# American Institute of Mathematical Sciences

August  2017, 14(4): 953-973. doi: 10.3934/mbe.2017050

## A tridiagonal patch model of bacteria inhabiting a Nanofabricated landscape

 1 Department of Mathematics, The University of Miami, Coral Gables, FL 33124, USA 2 Department of Public Health Division of Biostatistics, Miller School of Medicine, The University of Miami, Miami, FL 33136, USA

* Corresponding author: RSC

Received  July 2016 Accepted  January 2017 Published  February 2017

In this paper we employ a discrete-diffusion modeling framework to examine a system inspired by the nano-ecology experiments on the bacterium Escherichia coli reported upon in Keymer et al. (2006). In these experiments, the bacteria inhabit a linear array of 85" microhabitat patches (MHP's)", linked by comparatively thinner corridors through which bacteria may pass between adjacent MHP's. Each MHP is connected to its own source of nutrient substrate, which flows into the MHP at a rate that can be controlled in the experiment. Logistic dynamics are assumed within each MHP, and nutrient substrate flow determines the prediction of the within MHP dynamics in the absence of bacteria dispersal between patches. Patches where the substrate flow rate is sufficiently high sustain the bacteria in the absence of between patch movement and may be regarded as sources, while those with insufficient substrate flow lead to the extinction of the bacteria in the within patch environment and may be regarded as sinks. We examine the role of dispersal in determining the predictions of the model under source-sink dynamics.

Citation: Robert Stephen Cantrell, Brian Coomes, Yifan Sha. A tridiagonal patch model of bacteria inhabiting a Nanofabricated landscape. Mathematical Biosciences & Engineering, 2017, 14 (4) : 953-973. doi: 10.3934/mbe.2017050
##### References:
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##### References:
 [1] K.J. Brown and S.S. Lin, On the existence of positive solutions for an eigenvalue problem with an indefinite weight function, Journal of Mathematical Analysis and Applications, 75 (1980), 112-120. doi: 10.1016/0022-247X(80)90309-1. Google Scholar [2] R. S. Cantrell and C. Cosner, Spatial Ecology via Reaction-Diffusion Equations Wiley and Sons, Chichester, UK, 2003. doi: 10.1002/0470871296. Google Scholar [3] F. Centler, I. Fetzer and M. Thullner, Modeling population patterns of chemotactic bacteria in homogeneous porous media, Journal of Theoretical Biology, 287 (2011), 82-91. doi: 10.1016/j.jtbi.2011.07.024. Google Scholar [4] B. Fiedler and T. Gedeon, A Lyapunov function for tridiagonal competitive-cooperative systems, SIAM Journal on Mathematical Analysis, 30 (1999), 469-478. doi: 10.1137/S0036141097316147. Google Scholar [5] J.K. Hale and P. Waltman, Persistence in infinite-dimensional systems, SIAM Journal on Mathematical Analysis, 20 (1989), 388-395. doi: 10.1137/0520025. Google Scholar [6] J.E. Keymer, P. Galajda, C. Muldoon, S. Park and R.H. Austin, Bacterial metapopulations in nanofabricated landscapes, Proceedings of the National Academy of Sciences, 103 (2006), 17290-17295. doi: 10.1073/pnas.0607971103. Google Scholar [7] S. Senn and P. Hess, On positive solutions of a linear elliptic eigenvalue problem with Neumann boundary conditions, Mathematische Annalen, 258 (1982), 459-470. doi: 10.1007/BF01453979. Google Scholar [8] J. Smillie, Competitive and cooperative tridiagonal systems of differential equations, SIAM Journal on Mathematical Analysis, 15 (1984), 530-534. doi: 10.1137/0515040. Google Scholar [9] H.R. Thieme, Persistence under relaxed point-dissipativity (with applications to an endemic model), SIAM Journal on Mathematical Analysis, 24 (1993), 407-435. doi: 10.1137/0524026. Google Scholar
(Experiment 1) We illustrate the role of increasing the bacterial self-aggregation parameter $\gamma$ in Experiment 1. Here the value of $\gamma$ is: $(a)$ $0$, $(b)$ $4$, $(c)$ $10$ and $(d)$ $50$.
(Experiment 2) We display equilibrium values for the model (6) with $7$ MHP's. Values of all parameters except $\beta_{3}$ and $\beta_{5}$ are fixed as in the text. Values for $(\beta_{3},\beta_{5})$ for each experiment are: $(a)$ $(0.3, 0.14)$, $(b)$ $(0.26, 0.18)$, $(c)$ $(0.24, 0.2)$, $(d)$ $(0.2, 0.24)$, $(e)$ $(0.16, 0.28)$, $(f)$ $(0.15, 0.29)$, $(g)$ $(0.14, 0.3)$, $(h)$ $(0.14, 0.39)$.
 Patch Number 1 2 3 4 5 6 7 a 0.0000589 0.000331 0.00187 0.0134 0.00139 0.000208 0.0000305 b 0.0000584 0.000307 0.00172 0.0133 0.00148 0.000219 0.0000324 c 0.0000530 0.000297 0.00165 0.0133 0.00153 0.000226 0.0000333 d 0.0000497 0.000278 0.00154 0.0133 0.00164 0.000242 0.0000356 e 0.0000470 0.000263 0.00144 0.0133 0.00178 0.000259 0.0000382 f 0.0000463 0.000259 0.00142 0.0134 0.00181 0.000264 0.0000389 g 0.0000457 0.000256 0.00140 0.0134 0.00185 0.000269 0.0000397 h 0.0000472 0.000264 0.00145 0.0145 0.00229 0.000326 0.0000478
 Patch Number 1 2 3 4 5 6 7 a 0.0000589 0.000331 0.00187 0.0134 0.00139 0.000208 0.0000305 b 0.0000584 0.000307 0.00172 0.0133 0.00148 0.000219 0.0000324 c 0.0000530 0.000297 0.00165 0.0133 0.00153 0.000226 0.0000333 d 0.0000497 0.000278 0.00154 0.0133 0.00164 0.000242 0.0000356 e 0.0000470 0.000263 0.00144 0.0133 0.00178 0.000259 0.0000382 f 0.0000463 0.000259 0.00142 0.0134 0.00181 0.000264 0.0000389 g 0.0000457 0.000256 0.00140 0.0134 0.00185 0.000269 0.0000397 h 0.0000472 0.000264 0.00145 0.0145 0.00229 0.000326 0.0000478
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