing theories into usable forms that relate to the associated processes. A key el-
FIGURE 1
The effect of resource supply levels and ratios on the selection and activity of populations in a two-population system This graph shows crossing zero net growth isoclines (ZNGIs; i.e., M-M and N-N) for two hyphothetical populations—M, which has innately high transformation rates; and N, which has innately low transformation rates—growing on limiting resources, Resource 1 and Resource 2. ZNGIs indicate resource combinations where per capita growth rate equals per capita loss rate for each population. The white area is the no-growth region for M or N, the blue area marks the region of M dominance, the yellow area is N dominance, and the green area shows M and N coexistence. The red dashed line A-A demonstrates how population composition changes as a function of changes in relative resource ratios (5, 6). M
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Figure 1 shows how resource supply levels can alter population dominance in a simple microbial community (8). The figure shows how two populations, M and N, are selectively enriched under different conditions as a result of differing affinities for two resources (“affinity” is defined by Monod equations for each population and each resource). Lines M-M and N-N are zero net growth isoclines (ZNGI) for the two populations. These lines define where each population will and will not grow based on the resource use kinetics and population death rates. Furthermore, moving along line A-A shows how community and population dominance might change with differing resource supply rates and ratios. For example, at low-to-moderate Resource 2:Resource 1 ratios, N would dominate, whereas at moderate-to-high ratios, M would prevail. If such resource supply–community composition relationships could be developed for engineering applications, resource supply level(s) could be used to selectively enrich desired versus undesirable populations. Although this example is simple, the same logical framework might be used to manipulate more complex systems. Although RRT appears ideal for the control of engineered processes, it has limitations. In particular, RRT presumes that the specific resources that control community composition can be clearly defined. However, basic logic and practical data suggest that real systems are not usually that simple. In fact, few biological systems have a small number of well-defined controlling resources, and as a result, RRT can sometimes oversimplify the factors that dictate community structure and function. There are alternative scenarios where RRT is clearly useful: an ideal example being the use of iron limitation and elevated pH levels to promote carbon tetrachloride degradation under anoxic conditions by Pseudomonas stutzeri strain KC (9, 10). In this case, research indicated that these two “resources” dominated strain KC survival in treatment environments, and therefore, could be used for process control. Unfortunately, this application is rather unusual because the resource supply–community composition relationship is quite straightforward. Therefore, we cannot presume that RRT will always directly extend to other biological processes in which resource–community relationships are more complex. However, Huisman and Weissing have recently shown that, in theory, the governing equations used in RRT can be used to describe more complex and dynamic communities (9); fortunately, a framework exists that allows us to consider such nonlinear and possibly chaotic systems (10).
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ement in assessing the implications of nonlinear dynamics for engineered processes is that this worldview is deterministic (although it does not appear that way). May showed that a single microbial population, grown under density-constrained growth conditions, has innately irregular temporal growth patterns even if the system model is the “simple” logistic equation (10). For example, Figure 2 describes how population size changes over time differently in conjunction with even small changes in specific growth rate, r (8). The figure shows that with only variations in r, shifts occur in population dynamics from stability at a point (r = 1.5) to period-two oscillatory stability (r = 2.2) to higher-period stability (r = 2.65). In fact, this model system becomes wholly unstable and crashes when r > 3.0 (8). Although simple, this example shows that dynamic population behavior is probably innate to biological systems, including engineered biological processes. The figure suggests, therefore, that true “steady-state” conditions may never really exist in engineered processes, even in processes with no transient inputs or apparent variations in growth conditions, and that such processes will naturally operate in orbits, continually revolving around a median condition rather than at a discrete point. In reality, this behavior is more consistent with our collective experience than the model provided by traditional descriptions. The realization that microbial populations and communities are innately dynamic has various impliFEBRUARY 1, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY ■ 67 A
