Modeling bisubstrate removal by biofilms

A bisubstrate secondary utilization model is based on the concept that an individual substrate can be utilized not only by the biomass by its utilization but also by the biomass made from the utilization of the other substrate. When substrate concentrations are low, a key factor is having sufficient substrate to initiate biofilm growth. Modeling results for three characteristic cases demonstrate that satisfying a total S(min) concentration for a bisubstrate system is the necessary condition for initiating biofilm growth and simultaneous utilization of both substrates. Because having more than one substrate supporting biofilm growth enhances the removal of each compound, the utilization rate of a specific compound can be increased by the concentration of other compounds, and the total S(min) concentration can be less than the weighted average of individual S(min) values.     

Improved pseudoanalytical solution for steady-state biofilm kinetics

Simple algebraic expressions for the flux of substrate into a steady-state biofilm are developed. This pseudoanalytical solution, which eliminates the need for repetitiously solving numerically a set of nonlinear differential equations, is based on an analysis of the numerical results from the numerical solution of the differential equations. The critical advantage of this new pseudoanalytical solution is that it is highly accurate for the entire range of substrate concentrations and kinetic parameters. The article also illustrates that previous pseudoanalytical solutions for steady-state biofilm kinetics are seriously inaccurate for certain ranges of substrate concentration and kinetic parameters.     

Phylogenetic probes for analyzing abundance and spatial organization of nitrifying bacteria.

A hierarchical set of five 16S rRNA-targeted oligonucleotide DNA probes for phylogenetically defined groups of autotrophic ammonia- and nitrite-oxidizing bacteria was developed for environmental and determinative studies. Hybridization conditions were established for each probe by using temperature dissociation profiles of target and closely related nontarget organisms to document specificity. Environmental application was demonstrated by quantitative slot blot hybridization and whole-cell hybridization of nitrifying activated sludge and biofilm samples. Results obtained with both techniques suggested the occurrence of novel populations of ammonia oxidizers. In situ hybridization experiments revealed that Nitrobacter and Nitrosomonas species occurred in clusters and frequently were in contact with each other within sludge flocs.     

Competition and Coexistence of Sulfate-Reducing and Methanogenic Populations in Anaerobic Biofilms

The microbial population structure and function of natural anaerobic communities maintained in laboratory fixed-bed biofilm reactors were tracked before and after a major perturbation, which involved the addition of sulfate to the influent of a reactor that had previously been fed only glucose (methanogenic), while sulfate was withheld from a reactor that had been fed both glucose and sulfate (sulfidogenic). The population structure, determined by using phylogenetically based oligonucleotide probes for methanogens and sulfate-reducing bacteria, was linked to the functional performance of the biofilm reactors. Before the perturbation, the methanogenic reactor contained up to 25% methanogens as well as 15% sulfate-reducing bacteria, even though sulfate was not present in the influent of this reactor. Methanobacteriales and Desulfovibrio spp. were the most abundant methanogens and sulfate-reducing bacteria, respectively. The presence of sulfate-reducing bacteria (primarily Desulfovibrio spp. and Desulfobacterium spp.) in the absence of sulfate may be explained by their ability to function as proton-reducing acetogens and/or fermenters. Sulfate reduction began immediately following the addition of sulfate consistent with the presence of significant levels of sulfate-reducing bacteria in the methanogenic reactor, and levels of sulfate-reducing bacteria increased to a new steady-state level of 30 to 40%; coincidentally, effluent acetate concentrations decreased. Notably, some sulfate-reducing bacteria (Desulfococcus/Desulfosarcina/Desulfobotulus group) were more competitive without sulfate. Methane production decreased immediately following the addition of sulfate; this was later followed by a decrease in the relative concentration of methanogens, which reached a new steady-state level of approximately 8%. The changeover to sulfate-free medium in the sulfidogenic reactor did not cause a rapid shift to methanogenesis. Methane production and a substantial increase in the levels of methanogens were observed only after approximately 50 days following the perturbation.     

