Size regulation of multiple organelles competing for a shared subunit pool

Abstract

How cells regulate the size of intracellular structures and organelles, despite continuous turnover in their component parts, is a longstanding question. Recent experiments suggest that size control of many intracellular assemblies is achieved through the depletion of a limiting subunit pool in the cytoplasm. While the limiting pool model ensures organelle size scaling with cell size, it does not provide a mechanism for robust size control of multiple co-existing structures. Here we propose a kinetic theory for size regulation of multiple structures that are assembled from a shared pool of subunits. We demonstrate that a negative feedback between the growth rate and the size of individual structures underlies size regulation of a wide variety of intracellular assemblies, from cytoskeletal filaments to three-dimensional organelles such as centrosomes and the nucleus. We identify the feedback motifs for size control in these structures, based on known molecular interactions, and quantitatively compare our theory with available experimental data. Furthermore, we show that a positive feedback between structure size and growth rate can lead to bistable size distributions arising from autocatalytic growth. In the limit of high subunit concentration, autocatalytic growth of multiple structures leads to stochastic selection of a single structure, elucidating a mechanism for polarity establishment.

Analysis of Biological Noise in an Organelle Size Control System

“…As with any chemical reaction, assembly and disassembly of tubulin subunits on the axonemal doublets is an inherently stochastic process and thus constitutes one unavoidable source of noise. Simulations of chemical master equations have been used to predict fluctuations in flagellar assembly and length at steady state based on the inherent stochasticity of tubulin monomer addition and removal (Banerjee 2020; Patra 2020). However, additional sources of variation may include stochastic fluctuations in cargo loading onto the IFT system (Craft 2015), transport through the flagellar pore (Dishinger 2010; Ludington 2014; Harris 2020), and fluctuation of IFT traffic due to traffic jams, motor pausing, and reversals (Bressloff 2006; Pinkoviezky 2014; Mijalkovic 2017; Tang 2019).…”

“…A more comprehensive approach would address the full nonlinear model. Several nonlinear stochastic models have recently been proposed for flagellar length dynamics (Fai 2019; Banerjee 2020; Patra 2020). Two of these studies analyzed models for flagellar length control using master equations to represent the inherently stochastic nature of biochemical reactions, which has the advantage of not requiring an extra “noise source” to be postulated.…”

“…Two of these studies analyzed models for flagellar length control using master equations to represent the inherently stochastic nature of biochemical reactions, which has the advantage of not requiring an extra “noise source” to be postulated. One study (Banerjee 2020) analyzed a model closely related to our balance point model, which assumes the 1/L dependence of IFT on length, while a second study (Patra 2020) analyzed a more elaborate model in which IFT injection is regulated by a time-of-flight mechanism. A third recent study describes a model for length regulation based on length-dependent transport of disassembly factors (Fai 2019), which is distinct from our current model.…”

“…However, previous experimental studies of flagellar length variation have not distinguished between sources of variation that take place within the flagella (intrinsic noise) versus source of variation that take place in the cell body (extrinsic noise). Theoretical models for flagellar length regulation make distinct predictions concerning the nature of flagellar length fluctuation and variation (Bressloff 2006; Hendel 2018; Fai 2019; Banerjee 2020; Patra 2020), hence a measurement of length fluctuation has the potential to test existing models and inform future modeling efforts.…”

Size-Regulated Symmetry Breaking in Reaction-Diffusion Models of Developmental Transitions

“…These activator– substrate models exhibit two distinct dynamic regimes [ 44 ]: the wave-pinning regime, characterised by wide mesa-like patterns and saturated subunit association kinetics and the Turing regime that yields narrow concentration peaks by virtue of competition between structures. The Turing regime operates below saturation ( ), where a winner-take-all competition between structures asymptotically results in a single concentration peak [ 31 , 45 , 46 ]. By contrast, in the regime above saturation ( ), the structures can co-exist for very long timescales, with the timescale of coarsening determined by parameters such as the diffusion coefficients or the reaction fluxes [ 47 ].…”