1 Center for Biofilm Engineering and Department of Chemical and Biological Engineering, Montana State University-Bozeman, Bozeman, MT 59717-3980, USA
2 Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
Correspondence
Joao B. Xavier
J.Xavier{at}tnw.tudelft.nl
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ABSTRACT |
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INTRODUCTION |
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Promoting detachment is the least investigated of the possible strategies to remove unwanted biofilms (Stewart et al., 2000). The use of substances to induce biofilm removal directly by destroying the physical integrity of the biofilm matrix would be an attractive alternative for both medical and industrial applications where complete biofilm removal is essential. In industrial applications, this approach would also have the advantage of reducing reliance on inherently toxic antimicrobial agents, whose continued use is fundamentally at odds with the trend towards increasingly restrictive environmental regulations (Chen & Stewart, 2000
).
Biofilms are primarily composed of bacteria, extracellular polymeric substances (EPSs) of microbial origin and other particulate substances. EPSs constitute a matrix embedding bacterial cells and almost certainly have essential roles in defining the cohesiveness and other physical properties of these attached microbial communities (Wingender et al., 1999). Biofilm EPSs are typically composed of diverse substances, including polysaccharides, proteins, nucleic acids, lipids and humic substances (e.g. Nielsen et al., 1996
; Tsuneda et al., 2003
). Substantial evidence exists supporting the role of polysaccharides in the cohesiveness of the EPS matrix (e.g. Boyd & Chakrabarty, 1995
; Hughes et al., 1998
). Multivalent cations such as calcium and magnesium also probably play a role in the cohesiveness of microbial aggregates, as evaluated from the study of anaerobic-sludge granules (Grotenhuis et al., 1991
), activated-sludge flocs (Higgins & Novak, 1997
) and biofilms (Chen & Stewart, 2002
), by bridging negatively charged sites on extracellular polymers to create stable intermolecular and cellEPS connections (Mayer et al., 1999
).
Methods for promoting biofilm detachment by using chemical agents aimed at attacking the EPS have been investigated, so far with mixed results (Chen & Stewart, 2000). Table 1
lists several substances reported in the literature to affect the cohesiveness of EPS of bacterial origin. These substances, being enzymes, chelating agents or other agents, may reduce EPS cohesiveness through a variety of mechanisms. In the present article, all such substances that affect EPS cohesiveness and may potentially be used to promote biofilm detachment will generally be termed detachment-promoting agents (DPAs). Several enzymes have been observed to induce detachment by acting on the EPS matrix, by depolymerizing either polysaccharides (Allison et al., 1998
; Boyd & Chakrabarty, 1994
; Chen & Stewart, 2000
; Itoh et al., 2005
; Kaplan et al., 2004
; Mayer et al., 1999
) or extracellular DNA (Whitchurch et al., 2002
). Chemicals that alter the ionic strength and composition of the liquid medium and affect electrostatic interactions involved in the cohesiveness of the EPS matrix, such as salts or chelating agents (Chen & Stewart, 2000
; Mayer et al., 1999
), may also be used as DPAs.
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IbM was previously applied to study biofilm-structure dynamics in the presence of erosion forces in monospecies biofilms (Xavier et al., 2004, 2005b
), EPS production (Kreft & Wimpenny, 2001
; Xavier et al., 2005a
, b
) and multispecies biofilms (Picioreanu et al., 2004
; Xavier et al., 2005a
). Previous studies using other 2D- or 3D-modelling approaches addressed the effect of detachment caused by mechanical shear (Hermanowicz, 2001
; Picioreanu et al., 2001
), biomass decay (Pizarro et al., 2001
), detachment induced by a chemical produced by the biofilm (Hunt et al., 2003
) and starvation (Hunt et al., 2004
). The present study is the first application of a multidimensional biofilm model for the evaluation of strategies for the removal of unwanted biofilms by treatment with chemical substances.
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MODEL DESCRIPTION |
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Biofilm system.
As in other biofilm models (e.g. Wanner & Gujer, 1986), the dynamics of two types of components are followed here. First, the soluble matter comprises any dissolved substance, such as substrates, products or DPA. Second, the particulate matter consists of solid matter, such as microbial cells and EPS. The computational volume used for the simulations represents a typical biofilm system composed of a biofilm (solid) phase and a liquid phase. Fig. 2
is a schematic representation of the system phases and geometry.
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Dynamics of particulate components
Representation.