A structured model of dual-limitation kinetics

A structured model of substrate-utilization kinetics that encompasses dual-limitation conditions, caused by simultaneously low concentrations of the electron donor and the electron acceptor, is developed by incorporating the internal cofactor responses into the kinetic variables. The structured model is based on an assumption that the maximum specific electron-donor-oxidation rate (q(md)) is not a constant, but is linearly controlled by the intracellular chemical potentials, log(NAD/NADH) and log(ATP/ADP . P(i)). Determination of the kinetic parameters for the dual-limitation model, using experimental data from the companion article, verifies that q(md) varies and demonstrates that the NAD/NADH ratio affects q(md) in a positive direction; thus, an increase of the ratio increases the rate of electron-donor utilization. Because the internal NAD/NADH ratio rises with an increase in S(ar) the specific electron-donor-utilization rate is accelerated by high S(a). Since the ratio also increases as the specific electron-donor-utilization rate falls, the specific rate is intrinsically accelerated by the cofactor response when it becomes low due to a depletion of electron donor. Because the cofactor responses upon changes of the external substrate concentrations are systematic, the dual-limitation model can be expressed as a function of only external concentrations of electron donor and electron acceptor, which results in a multiplicative (double-Monod) form. Thus, dual limitation by both substrates reduces the overall reaction rate below the rate expected from single limitation by only one, the most severely limiting, substrate. (c) 1996 John Wiley & Sons, Inc.     

Evaluation of a model for the effects of substrate interactions on the kinetics of reductive dehalogenation

The effects of primary electron-donor and electron-acceptor substrates on the kinetics of TCA biodegradation in sulfate-reducing and methanogenic biofilm reactors are presented. Of the common anaerobic electron-donor substrates that were tested, only formate stimulated the TCA biodegradation rate in both reactors. In the sulfate-reducing reactor, glucose also stimulated the reaction rate. The effects of formate and sulfate on TCA biodegradation kinetics were analyzed using a model for primary substrate effects on reductive dehalogenation. Although some differences between the model and the data are evident, the observed responses of the TCA degradation rate to formate and sulfate were consistent with the model. Formate stimulated the TCA degradation rate in both reactors over the entire range of TCA concentrations that were studied (from 50 g TCA/L to 100 mg TCA/L). The largest effects occurred at high TCA concentrations, where the dehalogenation kinetics were zero order. Sulfate inhibited the first-order TCA degradation rate in the sulfate-reducing reactor, but not in the methanogenic reactor. Molybdate, which is a selective inhibitor of sulfate reduction, stimulated the TCA removal rate in the sulfate-reducing reactor, but had no effect in the methanogenic reactor.     

Quantification of the effect of substrate concentration on the conjugal transfer rate of the TOL plasmid in short-term batch mating experiments

Batch mating experiments with Pseudomonas putida PAW 1 (TOL) as a donor and Pseudomonas aeruginosa PAO 1162 as a recipient strain were performed to quantify the effect of the substrate concentration in the mating medium on the observed plasmid transfer rate coefficient. The impact of the substrate concentration in the mating medium was highly correlated with the growth history of the donor strain. When the donor strain was harvested in exponential growth phase, no impact was observed; when the donor strain was taken from the stationary phase, however, a strong impact of the substrate concentration was measured: a 10-fold reduction in the substrate concentration decreased the observed plasmid transfer rate by 55%.     

A model for the effects of primary substrates on the kinetics of reductive dehalogenation

