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

TY - JOUR

T1 - Modeling transcriptional factor cross-talk to understand parabolic kinetics, bimodal gene expression and retroactivity in biosensor design

AU - Aris, Hannah

AU - Borhani, Shayan

AU - Cahn, Devorah

AU - O'Donnell, Colleen

AU - Tan, Elizabeth

AU - Xu, Peng

N1 - Publisher Copyright: © 2019 Elsevier B.V.

PY - 2019/4/15

Y1 - 2019/4/15

N2 - 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 expression 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) effect and the carrying capacity of the system. Our simulation fits 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 find 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 developed here will also provide an educational toolbox for synthetic biologist and biochemical/biomolecular engineering students to understand biosystem design and transcriptional regulation.

AB - 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 expression 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) effect and the carrying capacity of the system. Our simulation fits 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 find 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 developed here will also provide an educational toolbox for synthetic biologist and biochemical/biomolecular engineering students to understand biosystem design and transcriptional regulation.

KW - Bimodal gene expression

KW - FapR

KW - Hysteresis

KW - Kinetic model

KW - Malonyl-CoA sensor

KW - Transcriptional factor cross-talk

UR - http://www.scopus.com/inward/record.url?scp=85061207345&partnerID=8YFLogxK

U2 - 10.1016/j.bej.2019.02.005

DO - 10.1016/j.bej.2019.02.005

M3 - 文章

AN - SCOPUS:85061207345

SN - 1369-703X

VL - 144

SP - 209

EP - 216

JO - Biochemical Engineering Journal

JF - Biochemical Engineering Journal

ER -