Back

Analysis of Biological Noise in an Organelle Size Control System

Bauer, D.; Ishikawa, H.; Wemmer, K.; Marshall, W. F.

2020-11-08 biophysics
10.1101/2020.08.31.276428 bioRxiv
Show abstract

Analysis of fluctuation in organelle size provides a new way to probe the mechanisms of organelle size control systems. By analyzing cell-to-cell variation and within-cell fluctuations of flagellar length in Chlamydomonas, we show that the flagellar length control system exhibits both types of variation. Cell to cell variation is dominated by cell size, while within-cell variation results from dynamic fluctuations that are subject to a constraint, providing evidence for a homeostatic size control system. We analyzed a series of candidate genes affecting flagella and found that flagellar length variation is increased in mutations which increase the average flagellar length, an effect that we show is consistent with a theoretical model for flagellar length regulation based on length-dependent intraflagellar transport balanced by length-independent disassembly. Comparing the magnitude and time-scale of length fluctuations with simple models suggests that tubulin assembly is not directly coupled with IFT-mediated arrival and that observed fluctuations involve tubulin assembly and disassembly events involving large numbers of tubulin dimers. Cells with greater differences in their flagellar lengths show impaired swimming but improved gliding motility, raising the possibility that cells have evolved mechanisms to tune intrinsic noise in length. Taken together our results show that biological noise exists at the level of subcellular structures, with a corresponding effect on cell function, and can provide new insights into the mechanisms of organelle size control.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.