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Keywords:

  • apparent growth yield coefficient;
  • growth and maintenance respiration;
  • maintenance coefficient;
  • physiological maintenance factor;
  • specific maintenance rate;
  • true growth yield coefficient

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

We attempted to reconcile three microbial maintenance models (Herbert, Pirt, and Compromise) through a theoretical reassessment. We provided a rigorous proof that the true growth yield coefficient (YG) is the ratio of the specific maintenance rate (a in Herbert) to the maintenance coefficient (m in Pirt). Other findings from this study include: (1) the Compromise model is identical to the Herbert for computing microbial growth and substrate consumption, but it expresses the dependence of maintenance on both microbial biomass and substrate; (2) the maximum specific growth rate in the Herbert (μmax,H) is higher than those in the other two models (μmax,P and μmax,C), and the difference is the physiological maintenance factor (mq = a); and (3) the overall maintenance coefficient (mT) is more sensitive to mq than to the specific growth rate (μG) and YG. Our critical reassessment of microbial maintenance provides a new approach for quantifying some important components in soil microbial ecology models.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

Maintenance requirements of microbial biomass represent the additional consumption of energy and carbon for purposes other than the production of biomass (Marr et al., 1963; Anderson & Domsch, 1985a). The early terminology ‘endogenous metabolism’ postulated by Herbert is thought to be equivalent to the maintenance energy requirements (Dawes & Ribbons, 1962, 1964, 1965; Pirt, 1965; Mason et al., 1986). Although the concept of maintenance energy is largely studied in starving cells, growing cells should also be included (Dawes & Ribbons, 1962, 1964). Some studies presume that growth is a secondary feature of energy utilization after maintenance purposes (Dawes & Ribbons, 1964). van Bodegom (2007) summarized eight nongrowth components for microbial maintenance: (1) cell motility, (2) osmoregulation, (3) proofreading, synthesis and turnover of macromolecular compounds, (4) defense against O2 stress, (5) shifts in metabolic pathways, (6) energy spilling reactions, (7) changes in stored polymeric carbon, and (8) extracellular losses of compounds not involved in osmoregulation. The first four components were classified as the physiological maintenance (van Bodegom, 2007).

Mathematical modeling of the growth of microbial biomass and consumption of substrate usually follows (Tempest & Neijssel, 1984):

  • display math(1)
  • display math(2)
  • display math(3)

where x and s are the concentrations (contents) of microbial biomass and substrate, respectively; μ(s) is the observed specific growth rate of microbial biomass (h−1); q(s) is the observed specific consumption rate of substrate (h−1); and Y denotes the apparent growth yield coefficient.

It is noted that Eqn (3) is a general formula relating the growth of microbial biomass to the consumption of substrate (van Bodegom, 2007). However, the expressions for Eqns (1) and (2) are specific and can be different based on different assumptions (Beeftink et al., 1990). Two models have been widely used and their major difference is associated with the maintenance component. One is the Herbert model specified by the specific maintenance rate (a) in Eqn (1), which may be regarded as an endogenous metabolism rate resulting in consumption of maintenance energy and decrease in the biomass (Beeftink et al., 1990). The other is the Pirt model characterized by the maintenance coefficient (m) in Eqn (2) representing the consumption of substrate for nongrowth functions (Pirt, 1965).

The two models have caused the debates on the relationship between the two rate constants, that is, a and m. Most of the studies postulated or derived that the true growth yield (YG) was the key connecting the two parameters (Schulze & Lipe, 1964; Pirt, 1965; Nagai et al., 1969; Van de Werf & Verstraete, 1987; Beeftink et al., 1990). Another interpretation was that the apparent yield coefficient (Y) rather than YG served as the link, and the overall maintenance coefficient was insensitive to the variation in physiological maintenance (van Bodegom, 2007). In addition, Beeftink et al. (1990) put forward a model called the ‘Compromise’, based on mechanistic considerations that combined the features of previous models. However, the maximum specific growth rates were regarded as the same for all models, and the solutions for μ and q from the Compromise model were thought to be between the solutions from the Herbert and Pirt models.

A complete analysis of all the maintenance components is beyond the scope of this study. The present contribution attempts to reconcile the models describing microbial maintenance through clarifying the relationships between the three models and deriving a new equation for the overall maintenance coefficient (mT). We also aim to elucidate implications for the modeling of soil organic carbon (SOC) decomposition based on our reassessment of microbial maintenance.