Biofilm matter (biomass or particulate components) is represented in terms of its composition in active biomass (i.e. living micro-organisms) and EPS. For simplicity, it is assumed here that particulate matter exists only in the biofilm phase and not in the liquid phase. The IbM approach is used for the spreading of the biofilm matter as described previously (Xavier et al., 2005a). In the original framework, only one EPS type was allowed for each active biomass species. In this study, the framework is extended to allow EPS composition to include any number of particulate species. This extension permits implementation of two EPS states, cohesive (natural EPS) and compromised (EPS altered by DPA, denoted here as EPS*), with consequences that will be explained further. In IbM, biomass is represented by using spherical particles that act independently. These particles are entities with an internal state, defined in the present work in terms of their biomass composition (in active mass, EPS and EPS*), size and location in space. The size (volume, Vp) of a particle is related to its composition. Each of the biomass components, such as the active mass (MX), the EPS mass (MEPS) and the EPS* mass (
), accounts for a volume related to its specific mass (
):
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Processes.
Biomass particles follow behaviour rules that mimic the behaviour of a microbial cell. They can: (i) grow by intake of nutrients; (ii) divide, creating an offspring agent; (iii) move (in continuous spatial coordinates) when pushed by neighbouring particles; and (iv) produce and excrete EPS. In addition, biomass particles can be removed from the biofilm body by any detachment mechanism. Growth of the spherical biomass particles occurs when active biomass or EPS is produced, the production of which is governed by equations (4) and (6), respectively. Moving follows the iterative procedure of the IbM, where the individuals (biomass particles) shove each other every time step to undo any overlap between neighbours generated by divisions and growth.
Kinetics.
The growth rate of active biomass (rX,prod) is governed by a Monod-type expression:
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The rate of EPS production (rEPS,prod) is assumed to be coupled to biomass growth and related to rX,prod by a yield coefficient YEPS, as described previously (Stewart, 1993):
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The model proposed here also addresses the possibility of inactivation of the DPA. An example of an inactivation process is the decay of the DPA, applicable for instance in the case where the DPA is an enzyme. For simplicity, first-order decay rate will be used to model this case:
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Stoichiometry and rates of reactions used by the current biofilm model are presented in Table 2.
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Introducing the influence of biofilm cohesiveness in the detachment expression.
Detachment occurs when external forces acting on the biofilm exceed the local biofilm cohesiveness. A biofilm cohesiveness factor, , is proposed here, so that the local detachment speed is inversely proportional to
:
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In equation (9), (x) refers to the local value of the biofilm cohesiveness at location x on the biofilm/liquid interface.
A detachment-speed function used previously for modelling biofilm development on a planar substratum (Xavier et al., 2004, 2005b
) included a dependence on the square of the distance to the solid substratum, x2, and the local biofilm density,
(x), as follows:
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The value for the local biofilm density, (x), used in this expression is the total biomass (all types of EPS and active mass) per unit volume. It is noteworthy that this modelling framework allows for other types of detachment functions to be used, allowing the model to be customized for each particular system. The dependence on x2 was chosen because it is used commonly in biofilm studies (Stewart, 1993
; Wanner & Gujer, 1986
; Xavier et al., 2004
, 2005b
) and ensures the existence of a steady state (Stewart, 1993
). The detachment-speed coefficient in equation (10), kdet, can be used to alter the effect of the external forces acting on the biofilm. Equation (10) may be interpreted as a balance between external forces acting on the biofilm (the product kdetx2) and its cohesive forces, here proportional to the local biofilm density,
(x):
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In the present study, equation (10) will be extended to include the role of EPS In the cohesiveness of the biofilm matrix. In order to encompass the generality of mechanisms for the action of DPAs, two states for EPS, the cohesive state and the compromised state, are proposed. The cohesive state (represented by subscript EPS) refers to EPS in its normal state, which provides the biofilm with its natural characteristics. This is a general representation of the EPS that groups all its possible components (polysaccharides, proteins, etc.). The compromised state (represented by subscript EPS*) refers to a denatured state of the EPS after reacting with a DPA. Consequently, two EPS concentrations are distinguished, CEPS and , as mass of EPS per biofilm volume.
The rate of EPS decomposition, rEPS,dec, will depend on the type of the DPA used. For example, if the DPA is an enzyme, MichaelisMenten kinetics may be used:
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In equation (12), kEPS,dec is a decomposition-rate coefficient, CDPA is the DPA concentration and KEPS is the MichaelisMenten (or saturation) coefficient.
