Species Coexistence in Nitrifying Chemostats: A Model of Microbial Interactions

Abstract

Abstract:In a previous study, the two nitrifying functions (ammonia oxidizing bacteria (AOB) or nitrite-oxidizing bacteria (NOB)) of a nitrification reactor-operated continuously over 525 days with varying inputs-were assigned using a mathematical modeling approach together with the monitoring of bacterial phylotypes. Based on these theoretical identifications, we develop here a chemostat model that does not explicitly include only the resources' dynamics (different forms of soluble nitrogen) but also explicitly takes into account microbial inter-and intra-species interactions for the four dominant phylotypes detected in the chemostat. A comparison of the models obtained with and without interactions has shown that such interactions permit the coexistence of two competing ammonium-oxidizing bacteria and two competing nitrite-oxidizing bacteria in competition for ammonium and nitrite, respectively. These interactions are analyzed and discussed.

Microbial Interactions as Drivers of a Nitrification Process in a Chemostat

“…In the work of Dumont et al [16], the growth rates seen in Equation 6were calibrated against experimental data for the two most abundant OTU of each functional group.…”

“…In this case the equilibrium point can be calculated from the solutions of the system of Equations (15), (16), (19) and (21) with non-negative coordinates. If the system only provides solutions with at least one negative entry then the set J cannot define an equilibrium point.…”

“…As a departing point, the work of Dumont et al [16] is presented in Section 2. They modelled a chemostat experiment where nitrification takes place by considering a glV model coupled with a substrate limited growth expression (µ i is no longer a constant, but the classic Monod expression) and fitted their model using absolute abundances of the major OTU identified by molecular fingerprints obtained by single-strand conformation polymorphism (SSCP).…”

“…The regular control is composed of a feedback part on the species state variable, and a feed forward part, or tracking, on the measurement of abundances of each species; the method involves solving state-dependent Ricatti equations [17]. In Section 5, the methodology from Section 4 is applied to the data from the experiments performed by Dumont et al [18] and not just to the most abundant species as it was the case of the model analysed in Section 3 [16]. This approach explicitly assumes that dynamics of complex ecosystems are driven by interactions, that are the results of feedback loops of each species on the growth rate of others.…”

Bifurcation analysis of an impulsive system describing Partial Nitritation and Anammox in a hybrid reactor

“…An extension of the widely-used Activated Sludge Models (ASMs) to account for nitrite dynamics with the inclusion of biomass terms for the Ammonium and Nitrite Oxidising Organisms (AOB and NOB) as separate state variables [12] has motivated their use for simulation and control of PN/A systems [23,29,30,28,10]. Others have applied modelling to address specific process performance questions in floc-based [19], granular sludge [1] or biofilm systems [7,18], or to explore fundamental questions related to microbial ecology, such as coexistence in nitrifying systems [2] and competition [20,22].…”