Upscaling of microbially driven first-order reactions in heterogeneous porous media

Jung, Heewon; Meile, Christof

doi:10.1016/j.jconhyd.2019.04.006

Cited by 23 publications

(16 citation statements)

References 71 publications

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“…They are valid when the microbial aggregates have a negligible impact on flow fields, which is a reasonable approximation for low microbial density conditions. However, it may not hold when biomass grow large and perturb flows substantially which would require models fully resolving nonlinear feedback between fluid flows and transport phenomena [30–32]. The equations become less accurate at low Pe as under low flow conditions, the estimates from Eq.…”

Section: Resultsmentioning

confidence: 99%

Mathematical investigation of microbial quorum sensing under various flow conditions

Jung¹,

Meile

2020

Preprint

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10Microorganisms efficiently coordinate phenotype expressions through a decision-making 11 process known as quorum sensing (QS). We investigated QS amongst heterogeneously 12 distributed microbial aggregates under various flow conditions using a process-driven numerical 13 model. Model simulations assess the conditions suitable for QS induction and quantify the 14 importance of advective transport of signaling molecules. In addition, advection dilutes signaling 15 molecules so that faster flow conditions require higher microbial densities, faster signal 16 production rates, or higher sensitivities to signaling molecules to induce QS. However, 17 autoinduction of signal production can substantially increase the transport distance of signaling 18 molecules in both upstream and downstream directions. We present approximate analytical 19 solutions of the advection-diffusion-reaction equation that describe the concentration profiles of 20 signaling molecules for a wide range of flow and reaction rates. These empirical relationships, 21 which predict the distribution of dissolved solutes following zero-order production kinetics along 22 pore channels, allow to quantitatively estimate the effective communication distances amongst 23 multiple microbial aggregates without further numerical simulations. 24 25 Author Summary 26Microbes can interact with their surrounding environments by producing and sensing small 27 signaling molecules. When the microbes experience a high enough concentration of the signaling 28 molecules, they express certain phenotypes which is often energetically expensive. This 29 microbial decision-making process known as quorum sensing (QS) has been understood to 30 confer evolutionary benefits. However, it is still not completely understood how transport of the 31 produced signaling molecules affects QS. Using a mathematical approach investigating QS 32 across a range of environmentally relevant flow conditions, we find that advective transport 33 promotes QS downstream yet also dilutes the concentration of signaling molecules. We quantify 34 the importance of microbial cell location with respect to both other microbes and flow direction. 35 By analyzing complex numerical simulation results, we provide analytical approximations to 36 assess the distribution of signaling molecules in pore channels across a range of flow and 37 reaction conditions. 38 39 Introduction 40Microorganisms preferentially reside on solid surfaces, which often leads to a closer 41 proximity of neighboring cells than when in a planktonic form [1]. At elevated cell densities, 42 microorganisms need to efficiently coordinate the expression of energetically expensive 43 phenotypes, such as biofilm development, exoenzyme production, and microbial dispersal. 44 Efficiency is achieved by producing and detecting relatively cheap signaling molecules which 45 regulate the phenotype expression only when a sufficient signal concentration has been reached 46 [2]. This microbial decision-making process called "quorum sensing (QS)" was orig...

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Section: Resultsmentioning

confidence: 99%

Mathematical investigation of microbial quorum sensing under various flow conditions

Jung¹,

Meile

2020

Preprint

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“…However, it may not hold when large aggregates producing extracellular polymeric substances (EPS) perturb flows substantially. In such a case, estimating signal transport requires fully resolving nonlinear feedback between cell activity and fluid flow (Thullner & Baveye, 2008;Carrel et al, 2018;Jung & Meile, 2019), including diffusion limitation (Stewart, 2003). Finally, accounting for degradation of signaling molecules (Lee et al, 2002;Yates et al, 2002) and increased spreading of signaling molecules in 3D systems than 2D, would result in shorter transport distances than this study.…”

Section: Discussionmentioning

confidence: 96%

Numerical investigation of microbial quorum sensing under various flow conditions

Jung¹,

Meile²

2020

PeerJ

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Microorganisms efficiently coordinate phenotype expressions through a decision-making process known as quorum sensing (QS). We investigated QS amongst distinct, spatially distributed microbial aggregates under various flow conditions using a process-driven numerical model. Model simulations assess the conditions suitable for QS induction and quantify the importance of advective transport of signaling molecules. In addition, advection dilutes signaling molecules so that faster flow conditions require higher microbial densities, faster signal production rates, or higher sensitivities to signaling molecules to induce QS. However, autoinduction of signal production can substantially increase the transport distance of signaling molecules in both upstream and downstream directions. We present empirical approximations to the solutions of the advection–diffusion–reaction equation that describe the concentration profiles of signaling molecules for a wide range of flow and reaction rates. These empirical relationships, which predict the distribution of dissolved solutes along pore channels, allow to quantitatively estimate the effective communication distances amongst multiple microbial aggregates without further numerical simulations.

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“…However, it may not hold when large aggregates producing extracellular polymeric substances (EPS) perturb flows substantially. In such a case, estimating signal transport requires fully resolving nonlinear feedback between cell activity and fluid flow (Thullner and Baveye 2008;Carrel et al 2018;Jung and Meile 2019), including diffusion limitation (Stewart 2003). Finally, accounting for degradation of signaling molecules (Lee et al 2002;Yates et al 2002) and increased spreading of signaling molecules in 3D systems than 2D, would result in shorter transport distances than this study.…”

Section: Discussionmentioning

confidence: 96%

Peer Review #1 of "Numerical investigation of microbial quorum sensing under various flow conditions (v0.2)"

Secchi¹

2020

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Microorganisms efficiently coordinate phenotype expressions through a decision-making process known as quorum sensing (QS). We investigated QS amongst distinct, spatially distributed microbial aggregates under various flow conditions using a process-driven numerical model. Model simulations assess the conditions suitable for QS induction and quantify the importance of advective transport of signaling molecules. In addition, advection dilutes signaling molecules so that faster flow conditions require higher microbial densities, faster signal production rates, or higher sensitivities to signaling molecules to induce QS. However, autoinduction of signal production can substantially increase the transport distance of signaling molecules in both upstream and downstream directions. We present empirical approximations to the solutions of the advection-diffusionreaction equation that describe the concentration profiles of signaling molecules for a wide range of flow and reaction rates. These empirical relationships, which predict the distribution of dissolved solutes along pore channels, allow to quantitatively estimate the effective communication distances amongst multiple microbial aggregates without further numerical simulations.

show abstract

Upscaling of microbially driven first-order reactions in heterogeneous porous media

Cited by 23 publications

References 71 publications

Mathematical investigation of microbial quorum sensing under various flow conditions

Mathematical investigation of microbial quorum sensing under various flow conditions

Numerical investigation of microbial quorum sensing under various flow conditions

Peer Review #1 of "Numerical investigation of microbial quorum sensing under various flow conditions (v0.2)"

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