Modeling transcriptional factor cross-talk to understand parabolic kinetics, bimodal gene expression and retroactivity in biosensor design

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https://www.sciencedirect.com/science/article/pii/S1369703X19300452

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https://doi.org/10.1016/j.bej.2019.02.005
 http://hdl.handle.net/11603/12856

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UMBC Chemical, Biochemical & Environmental Engineering Department
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2019-02-05

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journal articles postprints
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Hannah Aris , Shayan Borhani , Devorah Cahn , Colleen O'Donnell , Elizabeth Tan , Peng Xu, Modeling transcriptional factor cross-talk to understand parabolic kinetics, bimodal gene expression and retroactivity in biosensor design, Biochemical Engineering Journal Volume 144, 15 April 2019, Pages 209-216, https://doi.org/10.1016/j.bej.2019.02.005

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Subjects

Malonyl-CoA sensor
transcriptional factor cross-talk
FapR
kinetic model
hysteresis
bimodal gene expression

Abstract

Transcriptional factor-based biosensor has been widely used to reprogram and adjust cellular activity to the changing environment. These biosensors translate an internal cellular signal to a transcriptional reporter output. Previous examples have demonstrated transcriptional factor (TF) cross-talk could lead to complex gene ex- pression dynamics. In this report, we formulated a mechanism-based kinetic model to simulate and predict how TF cross-talk reshape the transcriptional dynamics of an engineered biosensor. Our model comprises TF cross- talk and Hill-type equation to quantify the degree of gene repression and de-repression; it also accounts for protein degradation, cell growth (dilution) e ff ect and the carrying capacity of the system. Our simulation fi ts well with the biphasic parabolic gene expression pattern of an experimentally validated malonyl-CoA sensor. We discovered that exponential growth could lead to hysteresis in the TF cross-talk model. With Logistic growth to limit the carrying capacity, we fi nd that bimodal gene expression pattern could arise, and this phenomenon is rooted in the hysteresis characteristics of the TF cross-talk model. The computational insights obtained in this study will guide us to design accurate, sensitive TF-based biosensors and may serve as a diagnostic platform to troubleshoot the complex transcriptional dynamics in biosensor design. The computational framework devel- oped here will also provide an educational toolbox for synthetic biologist and biochemical/biomolecular en- gineering students to understand biosystem design and transcriptional regulation