Models for microbial maintenance

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

Herbert model

In the Herbert model (Dawes & Ribbons, 1964; Pirt, 1965), the specific maintenance rate (endogenous metabolism) is regarded as a negative growth rate:

  • display math(4)
  • display math(5)

where a is termed the specific maintenance rate (h−1); μmax,H is the maximum specific growth rate for the Herbert model (h−1); and YG is the ‘true’ growth yield (Pirt, 1965; Neijssel & Tempest, 1976) or potential (maximum) growth yield coefficient (Kuhn et al., 1980; Tempest & Neijssel, 1984) of microbial biomass. The function g(s) = s/(Ks s) satisfies the requirements that g(s) = 0 at s = 0 and g(s)[RIGHTWARDS ARROW]1 when ≫ Ks, where Ks is the half-saturation constant with the same units of substrate.

The Herbert model allows the decrease in microbial biomass resulting from microbial turnover rather than maintenance respiration per se at low substrate concentration where μmax,H·g(s) < a. The maintenance energy results in a decrease in the growth of microbial biomass. It is worth noting that Eqn (4) and (5) should be presented together to express the basic idea of the Herbert model. In some studies (e.g., Schulze & Lipe, 1964; van Bodegom, 2007), only Eqn (4) was used for analysis, which resulted in misunderstanding or incorrect derivation of the relationship between the specific maintenance rate (a) and the maintenance coefficient (m) of the Pirt model.

Pirt model

The Pirt model postulates that the consumption of substrate also supplies energy for maintenance in addition to microbial growth (Pirt, 1965):

  • display math(6)
  • display math(7)

where μmax,P is the maximum specific growth rate for the Pirt model, and m is the maintenance coefficient (h−1). The same substrate function g(s) is used here as in the Herbert model. In the Pirt model the observed specific growth rate is always nonnegative.

Compromise model

Both the Herbert and Pirt models presume that maintenance energy depends only on time and microbial biomass. However, many experiments observed that maintenance energy varies with growth stage and specific growth rate (Pirt, 1965, 1982; van Bodegom, 2007). The assumption of constant specific maintenance rates was thought to be invalid for Escherichia coli and Bacillus polymyxa cultures (van Verseveld et al., 1984). As a result, the maintenance coefficient or the specific maintenance rate can vary with the substrate concentrations. Experiments have indicated that maintenance energy is supplied by substrate under sufficient substrate conditions (Dawes & Ribbons, 1964). Thus, the equation including a maintenance component like Eqn (7) is more explicit than Eqn (5). In addition, the biomass yield might decrease at slower growth rates (Dawes & Ribbons, 1964), which means that microorganisms might cover their maintenance requirements from biomass when substrate becomes depleted and the formula like Eqn (4) is somewhat more advantageous than Eqn (6). Therefore, the models of Herbert and Pirt have their own attractive features. It is feasible to combine these features to develop a compromise model.

Based on the above analysis, assuming that maintenance energy is supplied by both substrate and biomass:

  • display math(8)
  • display math(9)

where μmax,C is the maximum specific growth rate for the Compromise model, mq denotes the specific physiological maintenance factor (h−1), h(s) is a function of s fulfilling the requirements of h(0) = 0 and h(s)[RIGHTWARDS ARROW]1 when ≫ Ks, and b(s) is a function of s.

Assuming that Eqns (8) and (9) satisfy the relationship as indicated in Eqn (1) of Pirt (1982):

  • display math(10)

Substituting Eqn (8) and (9) into (10), one obtains that b(s) = mq·[1 − h(s)] and

  • display math(11)

Equations (8) and (11) constitute a model presented in Beeftink et al. (1990), where the model was derived by mechanistic considerations. It is a compromise between the Herbert and the Pirt model. The Compromise model follows four assumptions (Beeftink et al., 1990): (1) negative net growth at s[RIGHTWARDS ARROW]0, (2) no substrate consumption at s = 0, (3) no microbial biomass degradation at ≫ Ks, and (4) μ(s)[RIGHTWARDS ARROW]μmax,C at ≫ Ks. Therefore, h(s) = s/(Ks s), the same as g(s) in the Pirt model, is a suitable selection. It is noted that we modified the condition for sufficient substrate from s[RIGHTWARDS ARROW]∞ stated by Beeftink et al. (1990) to ≫ Ks since h(s)[RIGHTWARDS ARROW]1 can be easily achieved under the latter condition.

