TY - JOUR
T1 - Extrinsic noise driven phenotype switching in a self-regulating gene
AU - Assaf, Michael
AU - Roberts, Elijah
AU - Luthey-Schulten, Zaida
AU - Goldenfeld, Nigel
PY - 2013/7/30
Y1 - 2013/7/30
N2 - Analysis of complex gene regulation networks gives rise to a landscape of metastable phenotypic states for cells. Heterogeneity within a population arises due to infrequent noise-driven transitions of individual cells between nearby metastable states. While most previous works have focused on the role of intrinsic fluctuations in driving such transitions, in this Letter we investigate the role of extrinsic fluctuations. First, we develop an analytical framework to study the combined effect of intrinsic and extrinsic noise on a toy model of a Boolean regulated genetic switch. We then extend these ideas to a more biologically relevant model with a Hill-like regulatory function. Employing our theory and Monte Carlo simulations, we show that extrinsic noise can significantly alter the lifetimes of the phenotypic states and may fundamentally change the escape mechanism. Finally, our theory can be readily generalized to more complex decision making networks in biology.
AB - Analysis of complex gene regulation networks gives rise to a landscape of metastable phenotypic states for cells. Heterogeneity within a population arises due to infrequent noise-driven transitions of individual cells between nearby metastable states. While most previous works have focused on the role of intrinsic fluctuations in driving such transitions, in this Letter we investigate the role of extrinsic fluctuations. First, we develop an analytical framework to study the combined effect of intrinsic and extrinsic noise on a toy model of a Boolean regulated genetic switch. We then extend these ideas to a more biologically relevant model with a Hill-like regulatory function. Employing our theory and Monte Carlo simulations, we show that extrinsic noise can significantly alter the lifetimes of the phenotypic states and may fundamentally change the escape mechanism. Finally, our theory can be readily generalized to more complex decision making networks in biology.
UR - http://www.scopus.com/inward/record.url?scp=84881491748&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.111.058102
DO - 10.1103/PhysRevLett.111.058102
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C2 - 23952448
AN - SCOPUS:84881491748
SN - 0031-9007
VL - 111
JO - Physical Review Letters
JF - Physical Review Letters
IS - 5
M1 - 058102
ER -