Conversion of EPS into EPS* will depend on the particular composition of the biofilm EPS. The following generic expression is proposed here:
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If we define the local fraction of EPS in its cohesive state, relative to the total EPS (cohesive and compromised states), as
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This states that biofilm cohesiveness decreases with a decreasing fraction of cohesive EPS, fEPS. Any order () of the dependence on fEPS may be used, depending on the particular case being modelled. The order
will reflect the importance of the EPS components affected by the DPA for the biofilm cohesiveness. An expression that extends the detachment function (equation 10) is then derived from equation (15):
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Simulations carried out.
Parameters for biomass growth and EPS production obtained from the literature were used in the simulations and are listed in Table 3. The effectiveness of biofilm removal using a DPA was evaluated by the time course of the simulated reduction of active mass in the biofilm. Several scenarios were evaluated, considering various characteristics of the DPA. A set of six variables defines each particular scenario. These variables are the diffusivity of the DPA (DDPA), the decay-rate coefficient of the DPA (kDPA,dec), the concentration of DPA used in the treatment (CDPA,treat), the decay-rate coefficient of the EPS (kEPS,dec), the yield (
) and the order of the EPS cohesiveness dependence on fEPS (
). The first two variables may be grouped, together with the biofilm thickness, Lf, in a dimensionless number, the Thiele modulus
, defined as
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This Da number relates the timescale of cohesive EPS decay to the timescale of EPS production by the micro-organisms. The remaining two variables from the set, and
, are addressed independently.
was varied to represent scenarios in which the EPS mass is degraded at varying degrees. The effect of parameter
on biofilm removal was also assessed by carrying out simulations with values of
ranging from 0·2 to 10.
Animations concerning all of the simulations analysed in the present study were also produced. These animations may be obtained in the form of digital video files from our website at http://www.biofilms.bt.tudelft.nl/biofilmControl/index.html.
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RESULTS |
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Effect of on biofilm removal
Parameter defines the order of the dependence of biofilm cohesiveness on the fraction fEPS through equation (15). The effect of
on the effectiveness of the DPA treatment was assessed by carrying out simulations with values of
ranging from 0·2 to 10. Fig. 7(c)
shows the time course of the decrease in active biomass in the biofilm obtained from those simulations (carried out with
=3·6 and Da=0·71). The results show that the predicted effectiveness of the biofilm treatment is highly dependent on the value of
used. Simulations carried out with
>1 predicted full biofilm removal within 100 h simulated treatment with a DPA. At
1, meaning a weaker dependency of biofilm cohesiveness on the EPS fraction, the simulations did not predict full removal within 100 h. Furthermore, all simulations performed with values of
lower than 1 predicted a subsequent boost in biofilm development after an initial decrease in biofilm active mass. Presently, there are no experimental data available on the literature that could be used to determine
. Possible experimental methods to measure
are proposed in the Discussion.
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DISCUSSION |
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The dual-state representation for EPS proposed here and the parameter allow the description of several DPA action scenarios. Such scenarios range from cases where EPS mass is converted entirely to a non-cohesive substance, the compromised state, to cases where the EPS does not play a role in the biofilm cohesiveness. The latter case, represented by setting
, assumes that no conversion to a compromised state exists. Intermediate scenarios, where the solid EPS matrix is partially converted to EPS* (compromised EPS) and partially dissolved, are simulated by using values of
between 0 and 1. The simulations carried out at a range of
values (Fig. 7a
) indicate that DPAs whose action is directed at affecting the biofilm cohesiveness (represented by values of
closer to 1) may be the most effective in removing the biofilm. DPAs whose action is directed at degrading EPSs that do not influence the biofilm cohesiveness (cases represented by using low
values) may not be a suitable strategy for removing the biofilm. Results from simulations for these cases predict that biofilms that are thin and dense and that have a high content of active microbial biomass will result from the treatment, rather than full removal of the biofilm. This prediction is purely theoretical and only valid if the assumptions presented in the model formulation are themselves valid. Nevertheless, the mechanism observed here may be a possible explanation for the experimentally observed failure of alginate lyase to remove P. aeruginosa biofilms when the same enzyme is able to degrade alginate in other settings (Christensen et al., 2001
).
The parameter , used here to define the order of the dependence of the biofilm cohesiveness on the fraction fEPS, was also observed to be highly important for the efficacy of a treatment scenario. This was revealed from the results shown in Fig. 7(c)
. The value of
represents the importance that the EPS component affected by the DPA has on the biofilm cohesiveness.