Relationship between three models

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

Two forms of relationship have been proposed to relate m to a: one is the commonly used a = YG·m (Pirt, 1965), the other is a = Y·m (van Bodegom, 2007). Although the first one has been widely used, no rigorous and clear derivations are available. We agree that the first form (a = YG·m) is correct and the proof is shown as follows.

Two assumptions are made for the derivations: (1) YG is a constant and identical for the three models; and (2) the observed maximum specific growth rate [i.e., maximum μ(s) in Eqns (4), (6), or (11), denoted by μm] at ≫ Ks for the three models should be equal, as well as the observed maximum specific consumption rate [i.e., maximum q(s) in Eqns (5), (7), or (8), denoted by qm].

When ≫ Ks, g(s)[RIGHTWARDS ARROW]1, from Eqns (4) and (6), one can derive that

  • display math(12)

Similarly, from Eqns (5) and (7), it follows that

  • display math(13)

Combination of Eqns (12) and (13) shows that

  • display math(14)

Equations (13) and (14) also imply that μmax,H > μmax,P since both a and m are greater than 0.

Substitution of Eqns (6) and (7) into (3) shows that

  • display math(15)

where μP = μmax,P·g(s).

Substituting Eqn (14) into (15) gives

  • display math(16)

Similarly, from Eqns (3), (4), and (5), we can derive

  • display math(17)

where μH = μmax,H·g(s).

Equations (16) and (17) imply that Y depends on substrate concentration (s) since YG has been assumed constant and both μH and μP are s dependent.

Previous studies did not correctly or convincingly show the validity of Eqn (14). It seemed that Schulze & Lipe (1964) derived Eqn (14), but they mixed Y with YG and presumed that Y = YG. Regardless of this equality, there were sign errors in Eqns (31a) and (32) of Schulze & Lipe (1964). The same sign error occurred in Eqn (2) of Marr et al. (1963). Pirt (1965) directly defined the relation between m and a using the same expression as Eqn (14) and then worked out the same formula as Eqn (16) (Pirt, 1982). van Bodegom (2007) thought that a = YG·m was wrong, but his derivation process was incorrect. Eqn (8b) in van Bodegom (2007) was correct (i.e., the same as Eqn (17) of this study); however, he misrepresented ‘μ’ in his Eqn (8b), as equivalent to the ‘μH’ in our study, but the ‘μ’ in his Eqn (8a) should be the ‘μP’ in our study. Because of the confusion between μH and μP, his Eqn (9) relating a = Y·m was incorrect.

We show [Eqn (12)] that the maximum specific growth rates in the Herbert and Pirt models are not equal (i.e., μmax,H > μmax,P). This was not realized by Beeftink et al. (1990). In his Eqns (6)-(9) and Figs 1 and 2, he indicated that the maximum specific growth rates (μmax,H and μmax,P) for the two models were the same, which could result in different observed maximum specific growth rate (μm) or consumption rate (qm) using different models. This is inconsistent with our knowledge that the maximum observations (i.e., μm or qm) in a given experiment should be the same.

image

Figure 1. Comparison of three models: Pirt, Herbert, and Compromise. μmax,H and μmax,P (h−1) are maximum specific growth rates for the Herbert and Pert model, respectively; Ks is the half-saturation constant with the same units of substrate, and YG is the true growth yield coefficient.

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image

Figure 2. Multi-parameter sensitivity analysis of the overall maintenance coefficient (mT) to three variables: the physiological maintenance factor (mq), the specific growth rate (μG), and the true growth yield (YG). Greater discrepancy between the two (Acceptable and Unacceptable) cumulative probability distribution curves means higher parameter sensitivity.

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Similarly, with the assumption of h(s= g(s)[RIGHTWARDS ARROW]1 at ≫ Ks in combination with Eqns (6), (7), (8), and (11), it follows that

  • display math(18)
  • display math(19)

Comparing the Compromise model with the models of Herbert and Pirt, we found that the Compromise model is identical to the Herbert model for μ and q from the mathematical perspective. However, the Compromise model explicitly expresses that the microbial maintenance is associated with both microbial biomass and substrate and decomposes the overall maintenance into two components in Eqns (8) and (11), respectively. As illustrated in Fig. 1, the solutions of μ and q from the Herbert and the Compromise model are identical given the additional constraints that YG is the same in all model formulations. The values of μ and q by the Pirt model are higher than the μ and q by the Herbert and Compromise models at low substrate concentration. However, with the increasing of substrate concentration, the μ and q by the three models approach the same μm and qm, respectively. Our analysis indicates that the illustrations of Figs 1 and 2 in Beeftink et al. (1990) were incorrect.

