karin2017_Fig1G

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Model Manuscripts

Abstract
Tissues use feedback circuits in which cells send signals to each other to control their growth and survival. We show that such feedback circuits are inherently unstable to mutants that misread the signal level: Mutants have a growth advantage to take over the tissue, and cannot be eliminated by known cell-intrinsic mechanisms. To resolve this, we propose that tissues have biphasic responses in and the signal is toxic at both high and low levels, such as glucotoxicity of beta cells, excitotoxicity in neurons, and toxicity of growth factors to T cells. This gives most of these mutants a frequency-dependent selective disadvantage, which leads to their elimination. However, the biphasic mechanisms create a new unstable fixed point in the feedback circuit beyond which runaway processes can occur, leading to risk of diseases such as diabetes and neurodegenerative disease. Hence, glucotoxicity, which is a dangerous cause of diabetes, may have a protective anti-mutant effect. Biphasic responses in tissues may provide an evolutionary stable strategy that avoids invasion by commonly occurring mutants, but at the same time cause vulnerability to disease.
Id Name JWS model
model1_karin3 karin3 karin3
model10_karin3 karin3 karin3
model2_karin3 karin3 karin3
model3_karin3 karin3 karin3
model11_karin3 karin3 karin3
model18_karin3 karin3 karin3
model25_karin3 karin3 karin3
model4_karin3 karin3 karin3
model12_karin3 karin3 karin3
model19_karin3 karin3 karin3
model26_karin3 karin3 karin3
model5_karin3 karin3 karin3
model13_karin3 karin3 karin3
model20_karin3 karin3 karin3
model27_karin3 karin3 karin3
model6_karin3 karin3 karin3
model14_karin3 karin3 karin3
model21_karin3 karin3 karin3
model28_karin3 karin3 karin3
model7_karin3 karin3 karin3
model15_karin3 karin3 karin3
model22_karin3 karin3 karin3
model29_karin3 karin3 karin3
model8_karin3 karin3 karin3
model16_karin3 karin3 karin3
model23_karin3 karin3 karin3
model0_karin3 karin3 karin3
model9_karin3 karin3 karin3
model17_karin3 karin3 karin3
model24_karin3 karin3 karin3
Id Name Source Number of Data Sources
Id Name Model Simulation Simulation Simulation
task1_model1_karin3 karin3 0.0 20.0 1000
task7_model7_karin3 karin3 0.0 20.0 1000
task2_model2_karin3 karin3 0.0 20.0 1000
task8_model8_karin3 karin3 0.0 20.0 1000
task13_model13_karin3 karin3 0.0 20.0 1000
task18_model18_karin3 karin3 0.0 20.0 1000
task23_model23_karin3 karin3 0.0 20.0 1000
task28_model28_karin3 karin3 0.0 20.0 1000
task3_model3_karin3 karin3 0.0 20.0 1000
task9_model9_karin3 karin3 0.0 20.0 1000
task14_model14_karin3 karin3 0.0 20.0 1000
task19_model19_karin3 karin3 0.0 20.0 1000
task24_model24_karin3 karin3 0.0 20.0 1000
task29_model29_karin3 karin3 0.0 20.0 1000
task4_model4_karin3 karin3 0.0 20.0 1000
task10_model10_karin3 karin3 0.0 20.0 1000
task15_model15_karin3 karin3 0.0 20.0 1000
task20_model20_karin3 karin3 0.0 20.0 1000
task25_model25_karin3 karin3 0.0 20.0 1000
task5_model5_karin3 karin3 0.0 20.0 1000
task11_model11_karin3 karin3 0.0 20.0 1000
task16_model16_karin3 karin3 0.0 20.0 1000
task21_model21_karin3 karin3 0.0 20.0 1000
task26_model26_karin3 karin3 0.0 20.0 1000
task0_model0_karin3 karin3 0.0 20.0 1000
task6_model6_karin3 karin3 0.0 20.0 1000
task12_model12_karin3 karin3 0.0 20.0 1000
task17_model17_karin3 karin3 0.0 20.0 1000
task22_model22_karin3 karin3 0.0 20.0 1000
task27_model27_karin3 karin3 0.0 20.0 1000

2D Plots

Id Name Number of Curves
Figure1GvaryZ Figure 1G Different z[0] 11
Figure1GvaryY Figure 1G Different y[0] 20

CSV Reports

Id Name Number of Columns