A kinetic model that describes substrate interactions during reductive dehalogenation reactions is developed. This model describes how the concentrations of primary electron-donor and -acceptor substrates affect the rates of reductive dehalogenation reactions. A basic model, which considers only exogenous electron-donor and -acceptor substrates, illustrates the fundamental interactions that affect reductive dehalogenation reaction kinetics. Because this basic model cannot accurately describe important phenomena, such as reductive dehalogenation that occurs in the absence of exogenous electron donors, it is expanded to include an endogenous electron donor and additional electron acceptor reactions. This general model more accurately reflects the behavior that has been observed for reductive dehalogenation reactions. Under most conditions, primary electron-donor substrates stimulate the reductive dehalogenation rate, while primary electron acceptors reduce the reaction rate. The effects of primary substrates are incorporated into the kinetic parameters for a Monod-like rate expression. The apparent maximum rate of reductive dehalogenation (q _{m, ap} ) and the apparent half-saturation concentration (K _ap ) increase as the electron donor concentration increases. The electron-acceptor concentration does not affect q _{m, ap} , but K _ap is directly proportional to its concentration.Definitions for model parameters RX halogenated aliphatic substrate - E-M ⁿ reduced dehalogenase - E-M ⁿ⁺² oxidized dehalogenase - [E-M ⁿ ] steady-state concentration of the reduced dehalogenase (moles of reduced dehalogenase per unit volume) - [E-M ⁿ⁺²] steady-state concentration of the oxidized dehalogenase (moles of reduced dehalogenase per unit volume) - DH₂ primary exogenous electron-donor substrate - A primary exogenous electron-acceptor substrate - A2 second primary exogenous electron-acceptor substrate - X biomass concentration (biomass per unit volume) - f fraction of biomass that is comprised of the dehalogenase (moles of dehalogenase per unit biomass) - stoichiometric coefficient for the reductive dehalogenation reaction (moles of dehalogenase oxidized per mole of halogenated substrate reduced) - stoichiometric coefficient for oxidation of the primary electron donor (moles of dehalogenase reduced per mole of donor oxidized) - stoichiometric coefficient for oxidation of the endogenous electron donor (moles of dehalogenase reduced per unit biomass oxidized) - stoichiometric coefficient for reduction of the primary electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - stoichiometric coefficient for reduction of the second electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - r _RX rate of the reductive dehalogenation reaction (moles of halogenated substrate reduced per unit volume per unit time) - r _d1 rate of oxidation of the primary exogenous electron donor (moles of donor oxidized per unit volume per unit time) - r _d2 rate of oxidation of the endogenous electron donor (biomass oxidized per unit volume per unit time) - r _a1 rate of reduction of the primary exogenous electron acceptor (moles of acceptor reduced per unit volume per unit time) - r _a2 rate of reduction of the second primary electron acceptor (moles of acceptor reduced per unit volume per unit time) - k _RX mixed second-order rate coefficient for the reductive dehalogenation reaction (volume per mole dehalogenase per unit time) - k _d1 mixed-second-order rate coefficient for oxidation of the primary electron donor (volume per mole dehalogenase per unit time) - k _d2 mixed-second-order rate coefficient for oxidation of the endogenous electron donor (volume per mole dehalogenase per unit time) - b first-order biomass decay coefficient (biomass oxidized per unit biomass per unit time) - k _a1 mixed-second-order rate coefficient for reduction of the primary electron acceptor (volume per mole dehalogenase per unit time) - k _a2 mixed-second-order rate coefficient for reduction of the second primary electron acceptor (volume per mole dehalogenase per unit time) - q _m,ap apparent maximum specific rate of reductive dehalogenation (moles of RX per unit biomass per unit time) - K _ap apparent half-saturation concentration for the halogenated aliphatic substrate (moles of RX per unit volume) - k _ap apparent pseudo-first-order rate coefficient for reductive dehalogenation (volume per unit biomass per unit time)     

Dynamic process conditions in bioprocess development

In this review, we summarise recent studies that purposefully employed dynamic conditions, such as shifts, pulses, ramps and oscillations, for fast physiological strain characterisation and bioprocess development. We show the broad applicability of dynamic conditions and the various objectives that can thereby be investigated in a short time. Dynamic processes reveal information about the analysed system faster than traditional strategies, like continuous cultivations, as process parameters can directly be linked to platform and product parameters. Furthermore, we demonstrate that dynamic operations can result in increased productivity and high product quality, making this strategy a valuable tool for bioprocess development. With this review, we would like to encourage bioprocess engineers to an increased use of dynamic conditions in bioprocess development.