=1 indicates that biofilm cohesiveness is affected linearly as EPS is converted to EPS*. Values of
>1 may represent, for example, cases where the component affected by DPA is involved in several structural bonds in the EPS matrix. In such cases, biofilm cohesiveness could be affected non-linearly and with an order of fEPS higher than 1. Values of
<1 represent, in turn, the opposite scenario, where the affected EPS component does not play an important role in defining the EPS cohesiveness. Experimental values for parameters such as
are presently unavailable, although experimental techniques exist that may potentially be applied to obtain such information. The method proposed by Ohashi & Harada is based on applying both tensile force and shear force and was used successfully to measure adhesion strength of denitrifying biofilms (Ohashi & Harada, 1996
) and biofilms grown under different conditions (Ohashi et al., 1999
). A method developed recently that uses micro-cantilevers (Poppele & Hozalski, 2003
) to measure the tensile strength of biofilms and microbial flocs could be used to determine the alteration in biofilm strength as a consequence of the presence of a DPA substance. Results from such experiments would allow derivation of a function for the alteration of the biofilm cohesive strength.
The study presented here comprises four simulations of undisturbed biofilm growth and 43 simulations of biofilm-treatment scenarios. Animated visualizations of biofilm-structure evolution during the treatment with DPA may be obtained for all simulations from our website at http://www.biofilms.bt.tudelft.nl/biofilmControl/index.html.
Of all of the simulations of biofilm treatment, however, only a limited number predicted a successful, complete biofilm removal. A common pattern was observed in all of the simulations predicting complete biofilm removal: an initial rapid removal of the largest part of the biofilm occurs within the first moments after introduction of the DPA in the medium. Following this initial period, removal of the remaining portion of the biofilm requires a relatively long period, still in the presence of the DPA. This last portion of the biofilm often consists of a thin and sparse layer of biofilm. The simulation results shown in Fig. 5 exemplify this observation, as analysed previously in the Results. From an analysis of these simulation results, we can conclude that the persistence of a last sparse layer of biomass results (i) from a lower detachment rate acting on thinner biofilms (because the detachment-speed function decreases as the biofilm thickness decreases) and (ii) from higher specific-growth and EPS-production rates for the thin biofilm (because of lower mass-transfer limitations and lower amounts of biomass sharing the substrate). The removal of the last fraction of the biofilm may constitute the majority of the time required for full biofilm removal, as was the case for the simulation shown in Fig. 5
. For this simulation, as stated earlier, detachment of the last 1 % of the micro-organisms required 94 % of the total treatment time. This fact alerts to the necessity of a more precise knowledge of the detachment dynamics of the bacteria attached directly to the solid substratum for an accurate prediction of the efficiency of treatment using modelling techniques.
Other parameters related to the action of a DPA, i.e. reaction-rate coefficients, reaction yields and diffusivity coefficients, are also presently unavailable in the literature. Due to the absence of extensive information on the mechanisms of action of DPAs, the model presented here was kept as simple and generic as possible, but was still based on widely accepted principles. The framework used to construct the model allows even more detailed representations of the biomass. Biofilm models defined in this framework may include inert formation and multiple microbial species, as shown in previous studies (Xavier et al., 2005a, b
). Introducing more detail into the biomass description here would have been undesirable, as it would create the necessity for more assumptions and complicate derivation of the trends reported. However, if more information is made available in the future from experimental characterization of biofilm-removal experiments, the model can easily be extended to accommodate new processes or biomass components. For example, it is expected that, for older biofilms, the formation of inert material will play a role in biofilm cohesiveness, as inert material will not grow or produce more EPS and its presence may affect the biofilm cohesiveness.
Conclusions
The modelling approach proposed here, consisting of a theoretical framework and its numeric implementation by using IbM, allowed the prediction of the efficacy of biofilm-control scenarios by using DPAs. The results of simulations carried out by using several sets of parameters produced the following conclusions.
The efficacy of using a DPA for removing a biofilm will be highly dependent on: (i) the balance between the mass-transport properties of the DPA through the biofilm matrix and its decay rate (quantified by Thiele moduli); (ii) the balance between the kinetics of action of the DPA on the EPS and EPS production by the micro-organisms [which may be quantified by the Damköhler number proposed in equation (18)]; and (iii) the mechanisms of influence of the DPA on the EPS, included in the model through the parameters and
.
For cases where treatment with a DPA does result in complete removal of the biofilm, it is expected that the removal will follow a pattern where the majority of the biofilm biomass is removed in the initial instants of the treatment, but the removal of the last fraction of the biofilm will take a considerably larger fraction of the total treatment time.
The results from simulations presented here alert for the necessity to investigate experimentally the effect of DPAs on biofilm cohesiveness. This is essential for the accurate prediction of the efficiency of control strategies using modelling approaches.
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Received 29 April 2005;
revised 29 July 2005;
accepted 21 September 2005.
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