Noise filtering tradeoffs in spatial gradient sensing and cell polarization response
- Equal contributors
1 Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA
2 Center for Mathematical and Computational Biology Center for Complex Biological Systems Department of Mathematics University of California, Irvine Irvine, CA 92697, USA
3 Center for Complex Biological Systems Department of Developmental and Cell Biology University of California, Irvine Irvine, CA 92697, USA
4 Tau-Mu Yi Assistant Professor of Developmental and Cell Biology 2011 Biological Sciences III University of California, Irvine Irvine, CA 92697, USA
BMC Systems Biology 2011, 5:196 doi:10.1186/1752-0509-5-196Published: 13 December 2011
Additional file 1:
Supplemental Material. This file contains Table S1 (Yeast strains), a description of the mathematical models, Figure S1 (Parametric analysis of k0 and k1), Table S2 (Effect of gradient slope versus noise on polarization), Table S3 (Effects of noise on polarization quality), a section estimating external gradient noise, Table S4 (Effects of ligand diffusion noise (σL) and receptor-ligand binding noise (σRL) on projection directional accuracy), a comparison of chemotactic index to cos(θ) measure of directional accuracy, Table S5 (Effect of diffusion of the polarized species on polarization), Figure S2 (Diffusion decreases noise in polarization output and the extent of polarization), and Figure S3 (Image of microfluidics gradient labeled with tracer dye).
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Video S1. Time-lapse video of STE20-GFP bni1Δ cells. STE20-GFP bni1Δ cells were treated with 20 μM α-factor and imaged over a 10 min period at 1 min intervals. The polarization of Ste20-GFP was not maintained in a single location as observed in wild-type cells, but instead shifted position. There were also multiple peaks of polarization.
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