cations in biological process engineering. For example, two bioreactors operating under identical external conditions will almost never operate at the same, single operating point. This was clearly observed by Kaewpipat and Grady, who found two rapidly diverging microbial communities (identified using denaturing gradient gel electrophoresis) in two identical laboratory reactors inoculated from the same microbial seed (13). In fact, this type of reactor variability in “ideal” systems might challenge the presumption that reactor transients are only due to reactor “noise” (e.g., hydraulic or organic
FIGURE 2
Theoretical biological temporal dynamics of populations with different growth rates The different specific growth rates, r, are maintained in a virtual continuously stirred reactor: (a) r = 1.5; (b) r = 2.2; and (c) r = 2.65. These are modeled using the difference form of the logistic equation, where population size is the dependent variable, r is varied, the initial population size is 100, time increments change by 1 unit, and the carrying capacity of the system equals 10,000 (7). (a) 12,000 Population size
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loading variations). Nonlinear dynamics suggest that such irregular activity simply results from the complex nature of the biological system. Further, nonlinear dynamics explains why biological processes become more unpredictable when solids retention time (SRT), which inversely affects specific growth rate in continuous-flow bioreactors without recycle, is low. At low SRTs, r will increase and innately generate more operational dynamic variability (Figure 2). Such dynamic variation, which has been observed for years, has caused engineers to avoid high r values (i.e., they use higher SRTs), and in turn, design larger-capacity bioreactors to avoid unstable operation. This has sometimes resulted in oversized reactors and greater expense. However, if one recognizes that nonlinear dynamics describe reactor behavior best, it might be possible to use control theories that account for dynamic variability (14). Finally, if one recognizes that dynamic variability over time in reactor biomass is normal, one’s perspective on “stable” will change. For example, it is instinctive for a biological process operator to respond to fluctuating biomass levels; however, operator responses can be excessive and actually cause greater instability in reactor operations (this increased instability is predicted by nonlinear dynamics). If variation can be predicted, it may be incorporated into design, and units can be allowed to vary and self-correct within a known operational window. In terms of process design, therefore, engineered biological processes should be designed for maximum stability rather than to achieve a specific operating point, and a well-defined control system based on nonlinear dynamic theory should be designed a priori. The problem is that such processes are sometimes so complex that it is very difficult to prove that nonlinear dynamic theory actually applies. Nonlinear dynamics assumes that the underlying biological activity is deterministic and that underlying function(s) can be defined. Although we believe the above is true, the number of resources and complexities that affect community composition and activity may be too extensive to generate useful equations. The trick in applying such principles to engineered systems, however, is in the translation of current mathematical approaches to the design and description of engineered processes, which requires both solid experimental data and calibration with relevant systems. These data currently do not exist. Therefore, in the short term, stochastic approaches may prove more tractable. This leads us to the theory of island biogeography.
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Treatment communities are amazing. Somehow, hundreds or thousands of different organisms comprising thousands of millions of cells must come together to treat the waste. This happens in all treatment systems from a state-of-the-art nutrient removal plant to a compost heap. Cells form flocs, granules, and biofilms without knowing what it is that they are supposed to do. How does this happen? Imagine a bridge assembling itself out of the correct proportion of steel and concrete! Ecologists call this process community assembly. Community assembly may be thought of as occurring
new species is balanced by the extinction of previously prominent species (Figure 3) (2). If true, this model would be of considerable practical importance. For example, the impact of WWTP decentralization on overall system performance might be predicted, as indicated in Figure 3. It is apparent that as “islands” become larger, extinction curves tend to flatten, and therefore, larger islands, such as WWTPs, will have higher biodiversity and will be more stable. In the case of an individual plant, island biogeography implies that one might be able to “design” the diversity of a system by manipulating the probability of extinction and invasion. Thus, in the future, when the global or regional ubiquity of a particular functional group is better known, the probability of that group invading and being lost from a particular treatment plant might be predictable. Thus, an a priori estimate of the likelihood of a treatment plant acquiring a given function can be made. FIGURE 3
