確率的個体細胞サイズ恒常性の精密運動学を決定する世代間スケーリング則
Kunaal Joshi1, Charles S Wright1, Rudro R Biswas1
1Purdue University, West Lafayette, Department of Physics and Astronomy, Indiana 47907, USA.
Abstract:
Individual bacterial cells grow and divide stochastically. Yet they maintain their characteristic sizes across generations within a tightly controlled range. What rules ensure intergenerational stochastic homeostasis of individual cell sizes? Valuable clues have emerged from high-precision long-term tracking of individual statistically identical Caulobacter crescentus cells as reported by Joshi et al. [bioRxiv (2023)]10.1101/2023.01.18.524627 and Ziegler et al. [Mol. Biol. Cell 35, ar78 (2024)1059-152410.1091/mbc.E23-11-0452]: Intergenerational cell-size homeostasis is an inherently stochastic phenomenon, follows Markovian or memory-free dynamics, and cells obey an intergenerational scaling law, which governs the stochastic map characterizing generational sequences of cell sizes. These observed emergent simplicities serve as essential building blocks of the data-informed principled theoretical framework we develop here. Our exact analytic fitting-parameter-free results for the predicted intergenerational stochastic map governing the precision kinematics of cell-size homeostasis are remarkably well borne out by experimental data, including extant published data on other microorganisms, Escherichia coli and Bacillus subtilis. Furthermore, our framework naturally yields the general exact and analytic condition that is necessary and sufficient to ensure that stochastic homeostasis can be achieved and maintained. Significantly, this condition is more stringent than the known heuristic result from quasideterministic frameworks. In turn, the fully stochastic treatment we present here extends and updates extant frameworks and highlights the inherently stochastic behaviors of individual cells in homeostasis.
さらに関連する動画
関連する概念動画
Scaling
Cells Coordinate Growth and Proliferation
Mechanistic Models: Compartment Models in Individual and Population Analysis
Exponential Equations for Modeling Growth
Typical Model Studies
Life Histories


