TY - JOUR
T1 - A Predictive Reaction-Diffusion Based Model of E. ColiColony Growth Control
AU - He, Changhan
AU - Bayakhmetov, Samat
AU - Harris, Duane
AU - Kuang, Yang
AU - Wang, Xiao
N1 - Funding Information: Manuscript received September 12, 2020; revised November 12, 2020; accepted December 7, 2020. Date of publication December 23, 2020; date of current version March 15, 2021. This work was supported by National Institutes of Health (NIH) under Grant 5R01GM131405. Recommended by Senior Editor M. Arcak. (Changhan He and Samat Bayakhmetov contributed equally to this work.) (Corresponding authors: Yang Kuang; Xiao Wang.) Changhan He, Duane Harris, and Yang Kuang are with the School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287 USA (e-mail [email protected]). Publisher Copyright: © 2017 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - Bacterial colony formations exhibit diverse morphologies and dynamics. A mechanistic understanding of this process has broad implications to ecology and medicine. However, many control factors and their impacts on colony formation remain underexplored. Here we propose a reaction-diffusion based dynamic model to quantitatively describe cell division and colony expansion, where control factors of colony spreading take the form of nonlinear density-dependent function and the intercellular impacts take the form of density-dependent hill function. We validate the model using experimental E. coli colony growth data and our results show that the model is capable of predicting the whole colony expansion process in both time and space under different conditions. Furthermore, the nonlinear control factors can predict colony morphology at both center and edge of the colony.
AB - Bacterial colony formations exhibit diverse morphologies and dynamics. A mechanistic understanding of this process has broad implications to ecology and medicine. However, many control factors and their impacts on colony formation remain underexplored. Here we propose a reaction-diffusion based dynamic model to quantitatively describe cell division and colony expansion, where control factors of colony spreading take the form of nonlinear density-dependent function and the intercellular impacts take the form of density-dependent hill function. We validate the model using experimental E. coli colony growth data and our results show that the model is capable of predicting the whole colony expansion process in both time and space under different conditions. Furthermore, the nonlinear control factors can predict colony morphology at both center and edge of the colony.
KW - Synthetic biology
KW - bacterial colony expansion
KW - diffusion
KW - partial differential equations
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U2 - 10.1109/LCSYS.2020.3046612
DO - 10.1109/LCSYS.2020.3046612
M3 - Article
SN - 2475-1456
VL - 5
SP - 1952
EP - 1957
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
IS - 6
M1 - 9305287
ER -