Equilibrium model of island biogeography for small, medium, and large islands (a) This conceptual schematic shows the relative size of small, medium, and large islands equally close to the invasion source. (b) This theoretical plot shows extinction curves and an invasion curve. The equilibrium species number, Sˆ, is reached where the rate of invasion of new species (not present on the island) intersects (i.e., balances) the extinction rate. The extinction curve flattens for larger islands, and biodiversity (the equilibrium species number) increases [Sˆ (a) > Sˆ (b) > Sˆ (c)], because larger islands tend to have greater refuge opportunities than smaller islands. A further consequence of greater extinction rates on smaller islands is that for any given invasion curve, the turnover of species— indicated by the point where the invasion and extinction curves intersect—will be greater on smaller islands. Thus, smaller wastewater treatment plants should have more variable, less stable microbial populations (1). (a) a
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at essentially the species level (i.e., the number of species in a treatment system) and the single-cell level. At present, community assembly cannot be rationally engineered. Nevertheless, practicing engineers have a strong intuition that the nature and composition of the bacterial diversity in wastewater treatment plants (WWTPs) are related to performance. For example, experienced engineers frequently observe that reactor communities become acclimated to wastewaters and promote this by seeding with appropriate biological material. However, neither seeding nor acclimatization ensures success. Engineers investigating aerobic and anaerobic systems have produced compelling evidence for a relationship between diversity and plant performance. For example, work on anaerobic digesters suggests that process stability and instability may be related to the presence or absence of multiple pathways for volatile fatty acid production (15). Alternately, other work with activated sludge reactors suggests that some designs support greater ammonia-oxidizing bacteria (AOB) diversity and greater process stability than others (16, 17). The relationship among diversity, community structure, and performance may have implications for such diverse applications as the decentralization of domestic wastewater treatment (the use of numerous small WWTPs instead of one large facility) or the intensification of industrial wastewater treatment. The minimum level of diversity required for stable performance may dictate the limits of both decentralization and intensification. At the larger scale, engineers have classically drawn on the 80-year-old Beijerinck hypothesis “that everything is everywhere and the environment selects”. This way of thinking imagines the same limited range of organisms being deterministically selected from the environment to treat the waste at all places and all times. Some doubt is being cast on this model by the introduction of modern molecular tools that permit the rapid study and comparison of the most abundant bacterial taxa in WWTPs. Several authors studying aerobic and anaerobic systems have used such methods to examine the stability and reproducibility of diversity in WWTPs. The results have been superficially puzzling. The general bacterial community is highly variable, never settles down to a stable community, and varies between reactors. However, other groups, notably the Archaea in anaerobic systems and the AOB in activated sludge, tend to be more reproducible among reactors and less variable within a given plant. Theoretical support for the new observations can be found in the classical ecological literature. MacArthur and Wilson predicted that in insular communities, such as treatment plants, species composition will change over time, with the number of species reaching an equilibrium in which invasion of
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The work of MacArthur and Wilson has taken an important step forward with the recent publication of two new stochastic neutral community models (NCM) (18, 19). These are neutral models because they assume that all organisms within a particular functional group have identical properties—no single species or individual is, on average, any more competitive than any other member of a particular group. Of course, even the most pragmatic engineer might balk at such an assumption. However, Bell and Hubbell have shown that neutral models reproduce virtually all the generally accepted patterns in multispecies communities (18, 19). It would be very exciting if these conceptually simple theories could be used to design diversity. The changes in diversity and performance we observe at the treatment plant scale (hundreds of cubic meters) are the resultant properties of millions of individual cells (1–2 cubic micrometers), which have formed into flocs or biofilms in treatment plants, wetlands, or aquifers. We can measure systems at these larger scales. However, engineering at these scales is impeded because of the difficulty of modeling individual bacteria. Very significant advances have been made in biofilm modeling by using cellular automata in two- and three-dimensional mesoscale representations (19). These procedures accurately mimic the observed macrostructure of biofilms. Although these procedures are currently computationally expensive, this difficulty will certainly be overcome in the near future by techniques such as grid computing. The biofilm models like NCM make very few assumptions about the properties of the organisms. This is a very important point. For although these systems are obviously very complex, the theories that we need to understand them could be very simple. In fact, it is foreseeable that we could soon be experimenting with biological process designs on a computer in exactly the same way that aeronautical engineers test a new wing design.