Overall maintenance coefficient and sensitivity analysis

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

The overall maintenance coefficient (mT) derived by van Bodegom (2007) was based on the relation of a = Y·m. This incorrect relation resulted in an incorrect expression for mT [see Eqn (18) of his article]. In this study, we did not consider the partitioning of microbial biomass into inactive/reactive fractions. According to the Compromise model described by Eqns (8) and (11), we can add the two maintenance items (mq·h(s)/YG and mq·[1− h(s)]) to give

  • display math(20)

where μG = μmax,C·h(s) denotes the specific growth rate.

Equation (20) shows that mT depends on the physiological maintenance factor (mq), the specific growth rate (μG), and the true growth yield (YG) with the assumption of μmax,C being a constant. It is evident that mT is not a constant with a range of a = mq ≤ mT ≤ mq/YG = m when 0 < YG < 1. Therefore, the overall maintenance coefficient from the Compromise model is between the values from the Herbert model (i.e., a) and the Pirt model (i.e., m).

To determine the relative importance of the three variables in Eqn (20), we carried out a sensitivity analysis (Wang & Xia, 2010; Wang et al., 2012a). The sensitivities of mT to the changes in the three variables are

  • display math(21)
  • display math(22)
  • display math(23)

Defining the sensitivity index (Lenhart et al., 2002):

  • display math(24)

where X represents mq, μG, or YG, I denotes the sensitivity of mT to X at X = X0.

We used the variable ranges mq ∈ (0.01, 0.3) h−1 and μG ∈ (0.01, 0.85) h−1 from Pirt (1982), and YG ∈ (0.2, 0.7) from Devevre & Horwath (2000). Assuming that mp and μG follow log-uniform distributions and YG follows a uniform distribution, we computed the median values as 0.06, 0.09, and 0.45 for mq, μG, and YG, respectively. Using these medians as X0, we finally calculated that the values of I were 1.0, 0.1, and 0.2 for mq, μG, and YG, respectively. According to Lenhart et al. (2002), the sensitivities of mT to mq, μG, and YG were classified as very high, medium, and high.

We also conducted a sensitivity analysis using the multi-parameter sensitivity analysis (MPSA) method. Different from the above single-point (i.e., medians) analysis, MPSA assesses the parameter sensitivity in the entire parameter space based on the Monte Carlo simulations (Wang et al., 2009). The procedure of MPSA is summarized as follows (Wang & Chen, 2012): (1) Select the parameters and determine their value ranges/distributions. (2) Randomly generate a series of parameter values from certain probability distributions within their ranges. (3) Run the model using these parameter sets and compute the objective function values (OBFs). The OBFs are defined as the sum of squared errors between observed and simulated values. In particular, observed values achieve the OBF using the median of the characteristic range for each parameter. (4) Identify which parameter sets are acceptable or unacceptable by comparing the OBFs to a given criterion, for example, the 50th percentile of the sorted OBFs. A parameter set with its OBF less than the criterion is classified as an acceptable one, otherwise it is classified as unacceptable. (5) Evaluate the sensitivity of each parameter by comparing the degree of difference between two cumulative distribution curves for acceptable and unacceptable parameter values. A greater discrepancy between the two curves means higher parameter sensitivity. From the results of MPSA shown in Fig. 2, the sensitivity of mq was much higher than that of μG and YG.

Based on the sensitivity analysis from the two methods, we can conclude that the physiological maintenance factor (mq) is the most sensitive parameter. In contrast, van Bodegom (2007) found that the overall maintenance coefficient was insensitive to the physiological maintenance, however, this results from an incorrect analysis of mT.