Engineered biological process design in the future We believe that we are standing on the threshold of a renaissance in the engineering of biological systems. This belief is not based on a single method or technology but on a synthesis of apparently disparate developments in ecological theory, molecular microbial ecology, and the best of engineering philosophy. In just the three cases presented here, we have shown that ecology can tell us valuable things about the relationship between resource levels and community structure, the basis of process operational transients, and the effects of community assembly on process biodiversity and operating stability. We feel that when the implications of these theories are more fully explored, additional insights will be possible; in Tredgold’s terms, microorganisms will be the “power in nature”, theoretical microbial ecology will be the “natural philosophy”, and cleaner air, water, and soil will be the result. 70 A ■ ENVIRONMENTAL SCIENCE & TECHNOLOGY / FEBRUARY 1, 2003
As a final example, we foresee a future in which operating conditions, such as the general composition of an engineered microbial community, might actually be controllable. This might be done through a combination of the rules of RRT and island biogeography. RRT will specify resource levels that will maximize the success of desired groups of organisms, and island biogeography will tell us the probability of their establishment and their biodiversity within an operating process. Further, nonlinear dynamics will then describe a priori the group’s temporal variability within the system and help define appropriate control strategies to maximize process stability and performance. Although work remains to be done, we strongly believe this level of control will be possible and hope that through this article, others will be stimulated to help bring theoretical ecology into their worldview of biological process design. Thomas P. Curtis is a senior lecturer and Ian M. Head is a reader in the School of Civil Engineering and Geosciences, University of Newcastle-upon-Tyne in the United Kingdom. David W. Graham is an associate professor at the University of Kansas. Address correspondence to Graham at the Department of Civil, Environmental and Architectural Engineering, 4002 Learned Hall, Lawrence, KS 66045-2225 (dwgraham@ ukans.edu).
References (1) Tredgold, T. Institution of Civil Engineers Charter, London, June 3, 1828. (2) MacArthur, R.; Wilson, E. The Theory of Island Biogeography; Princeton University Press: Princeton, NJ, 1967. (3) Downing, A. L.; Painter, H. A.; Knowles, G. J. Inst. Sewage Purification 1964, 2, 130. (4) Lawrence, A. W.; McCarty, P. L. Proc. Am. Soc. Civil Eng. J. San. Eng. Div. 1970, 96, 757. (5) Tilman, D. Resource Competition and Community Structure; Princeton University Press: Princeton, NJ, 1982. (6) Smith, V. H. Advances in Microbial Ecology; Jones, J. G., Ed.; Plenum Press: New York, 1993; Vol. 13, pp 1–37. (7) Smith, V. H.; Graham, D. W.; Cleland, D. D. Environ. Sci. Technol. 1998, 32, 3386. (8) Graham, D. W.; Curtis, T. P. In Bioremediation: A Critical Review; Head, I., Singleton, I., Eds.; Horizon Press: Oxford, United Kingdom, 2003. (9) Huisman, J.; Weissing, F. J. Nature 1999, 402, 407–410. (10) May, R. M. Stability and Complexity in Model Ecosystems; Princeton University Press: Princeton, NJ, 1974. (11) Logan, B. E.; Wilkinson, D. Limnol. Oceanogr.; 1990, 35, 130. (12) Gleick, J. Chaos: Making a New Science; Vintage: London, 1998. (13) Kaewpipat, K.; Grady, C. P. L. Water Sci. Technol. 2002, 46, 1–2, 19. (14) Dochain, D.; Perrier, M. Adv. Biochem. Eng. 1997, 56, 148. (15) Hashsham, S. A.; et al. Appl. Environ. Microbiol. 2000, 66, 4050. (16) Daims, H.; Nielsen, J. L.; Nielsen, P. H.; Schleifer, K. H.; Wagner, M. Appl. Environ. Microbiol. 2001, 67, 5273. (17) Daims, H.; Purkhold, U.; Bjerrum, L.; Arnold, E.; Wilderer, P. A.; Wagner, M. Water Sci. Technol. 2001, 43, 9. (18) Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography; Princeton University Press: Princeton, NJ, 2001. (19) Bell, G. Science 2001, 293, 2413.