Implications for microbial ecology modeling

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

In the microbial-enzyme modeling of SOC decomposition, dissolved organic carbon (DOC) serves as a substrate for microbial biomass (MBC) (Chapman & Gray, 1986; Conant et al., 2011). Regarding the DOC-MBC system, we need to take into account specific growth respiration rate (Rg), specific maintenance respiration rate (Rm), specific enzyme synthesis/production rate (PE), and specific microbial mortality rate (rM) (Ryan, 1990; Blagodatsky et al., 2000; Jin & Bethke, 2003; Blagodatskaya et al., 2011; Franklin et al., 2011). Our critical reassessment of microbial maintenance provides a clear diagram (Fig. 3) for quantifying these components:

  • display math(25)
  • display math(26)
  • display math(27)
image

Figure 3. A diagram for the DOC-MBC system. DOC: dissolved organic carbon; MBC: microbial biomass carbon. Components include: specific growth respiration rate (Rg), specific maintenance respiration rate (Rm), specific enzyme synthesis rate (PE), and specific microbial mortality rate (rM); μmax,C (h−1) is the maximum specific growth rate in the Compromise model; and h(s) = s/(Ks s); see Figs 1 and 2 for other symbols.

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Equations (25)-(27) were derived from a re-analysis of the Compromise model with 0 < YG < 1. The overall maintenance coefficient (mT) is resolved into two components: the first component accounts for the synthesis and turnover of macromolecular compounds majorly including enzyme synthesis and microbial mortality and the second is the maintenance respiration. It is evident that the notation for the specific maintenance respiration rate (Rm) in Eqn (26) is similar to Rg with mq instead of μmax,C in Eqn (25). Different from the existing models where the maintenance respiration rate is a constant (e.g., Schimel & Weintraub, 2003; Lawrence et al., 2009), Rm derived herein also depends on the concentration of substrate that is included in the function of h(s). The allowance of specific maintenance respiration rate varying with substrate is consistent with the experimental observations of maintenance energy depending on the specific growth rate (van Verseveld et al., 1984), since the specific growth rate is controlled by the substrate concentration for a given maximum specific growth rate.

Equations (26), (27) and Fig. 3 indicate that (1) the parameter mq represents a combined specific maintenance rate for both enzyme synthesis and microbial mortality, which are dominant microbial maintenance components (Mandelstam, 1958; van Bodegom, 2007); and (2) a combination of mq and YG, that is, (1/YG − 1)·mq, denotes the specific maintenance respiration rate under sufficient substrate conditions. The synthesis of enzymes and the mortality of microbial biomass are presumed to be independent of substrate, whereas the maintenance respiration depends upon both microbial biomass and substrate.

It is noted that the values of mq and μG used in the above sensitivity analysis come from the continuous-flow cultures (Kuhn et al., 1980). In soil conditions, these reaction rates could be one to several orders of magnitude lower than the values shown above (Anderson & Domsch, 1985b). The discrepancy of mq and μG in the soil from those in pure-culture conditions might be explained by the facts that the substrate discontinuity or occlusion within soil aggregates and the existence of inactive biomass fractions could slow the reaction rates between microorganisms and substrates (Anderson & Domsch, 1985b; Conant et al., 2011; Wang et al., 2012b).

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

Our theoretical reassessment of microbial maintenance provided a rigorous proof that a = YG·m. Comparison of the three models indicates that the Compromise model is identical to the Herbert model for computing microbial growth and substrate consumption from the mathematical perspective, but the Compromise model is capable of decomposing microbial maintenance into two components [see Eqns (8) and (11)] depending on both biomass and substrate. In contrast to the illustration of Beeftink et al. (1990), we proposed a new one (Fig. 1) to show that the maximum specific growth rate (μmax,H) in the Herbert model is higher than those (μmax,P and μmax,C) in the other two models, with the difference attributed to the specific maintenance factor (mq or a). From the Compromise model, we derived a new expression for the overall maintenance coefficient (mT) and found that mT was more sensitive to mq than to μG and YG. Finally, we proposed an approach to quantify the specific growth respiration rate (Rg), specific maintenance respiration rate (Rm), enzyme synthesis rate (PE) plus microbial mortality rate (rM) in the microbial ecology model. Although the Compromise model was derived on a mechanistic basis and is a compromise between the Herbert and the Pirt model (Beeftink et al., 1990), the inability to describe the competition between the growth and maintenance energy requirements is a limitation of this model. Further modifications of the maintenance concepts and models are needed to solve this issue.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References

Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract No. DE-AC05-00OR22725. The authors thank Dr Shujiang Kang for his helpful comments. Thanks also go to Dr Ivan Janssens and another anonymous reviewer for their valuable comments.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Models for microbial maintenance
  5. Relationship between three models
  6. Overall maintenance coefficient and sensitivity analysis
  7. Implications for microbial ecology modeling
  8. Conclusions
  9. Acknowledgements
  10. References